Innovations in Investments and Corporate Finance (Advances in Financial Economics, Vol. 7)

LIST OF CONTRIBUTORS Raj Aggarwal Kent State University, USA Uri Benzion Technion, and Ben Gurion University, Israe...

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LIST OF CONTRIBUTORS

Raj Aggarwal

Kent State University, USA

Uri Benzion

Technion, and Ben Gurion University, Israel

Gurmeet S. Bhabra

University of Otago, New Zealand

Stephen P . Ferris

University of Missouri, USA

John F. Gaski

University of Notre Dame, Indiana, USA

Vijay Gondhalekar

University of Michigan, Flint, USA

Pim Jansen

Rabobank International, London, UK

William W. Jennings

U .S . Air Force Academy, USA

Jinho Jeong

Hansei University, Korea

Hoje Jo

Santa Clara University, USA

Beni Lauterbach

Bar Ilan University, Israel

Kwangwoo Park

University of Illinois, Champaign, USA

Enrico Perotti

University of Amsterdam and CEPR, The Netherlands

John M. Pinkerton

Virginia Polytechnic Institute and State University, USA

John G. Powell

University of Otago, New Zealand vii

LIST OF CONTRIBUTORS

Viii

Haim Reisman

Technion, Israel

Atulya Sarin

Santa Clara University, USA

Karen Schnatterly

University of Minnesota, USA

Paul J. Seguin

University of Minnesota, USA

Joseph Yagil

Haifa University, and Columbia University, USA

Sijing Zong

Kent State University, USA

VALUATION OF INTERNET COMPANIES : A SURVEY OF THE EVIDENCE

Pim Jansen and Enrico Perotti

ABSTRACT' In this article, we review the recent academic research on the valuation of Internet companies . In particular, we focus on the valuation method(s) which were said to be suitable for new economy start-ups in the boom years 1998-2000, and conclude that they were neither novel nor very accurate. Since the downturn in the sector, the valuation focus has returned to advanced fundamental valuation methods such as real options valuation .

INTRODUCTION The silliest claim was that in this New World of rapid technological change, old methods of share valuation had become irrelevant . Profits were for wimps, it claimed . Falls of 90-100% in the share prices of loss-making dot .com firms show that profits do matter after all. The Economist, May 12th, 2001

Innovations in Investments and Corporate Finance, Volume 7, pages 1-17 . Copyright © 2002 by Elsevier Science Ltd. All rights of reproduction in any form reserved . ISBN : 0-7623-0897-4 I

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PIM JANSEN AND ENRICO PEROTTI

At the height of the Internet craze, flotations of Internet companies have created many millionaires . It was argued at that time that new rules of valuation had to be applied in the New Economy . Revenues and growth potential, rather than cash flows, were to be the new foundation of value . After the abrupt fall of NASDAQ index in the spring of 2000, reality set in . Many Internet companies continued to burn cash and to generate significant losses, and many have since gone bankrupt . Furthermore, in the financial press, among economists and among respected academics around the world, a broad discussion has finally developed on the value creation of Internet companies . In this article, we review the recent academic research on the valuation of Internet companies . In particular, we investigate the valuation method(s) which were said to be suitable for new economy start-ups .

SPECIFIC CHARACTERS OF INTERNET FIRMS Internet firms are often strikingly different from other firms . Consider the results from a study by Hand (2000a) on a sample of 274 Internet firms, 274 non-Internet firms and 213 IPO-matched non-Internet firms . An Internet firm has over half its revenues from Internet. The median Internet firm has ten times the market capitalisation yet employs only 40% of the number of people from a median firm. Relative to the median firm, the median Internet firm also has more than three times the beta risk (2 .55 versus 0 .78), one third of its stock held by institutions (8% versus 27%), half as much of its issued shares in public float (31% versus 62%), a public float turnover that is 6 .5 times faster (once every 19 versus 143 trading days) and five times as much of its public float sold short (5% versus 1%) . This pattern holds also when Internet firms are compared with IPO-matched firms . Another striking feature is the underpricing of the median Internet firm : the Internet firm is four times as underpriced at its IPO as the median IPO-matched non-Internet firm (37% versus 9%) with the mean underpricing for Internet firms reaches 69% to be compared with an average underpricing for all U .S . IPOs over the period 1960-1996 of 16% . These differences are unusual but not unique historically . A study by Amir and Lev [AL, 1996] for the ten years beginning 1984, reported that 69% of quarterly EPS of the 14 independent cellular telephone companies they examined were n egative . AL also report that the corresponding figure for 44 biotechnology companies over the same period was 72% . This compares to Hand's result of 77% of Internet firms reporting negative EPS over the period 1997 :Ql - 1999 :Q2, suggesting that Internet firms may be no more unprofitable than other groups of firms in earlier technology-based, high growth industries which went through an IPO cycle .

Valuation of Internet Companies

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CLASSIFICATION OF INTERNET FIRMS We start by a general classification of the numerous types of Internet companies that have emerged in the last few years . The main distinction in use is between Business to Business (B2B) and Business to Consumer (B2C) Internet firms (e .g . Bowen, Davis & Rajgopal, 2000 ; Demers & Lev, Davis, 2001) . Across these categories, the most widely used in the USA (from w ww .intemet .com ) distinguishes : (1) e-tailers and e-commerce ; (2) Software ; (3) Enablers ; (4) Security ; (5) Content & portals ; (6) High speed and infrastructure ; and (7) ISP and access . Trueman Wong and Zhang (2000) especially focus on the difference between e-tailers (producing revenues by attracting visitors to their web sites and selling products) and the p/c firms (portal- and content community firms, who depend for their revenues largely on advertising) . Keating, Lys and Magee (2001) make a distinction between firms that market or sell primarily via the Internet ("direct" firms, or B2C firms) and firms that provide Internet infrastructure ("support" firms) . Perotti and Rossetto (2000) make a further distinction within the content and portal category, distinguishing between vertical portals, who specialise in the sales of one product (e .g . Amazon focusing originally on selling books via Internet) and horizontal portals, who sell all sorts of products or give access to multiple services .

FINANCIAL VALUE DRIVERS We now move to review common and less common financial measures used for valuing Internet firms and present some evidence on the value drivers of stock valuation . Is Accounting Information Useful for Internet Valuation? Hand (2000a) assesses the main accounting measures listed Internet firms operations and relates them to their stock market valuations . He finds evidence that basic accounting data is value relevant, albeit in a non-linear manner . Overall, he concludes that Internet firms' log-transformed market values are neatly linear in both log-transformed book-equity and log-transformed net income . This suggests a remarkable dispersion . Keating, Lys and Magee (2001) conclude that annual report information indeed explains a significant portion of the cross-sectional variation in valuations at both March and May 2000 . They find that, for support firms, the annual report provides 20 .5 times the explanatory power on valuation relative

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to bits of information provided by analysts' forecast and earnings surprise information (the new information) combined . 2 Furthermore, the contribution of the annual report information to explaining valuations was significantly higher than new information . Perhaps the more succesfull Internet firms were expected to spend more on customer and product development than was generated in revenue during 2000, resulting in a lower forecasted earnings for the year . The earnings expectations appear to have changed by the end of May . The annual information provides relatively 21 .3 times the information provided by analysts forecasts and earnings surprises combined, significant at the 0 .01 level . Further, they observe that the coefficient on Gross Profit is significantly positive, while the coefficients on R&D and Other Expenses are significantly negative . They conclude that firms earning greater profits in 1999 experience a relatively smaller stock price decline in spring 2000, while firms that spend more on research and development in 1999 experience a relatively larger stock price decline . Adjusted Accounting Information Accounting measures often need to be adjusted for the purpose of valuation . A primary adjustment to earnings is to subtract the cost of committed capital . This requires a precise measure of capital . Economic Value Added (EVA 3 ) considers R&D, marketing and advertising expenses as investment rather than expenses . Using EVA, a greater percentage of the value appears in the earlier years, where forecasting is easier . When initial costs (R&D, marketing and advertising) are capitalised as investments, this creates higher earnings in the earlier years, where forecasting is more practical . When a considerable part of a company's value is in future growth opportunities, a better understanding of intrinsic value may be gained by applying real options' techniques . We will discuss this issue later in the article . Price-to-Sales Ratio Demers and Lev (2000) examine the value relevance of two categories of Internet companies' expenditures related to the acquisition of intangible assets : (i) marketing expenses ; and (ii) product development and R&D expenses . They hypothesise that the market will positively value both of these variables as long as it views them as positive NPV investments . DL also examine the value relevance of the income statement components cost of goods/services sold (COGS), R&D and marketing expenses . With the exception of COGS, the income statement components are significantly value relevant in 1999 . This is


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consistent with studies on start-up industries (e .g . Amir & Lev, 1996) and R&D intensive firms (e .g . Lev & Sougiannis, 1996) . Their findings suggest that early in the Internet boom, the market viewed B2C Internet companies' material expenditures directed towards customer acquisitions and product development as investments rather than expenses . Following the major crash of NASDAQ in early 2000, the market appears to have shifted in valuation approach . In the year 2000, COGS is negatively and significantly associated with P/S-ratio but Marketing and Product Development expenses no longer explain Internet companies price-to-sales ratio . These findings suggest that in the year 2000, the market is no longer willing to implicitly capitalise expenditures on intangible assets in valuing Internet stocks . DL conclude that `the market continued to capitalise these expenditures during the first quarter of 2000 (i .e . until the bursting of the bubble), but then stopped capitalising them and/or began treating them as expenses in the second quarter of 2000 .' Using Aggressive Accounting Practices : Barter Trade & Grossed Up Revenues Stimulated by concern raised by the regulatory bodies Securities and Exchange Committee (SEC) and the Federal Accounting Standard Board (FASB), Angela Davis [May 2000, AD] studies the value relevance of revenues for Internet firms . In particular, she studies whether reporting grossed-up or barter revenues has any impact on their valuation between January 1998 and November 2000 . Consistent with prior studies she finds a positive association between market value of equity and revenue announcements . This provides evidence for the notion that Internet managers had incentives to increase reported revenue, possibly through the use of grossed-up and/or barter revenue . Davis concludes that use of these aggressive accounting techniques appears to be concentrated in only a few Internet sectors, particularly e-tailers, content/community firms and portals . There is a positive and highly significant relation between quarterly value and revenue for these firms in both the pre- and post-crash periods . She finds that there is no statistically significant positive relation between market capitalisation and earnings in the pre-crash period but it becomes positive and statistically significant in the post-crash period . She further concludes that the market's valuation of revenue declines from the pre- to the post-crash period for firms reporting grossed-up revenue and/or barter revenue while this remains virtually unchanged for firms not reporting grossed-up or barter revenue . Additionally, she shows that the larger the number of individual investors following an Internet company, the less value is

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attributed to reporting grossed-up revenue and barter revenue. This suggests a changing investor behaviour in the post-crash period . In a more recent study, Bowen, Davis and Rajgopal (BDR, 2001) focus on economic motives underlying management's accounting choices of Internet companies . First, BDR document the extent to which Internet companies use allegedly aggressive revenue recognition policies and confirm the results of Davis (2001) . Second, they examine the economic incentives that potentially influence firms to choose aggressive revenue accounting practices . Where Davis (2001) and Bagnoli et al . (2001) find that the market responds to revenue surprises, BDR document that revenue levels are strongly associated with market values of Internet firms . This leads them to conclude that managers of Internet firms have economic incentives to report high levels of revenue . Since this strategy cannot be sustained for long, the question is why Internet managers seek to fool the markets . Specific incentives to influence stock prices according to BDR include : (1) stock option compensation ; and (2) access to (and cost of) equity capital . Furthermore, Internet managers also likely have incentives to influence third parties (e .g . capital providers other than stock market investors, suppliers and customers by reporting higher revenues) . Similarly, they find a positive association between the use of both barter and grossed-up revenue and the extent of cash burn, suggesting that pressure to seek external financing influences the aggressive revenue recognition choices of Internet firm managers . Cash-Burn Ratio Demers and Lev (DL, 2000) construct a proxy for B2C companies' ability to sustain their current rate of cash burn . Their proxy for cash burn is cash on hand divided by current period's cash flows from operations . They find that this proxy is a significant value-driver in both 1999 and 2000 . Their proxy is defined as [cash on hand]/[current period's cash flows from operations] . For both 1999 and 2000, this ratio is significantly associated with the price-to-sales ratio of Internet companies . For companies with negative cash flows form operations, the ratio is negative, and therefore the significantly positive coefficient results in a reduction to overall firm value . The intuition for this might be, that free cash flows (i .e . significant cash stores and limited cash burn) provide these Internet companies with greater option value for growth . Thus, cash provides these companies with the flexibility to adapt to rapidly changing market conditions and to react to emerging opportunities .

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"NON-FINANCIAL" VALUE DRIVERS In this paragraph we discuss studies analysing non-financial value drivers . Web metrics will be discussed extensively, together with managerial actions, strategic alliances and stock options . Web Site Usage : Indicator of Potential Future Demand Trueman, Wong and Zang (TWZ, 2000) consider the role of non-financial data in the valuation of Internet stocks . They provide insight into valuation of Internet stocks by examining the impact of both accounting information and measures of Internet usage . First they relate Internet stock prices with accounting information . To that extent they decompose the firm's earnings in three components : gross profits, operating expenses and non-operating expenses . They assume future gross profits to be positively and linearly related to a current period's gross profit, operating expenses and web site usage . This latter is based on the assumption that current period Web site usage reflects potential future demand for the companies products and affects the rates a firm can charge for advertising on the company's Web sites . Web usage is measured alternatively by the number of unique visitors to the firm's Web site and by the number of page views at its site . Consistent with those who claim that financial statement information is of very limited use in the valuation of Internet stocks (such as Stern, Stewart, Bagnoli et al .), TWZ are unable to detect a significant positive association between bottom-line net income and their sample of market prices . In fact, the association is actually negative . However, when decomposing the net income into its components, they find that book value and gross profits are significantly and positively related to stock price . Market Values of Web Businesses in Relation to Web

Traffic

Rajgopal, Kotha and Venkatachalan (RKV, 2000a) analyse the importance of web traffic as a non-financial indicator of the market values of B2C Internet firms . They examine a sample of 92 firms (compared to `only' 56 firms for TWZ) over the five quarters beginning with the first quarter of 1999 . They find that the number of unique monthly visitors to a site is positively associated with stock prices and adds significant incremental explanatory power to a valuation regression including just financial statement information. Internet companies use many strategies to generate

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traffic, such as an alliance with America Online (AOL), extensive marketing expenses and media visibility . Consistent with TWZ (2000b), RKV find that web traffic levels predict one- and two-quarter ahead sales . However, traffic has no incremental information about future revenues once the predictive information in past sales is controlled for. Hence, the market does not appear to value traffic merely because it predicts future sales. Finally, RKV find that the market values of web businesses are positively associated with the number of unique visitors to the firm's web site . While RKV offer important insights to the literature, the direct link with the valuation of Internet companies is weak . Log Linear Regression on Web 0

Traffic Data

Hand (2000b) claims to clarify the previous research (i .e . RKV, 2000a; TWZ, 2000a ; Hand, 2000a) by investigating the value-relevance of a more comprehensive set of web traffic measures . He assesses the value-relevance of unique visitors, household reach, page views, the number of hours spent on the page views and the gender, age and income levels of the visitors . This reveals the value relevance of supply and demand forces on Internet stocks . He regresses the log-transformed market value of equity on log transformations of three groups of factors : economic fundamentals, web traffic and supply and demand forces . Economic fundamentals are captured by current book equity, forecasted one-year earnings and forecasted long-run growth in earnings . Contrary to popular perception, he finds that the prices of Internet stocks are not driven by web traffic as much as by economic fundamentals . Current book equity, one-year-ahead forecasted earnings and long-run-forecasted earnings growth dominate in explaining cross-sectional variation in Net stock prices . Only marginally are Internet firms' market values related to only one of the three measures of web traffic : the number of unique visitors to the firm's web site . Supplementary tests also indicate that Internet firms' equity market values are unrelated to the average income, age or gender of Web site visitors . Third, after controlling for economic fundamentals and web traffic, Internet firms' equity market values are negatively correlated with their public float, indicating that Internet firms stock prices are higher the lower is the fraction of total shares available for trading . In contracts, Internet firms' equity values are positively correlated with their short interest and institutional ownership, suggesting that Internet firms benefit from the stability and/or reduced irrational trading risk provided by greater institutional ownership .


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In summary, Hand concludes that pricing of U .S . Internet stocks is dominated by expectations of near- and long-term profitability, although they are also uniquely impacted by some non-traditional value-drivers such as web metrics . Using Lagged Web-Traffic Metrics

Demers and Lev (DL, 2000) expand upon the prior web metric studies by examining the value-relevance of three web performance factors both before and after the Internet market correction in March-April of 2000 : (i) reach (attraction of new visitors to a Web site) ; (ii) stickiness (retention of visitors on the site) ; and (iii) customer loyalty (ability to generate repeat visits from surfers who have been attracted to the site in the past) . They find that both contemporaneous levels and changes in various web traffic metrics are significantly correlated with monthly stock return in each of 1999 and 2000, but the significance levels decrease in the later time period, suggesting at reduction in the implicit valuation of web traffic . More strikingly, the one-month lag in web traffic levels is significantly correlated with monthly stock returns in 1999, but the significance disappears in 2000 . Thus, while investors generally appear to react promptly to the release of traffic measures, the results suggest that there may have been some delayed reaction by the market in the earlier stages of the Internet economy . DL's findings of significance for the year 2000 on reach and stickiness confirm that web traffic measures remain important after the burst of the bubble in 2000 . Thus, even as the Internet sector begins to mature and B2C companies develop longer operating histories (so that a longer time series of financial valuation variables becoming available), web traffic metrics that were relevant during the bubble period of the market continue to be significant determinants of Internet companies' price-to-sales ratios after the Internet shakeout, at least throughout 2000 . The Impact of Financial Analysts

Lui and Song (LS, 2000) study the role of financial analysts in the Internet stock bubble in spring 2000 . They try to test if financial analysts as a whole overestimated the prospect of Internet companies . To this extent they examine the change of financial analysts' earnings forecasts around the crash in April 2000 . They conclude that :

• Financial analysts as a whole were too optimistic about the Internet stocks before the crash . The percentage of analysts who were optimistic declined

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significantly after the crash (from 41 .5% before the 33 .3% during the crash quarter and 35 .6% one quarter later) . Analysts revised down their forecasts significantly after the burst of the bubble ; • Although financial analysts seemed to change their forecasting significantly for the quarterly earnings, they remained quite upbeat on the long-term prospects of the Internet firms ; • Forecasts of analysts whose affiliated brokerage firm had done initial public offerings (IPO) for the Internet companies are more negative in their quarterly earnings forecasts relative to those of non-affiliated analysts . Especially on the quarter after the crash, the financial analysts had 19% of the optimistic forecasts, while non-affiliated analysts had 35% . This is important evidence that some investment banks oversold Internet IPO's . Bagnoli, Kallapur and Watts (BKW, 2001) perform a similar study . They examine the characteristics and relative information content of revenue and earnings news for Internet firms during and after the bubble . In line with TWZ (2000b) they find a tendency for analysts to underestimate revenues . Keating, Lys and Magee (KLM, 2001) examine the impact of analysts forecasts as well as new disclosures on Internet stock prices . Financial analysts' average buy/sell recommendation became less favourable for pure Internet firms and more favourable for support firms . While statistically significant, the changes were, however, small, and do not suggest a major systematic shift in analyst's attitudes towards Internet stocks . KLM conclude that analysts recommendations were mixed : less favourable recommendations for pure firms and more favourable recommendations for support firms . The only significant adverse changes were seen in fiscal year 2000 earnings forecasts which were lowered significantly . Managerial Actions : rose .co m Cooper, Dimitrov and Rau (CDR, 2000) analyse the effect of a corporate name change (i .e . taking a name that ends o n .co m or .net) . In the popular press, CDR came across several articles that mention extremely large returns earned by these companies . These articles suggest the large returns being an effect of irrational day traders searching stock chat sites on the Internet . Companies that announce dot.co m name changes between June 1, 1998 and July 31, 1999 gave a mean return of 142% above that of similar companies during the period between 15 days before changing their name and 15 days after changing their name . The return is 122% for Internet companies and 203% for companies


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that have no relation with the Internet. The change in value does not appear to be temporary! CDR do not find evidence of a negative post-announcement drift even when removing the more extreme observations . CDR question whether it is a rational response for investors to apply a `premium' to a dot .com stock. They find that in the shorter horizons, market participants appear to apply a similar positive price premium across all companies changing their names to dot .co m names, regardless of a company's level of involvement with the Internet . In the longer horizon, and with the caveat that the sample size of the category not Internet related is very small, firms that have less involvement with the Internet have the greatest returns following a d ot .com . Overall a mere association with the Internet seems enough to provide a firm with a large and permanent value increase . CDR conclude that their results indicate irrational exhuberance by investors to be associated with the Internet at all costs . However, they acknowledge that only time will prove whether investors are rational in pricing large expectations of future earnings from the Internet into the stock price . The Value Relevance of Strategic Alliances Analysts' reports and anecdotal evidence suggest that strategic alliances are potentially important value drivers for Internet stocks . Demers and Lev (2000) find support for this argument after examining whether these are still positively valued by the market in 2000 . A strategic alliance with AOL has a positive relation to P/S ratio of B2C stocks in 1999, which complements the view of RKV (2000) who document a weak positive association between alliances with AOL and web traffic measure for reach during 1999 . In 2000, they find that AOL alliances are not adding value to B2C stocks . They also find that the cumulative sum of alliances entered into is significantly negatively associated with the P/S ratio in both 1999 and 2000 . Stock Option Plans Keating, Lys and Magee (2001) examine the valuation effect of Internet companies granting employee stock options . Stock grants are a form of employee compensation that many regard as an expense . Most companies grant options with strike price equal to the current market price which under current rules results in no expense recognition . In addition, changes in market conditions which make stock options less attractive for employees will force firms to offer more cash to attract and retain them, so in this case there is a

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risk that the company will not be able to fulfil investor's growth expectations . KLM argue that if these effects dominate investors' valuation of Internet firms, then the coefficient on stock option grants in the valuation regression should be negative . KLM construct as stock option variable the fair market value of options granted as disclosed during 1999 in the annual report footnotes . They expect this variable to change from March 2000 regression to the May 2000 regression in both a valuation model and a returns model . They conclude that investors positively view stock-option grants, most likely due to their effect on employee retention .

USING THE REAL OPTION APPROACH TO VALUE GROWTH OPPORTUNITIES The evidence on Internet firm valuation suggests a frequent use of nonfinancial information, which suggests that the market is attempting to use proxies for growth opportunities . We review the leading approach for their valuation : the real option approach . Using Real Options

Real options represent the most recent capital budgetting techniques to value growth opportunities . Schwartz and Moon (SM, 2000) and Perotti and Rossetto (2000) argue that Internet companies have call-option characteristics since they have large potential upside and limited downside potential (i .e . bankruptcy) . Furthermore, Internet companies have volatile revenues, rates of growth in revenues, costs, cash flows and stock prices . Since options are often claims on highly uncertain outcomes, these valuation techniques may be used to value Internet companies . Many strategic investments create opportunities that eventually will or will not be taken . In these cases, the investment opportunity can be viewed as a stream of cash flows plus a set of options . The real options approach involves options on real (non-financial, non-traded) assets . Uncertainty is defined as the unavoidable randomness of the external environment . Exposure to uncertainty is by consequence defined by a multiple of factors and can only be partially controlled by managerial actions . Risk is thus the potentially adverse consequence of a firm's exposure . The traditional valuation models are not appropriate to deal with these uncertainties since they normally use a single expected value of the future cash flows and it is difficult to find the appropriate discount rate for the options present (e .g . exit option) methods . SM (2000) argue, using a conventional DCF model with stochastic growth rates, that depending on the parameters chosen and given high enough


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growth rates of revenues, that the value of Internet stocks can be rationalised . Even when the chance that a company may go bankrupt is real, if the initial growth rates are sufficiently high and if there is enough volatility in this growth over time, valuations can be what would otherwise appear to be unbelievably high . In addition, they find the valuation has great sensitivity to initial conditions and exact specification of the parameters . This finding is consistent with observations that the returns of Internet stocks have been strikingly volatile . In 2001, SM apply the model to price the Internet company Ebay . Its market price at the time of measuring (11 April, 2000) was U .S .D 39 .17, 75% above the model price . The reasons could be that the market is implying higher margins, growth rate expectations and growth rate volatility and/or the model is missing some key aspects of value . Alternatively Ebay was overvalued at that date . The more recent stock price information suggests the latter . Even though real option valuation may not justify Internet valuation, it deals explicitly with option-like characteristics of Internet companies (i .e . asymmetric payoffs, large uncertainty in sales and sales growth and cost uncertainty) . Internet Portals as Portfolios of Entry Options Perotti and Rossetto (PR, 2001) investigate the valuation of platform investment, such as a software operating system or an Internet portal web page . They define a platform investment as a creation of an innovative distribution and production infrastructure which increases access to customers ; as a result it reduces entry costs in related products . A portal is a point of entry connected with relevant information or access to products . It lowers the cost of reaching customers and thus the cost to entry new market segments . PR make a distinction between horizontal and vertical internet portals . They relate the platform investment to the strategic real option literature . They build a model of the value of associated strategic advantages and compare the relative valuation of such `platform stocks' versus conventional producers (old economy) . In the comparative statistics they focus on the effects of uncertainty and show that it produces the greatest difference between option-based and NPV-based models of valuation . The effect of uncertainty on the strategic advantage and thus of the platform value is not obvious as there are two future countervailing effects : the higher the uncertainty the higher the value to wait, but at the same time the higher the expected profits in case of immediate cross entry . The important result is that in a context of increased uncertainty, the relative value of platform to traditional strategies increases ; the value of waiting


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to invest rises, but the value of platform increases even more . In some cases, platforms can reduce entry by making a parallel monopoly sustainable .

PR conclude that relative to conventional producers, portal-related firms enjoy

enhanced entry options in uncertain market segments, which may be valuable under some market conditions on Internet usage . Their advantage lies in the ability to exercise entry options at the optimal strategic timing .

CONCLUSIONS Based on the overview that has been given of recent academic research on the valuation of Internet firms, we draw the following conclusions :

I . Traditional accounting data remains important for valuing Internet companies yet the link between accounting numbers and Internet valuation is tenuous at best . • Large marketing expenses, R&D and selling expenses seem to be activated as intangible assets or valuable investments ;

• Internet firms which were profitable in 1999 are valued more realistically after the burst of the bubble (April, 2000), when bottom line profit does matter much more ;

• Aggressive accounting techniques (grossed-up revenue and/or barter

revenue) may have responded to exaggerate the focus on revenues . However, revenues are only seen as main value driver before the burst in 2000 . Thereafter the relationship seems to break down ;

• Managers have economic incentives (stock option compensation and access to/cost of equity capital) to report higher revenues .

II .

Web traffic is not a major value driver for Internet companies . • Web traffic is only value relevant for e-tailers ; more precisely it appears in fact to be used as a good predictor for future sales ;

• The significance of web traffic as a driver of monthly stock return •

disappears after the burst of the bubble ; If any, then only number of unique visitors is a useful web traffic variable in relation to valuation of Internet stocks .

III . Financial Analysts stimulated the overvaluation of Internet stocks : • Analysts were too optimistic in their forecast of quarterly earnings before the market crash, revenues were significantly downgraded after the burst of the bubble ;

• Long term prospects remained upbeat after the burst of the bubble ;


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• Focus during the boom shifted from profit to earnings ; • Support firms were revised upwards after the burst, B2C revised downwards, thus more value is attributed to infrastructure providers .

IV . New valuation factors deem unsustainable : Corporate name changes into dot .com, strategic alliances and stock option plans in the long term are not adding value to Internet stocks, only before the burst have they an impact on Internet stocks . V . New approaches to valuing Internet stocks are not new : a. Volatility of growth in revenues is a key value driver for Internet companies, this option variable rationalises the high valuation of Internet stocks ; b . Internet platform related firms enjoy enhanced entry options in uncertain market segments, to be exercised at the optimal strategic timing . The better the timing, the higher the value . All in all, we find no convincing evidence on new valuation techniques and/or value drivers for Internet stocks . Web traffic has important effects on predicting future sales and thus contributes to expectations on near- and long-term profitability but has no consistent direct impact on the valuation of Internet stock valuation . For strategic alliances, granting employee stock options and changing the company name into dot .com, only the latter impacted on the valuation of Internet stocks. This suggests that it is rather irrational exuberance before the crash than extreme optimism . Extreme optimism did play a role though . It is generally agreed upon that Internet stocks have many uncertainties - e .g . volatile (rates of growth) of revenues, cash flows and earnings - and it is a business that has little historical track record . However, after the burst of the bubble it has become obvious that the expectations (even those from financial analysts) were too optimistic and that this was the main reason for the overvaluation of Internet stocks . Traditional valuation techniques have not lost any of its relevance . It is just a matter of using the correct parameters and presenting them in a correct matter in the financial accounts to make the valuation of Internet stocks a `mission possible' !

NOTES 1 . This article is an abbreviated version of a paper, written as part of the Master in International Finance Program of the University of Amsterdam . 2 . Based on the relative information content provided by Theil (1987) .

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3 . EVA is calculated as [adjusted operating profit after tax-/-(cost of capital x adjusted capital employed)] .

REFERENCES Bagnoli, M ., Kallapur, S ., & Watts, S . (2001) . Top line and bottom line forecasts : a comparison of Internet firms during and after the bubble . Krannert Graduate Schoool of Management . Purdue University . Brealey, R. A., & Myers, S . C . (2000). Principles of Corporate Finance (6th ed.) . Irwin McGraw-Hill . Cooper, M . J., Dimitrov, 0 ., & Raghavendra, Rau, P . (2000). A rose .com by any other name (November) . Purdue University. Davis, A . K . (2001) . The value relevance of revenue for Internet firms : does reporting grossed-up or barter revenue make a difference? Journal of Accounting Research, conference version (May) . Washington University, St . Louis. Demers, E ., & Lev, B . (2000) . A Rude Awakening : Internet Shakeout in 2000 (September). William E . Simon Graduate School of Business Administration, University of Rochester . Financial Accounting Standard Board (2000) . EITF Abstracts 99-17, Accounting for Advertising barter transactions (January) . Financial Accounting Standard Board (2000) . EITF Abstracts 99-19, Reporting revenue gross as a principal versus net as an agent (May) . Greene, W. H . (1993) . Econometric Analysis (2nd ed .), 702. Prentice-Hall Inc. Englewood Cliffs, New Jersey . Hand, J . R. M . (2000a) . Profit, Losses, and the Non-linear Pricing of Internet Stocks, draft (January) . Kenan-Flagler Business School, UNC Chapel Hill . Hand, J . R. M . (2000b) . The role of economic fundamentals, web traffic, and supply and demand in the pricing of U.S. Internet Stocks (April) . Kenan-Flagler Business School, UNC Chapel Hill . Ho, N ., Hui, N., & Li, L. (2000) . Does EVA © beat earnings? Round 2 : evidence from Internet companies . University of New South Wales . Keating, E ., Lys, T ., & Magee, R . (2001) . The Internet downturn : finding valuation factors in spring 2000 (January) . Kellog graduate School of Business, Northwestern University . Kozbeg, A . (2001). The value drivers of Internet stocks : A business models approach (April). Stem School of Business, New York University. Liu, Q., & Song, F . (2001) . The rise and fall of Internet stocks : should financial analysts be blamed? (March) . University of Hong Kong . Milano, G ., Stern, E ., Fencl, T ., & Piza, N . (2000) . Internet valuation, why are the values so high? (February) . Stem Stewart Europe Limited . Perotti, E ., & Rossetto, S . (2000). Internet portals as portfolios of entry options, Preliminary draft (November 8) . University of Amsterdam and CEPR . Rajgopal, S ., Kotha, S ., & Venkatachalam, M . (2000a) . The relevance of web traffic for Internet Stock Prices (October) . University of Washington/Stanford University . Rajgopal, S ., Kotha, S ., & Venkatachalam, M . (2000b) . Managerial actions, stock returns and earnings: the case of business to business Internet firms (October) . University of Washington/Stanford University . Roon, F . (2000) . Valuing Dot.com (September) . Erasmus University Rotterdam .


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Schwartz, E., & Moon, M . (2000) . Rational pricing of internet companies . In : Financial Analysts Journal (Vol. 56, No . 3, (May/June)) . Association for Investment Management and Research. Schwartz, E., & Moon, M. (2000). Rational pricing of Internet companies revisited (revised April 2001). Anderson School at University of California, Los Angelas . Schwartz, E. S ., & Moon, M . (forthcoming) . Rational pricing of Internet companies Financial Analysts Journal . Shapiro, C ., & Varian, H . (1999) . Information Rules. Boston, MA : Harvard Business School Press . The Industry Standard (2000) . Portals start to feel the heat (May 1), 63 . Trueman, B ., Franco Wong, M . H ., & Zhang, X-J . (2000a) . The eyeballs have it : searching for the value of Internet stocks (January) . Haas School of Business, University of California Berkeley, Berkeley CA . Trueman, B ., Franco Wong, M . H ., & Zhang, X-J . (2000b) . Back to basics : forecasting the revenues of Internet firms (April) . Haas School of Business, University of California Berkeley, Berkeley CA . Trueman, B ., Franco Wong, M . H ., & Zhang, X-J ., (2001) . Anomalous stock returns around Internet firms' earnings announcements (April) . Haas School of Business, University of California Berkeley, Berkeley CA . UBS Warburg Global Equity Research (2000) . Navigating the I-valuation Jungle ( www. ubswarburg .com/researchweb) , May . Vuong, Q . (1989) . Likelihood Ratio Tests for Model Selection and Non-Nested Hypotheses, Econometrica, 57, 307-334 . Wingfield, N. (1999) . The tricky task of tracking web users, p . Cl, Wall Street Journal (November 22) .

DECISIONS IN FINANCIAL ECONOMICS : AN EXPERIMENTAL STUDY OF DISCOUNT RATES Uri Benzion and Joseph Yagil ABSTRACT Using three subsamples of subjects that differ in their level of formal education and knowledge in economics, this experimental study of intertemporal discount rates finds that subjective discount rates decrease with the time delay and monetary sum, and are higher for postpone-a-receipt than for a postpone-a-payment scenario . The findings indicate the existence of market segmentation, implicit risk and a weak added compensation . The findings also imply that subjective discount rates are lower and closer to real market rates of interest for subjects having higher levels of formal education and economics knowledge.

1 . INTRODUCTION The discount rate is one of the central concepts in financial economics . Its exact meaning in economics may vary across economic scenarios and can take such forms as borrowing rate, lending rate, and required return on financial assets such as bonds and stocks . While the discount-rate issue has been examined extensively in the financial economics literature, less attention has been devoted to the behavioral aspects of individuals' subjective (derived) discount rates . In Innovations in Investments and Corporate Finance, Volume 7, pages 1940 . Copyright © 2002 by Elsevier Science Ltd. All rights of reproduction in any form reserved. ISBN : 0-7623-0897-4 19

20

URI BENZION AND JOSEPH YAGIL

recent years, however, behavioral studies inspired by the pioneering work of Kahneman and Tversky (1979), focus on the psychological rather than the economic aspects of individuals' financial behavior . The objective of this study is to extend this line of research by focusing on subjective discount rates inferred from individuals' financial decisions . More specifically, the purpose of this study is twofold : (1) to re-examine the behavior of subjective discount rates across three dimensions : financial scenario (such as lending and borrowing), time delay and the monetary sum of the cash flow ; and (2) to investigate the extent to which the behavior of subjective discount rates is affected by the formal education level of the decision makers and their level of knowledge in financial economics . For three subsamples of a distinctive group of subjects, who vary with respect to the formal education level (undergraduates and graduates having bachelor's degree, master's degree or higher), and their knowledge level in financial economics, we find a very high similarity with respect to the behavior of their discount rates across the scenario, time and sum dimensions . However, the level of the subjective discount rates is lower for graduates than for undergraduates . The knowledge of financial economics also has a depressing effect on the level of the discount rates inferred from the subject's financial decisions . The plan of this study is as follows : Section 2 briefly reviews the relevant literature ; Section 3 presents the theory and hypotheses ; Section 4 discusses the experimental design ; Section 5 analyzes the findings ; and finally, Section 6 provides a brief summary .

2. LITERATURE REVIEW Experimental studies of subjective discount rates are reported by Thaler (1981), Benzion, Rapoport and Yagil (1989), Benzion, Granot and Yagil (1992, 1994), as well as others . Generally, the results of these studies are found to be consistent with psychological models based on the notion of a reference point discussed in Kahneman and Tversky (1979), Loewenstein (1988), Loewenstein and Thaler (1989), and Thaler (1994) . One of the features of Kahneman and Tversky's (1979, 1986, 1992) prospect theory is the reluctance to realize losses . This reluctance is examined by Shefrin and Statman (1993, 1994) . The issue of subjective discount rates studied here is also related to the overconfidence issue examined recently in the literature . Daniel, Hirshleifer and Subrahmanyam (1998) summarize a large body of evidence from cognitive psychological experiments and surveys, which shows that individuals overestimate their own abilities in various contexts . Based on this premise of investor overconfidence, they develop a theory of security markets . Specifically,

Decisions in Financial Economics

21

they note two well-known psychological biases : investor overconfidence about the precision of information and biased self-attribution, which causes asymmetric shifts in investors' confidence . The overconfidence phenomenon has recently been investigated further by Barber and Odean (2000, 2001), and Gervais and Odean (2001) . It is argued there that overconfidence can explain high trading levels and the resulting poor performance of individual investors . Barber and Odean (2001) also find that men are more overconfident and trade more excessively than women . Barberis, Shleifer and Vishny (1998) also note the large body of evidence concerning underreaction and overreaction which indicates that in the short run, security prices underreact to news, while in the long run they overreact to consistent patterns of news pointing in the same direction . Their findings, they emphasize, challenge the efficient market theory . They propose a parsimonious model of investor sentiment (how investors form beliefs) that is consistent with available statistical evidence . In a recent study on mental accounting, Barberis and Huang (2001) argue that it is possible to improve our understanding of firm level stock returns by employing the experimental evidence related to Kahneman and Tversky's (1979) "loss aversion", concept which asserts that people are more sensitive to losses than to gains . Another closely related concept is "myopic loss aversion" which, as defined by Thaler, Tversky, Kahneman and Schwartz (1997), is the combination of a greater sensitivity to losses than gains, and the tendency to evaluate outcomes frequently . They, as well as Gneezy and Potters (1997), find that the more frequently returns are evaluated, the more at risk investors are . Another related concept is Thaler's (1987) "mental accounting" term, which refers to the process by which people think about and evaluate their financial transactions . Experimental studies suggest that, when doing their mental accounting, people engage in "narrow framing", or narrowly defined gains and losses . Loss aversion and narrow framing have already been applied to the aggregate stock market and retirement investment by Benartzi and Thaler (1995, 1999) . Motivated by their studies, Barberis, Huang and Santos (2001) introduce loss aversion over financial wealth fluctuations into a dynamic equilibrium model, and find that it captures a number of aggregate market phenomena . Another two related studies are Ferris Haugen and Makhija (1988) on investors' reluctance to realize losses and De Bondt and Makhija (1988) on managers' reluctance to terminate economically unviable projects . In his attempt to reconcile market efficiency and behavioral finance, Fama (1998) contends that overreaction of stock prices to information is about as common as underreaction . In a recent survey of investor psychology and asset pricing, Hirshleifer (2001) sketches a framework for understanding decision biases, and discusses the importance of investor psychology for security prices .

22


This study attempts to extend prior studies of subjective discount rates, such as those by Thaler (1981) and Benzion, Rapoport and Yagil (1989), by re-examining the discount rate behavior with respect to the three dimensions of scenario, time and sum, and investigating the effect of formal education and economics knowledge on both the level and variation of subjective discount rates .

3. THEORY AND HYPOTHESES The standard relationship underlying discounting and compounding of time-varying cash flows is given by : F=P (1 +R

)T

(1)

where F and P are respectively the future and present value of a certain cash flow, R is the discount or capitalization rate, and T is the time factor . Eq . (1) is used to infer the subjective discount rate inherent in intertemporal decisions involving monetary sums . The pure economic meaning of the discount rate is the marginal rate of substitution between current and future consumption . In that respect, the discount rate may depend on time preferences as well as other factors, affecting the utility function . However, given the availability of borrowing and lending in perfect capital markets, the marginal discount rate is equal to the interest rate in the market . This perception is the foundation for what may be called the "classical approach" asserting that in perfect and certain capital markets, the discount rate is identical for all individuals and for all (financial economic) situations or scenarios . Furthermore, it will also be invariable with respect to the monetary sum involved, and the time factor ; that is it is time symmetric . A different view regarding the scenario effect is implied by the "market segmentation" approach, which implies that, due to capital market imperfections and the lack of perfect arbitrage, the rate of borrowing will be higher than the rate of lending . Similar to the classical approach, this approach also advocates that in each market segment, the discount rate will be invariant with respect to both time delay and sum of cash flow . Behavioral studies by Kahneman and Tversky, Thaler, and Loewentstein establish that even when the intertemporal decision problem is formulated in terms of certainty conditions, as in our experiment, the subjects frame it as one involving some implicit degree of risk. The "implicit risk" approach assumes that, in making their intertemporal decisions, individuals believe that there is some probability that delayed receipts will not be paid and delayed payments will not be collected . Benzion and Yagil (1987) offer two hypotheses concerning


23

the effect of risk : the "one-period-realization" (OPR), and the "multi-periodrealization" (MPR) . By the first hypothesis, the risk associated with a project or an outcome can be incorporated in the first period only, so that subsequent time periods merely reflect the pure time value of money given by the risk-free rate of interest . Formally stated, it is : F=P(1+d) (1+i) T

(2)

where i is the risk-free rate of interest and d is the risk premium . According to the alternative MPR hypothesis, risk is incorporated in the discount rate applicable to each future time period. Formally, it becomes : F=P [(1+d) (l+i)]T=P(l+d)T(1+i)T

(3)

Equations (2) and (3) demonstrate the formal difference between the two competing hypotheses : the power of the gross risk premium (1 + d) is 1 and T by the OPR and MPR, respectively . To test these two alternative risk hypotheses empirically, the OPR Eq . (2) can equivalently be written as : Ln(F/P)=Ln(1+d)+Ln(l+i)T

(4)

and the MPR Eq . (3) as : Ln(F/P)=Ln[(l+d) (1+i)IT

(5)

The resulting estimated regression equation for both the OPR and MPR hypotheses will then be : Ln(F/P)=bo +b 1 T+e

(6)

where bo and b i are the ordinary least squares (OLS) coefficients, and e is the error term . By the OPR : b o = Ln (1 + d), and b 1 = Ln (1 + i ), while by the MPR : bo = 0, and b I = Ln [(1 + d) (1 + i )] . It then follows that if b o is not statistically different than zero, and b i is statistically greater than the risk-free rate of interest, it would lend support to the MPR rather than the OPR risk hypothesis . The "added compensation" approach asserts that, in addition to other factors affecting a temporal monetary decision, there is an added compensation or premium associated with changing one's position or making readjustments . Furthermore, the premium will be positive for a negative position change and negative for a positive position change, where a negative change involves

24


receiving a cash flow in the future instead of in the present, while a positive position change involves paying a loan in the future instead of in the present . Stated differently, individuals will require a compensation for postponing a receipt, but on the other hand, will be willing to offer a premium for postponing a payment. The formal representation of the added compensation component (Q) can take various mathematical forms, one of which is the following : F = QP (1 + R ) T

(7)

For a negative position change such as a postponing-a-receipt scenario, Q should be greater than unity ; while for a positive position change such as a postponing-a-payment scenario, Q should be less than unity (and positive) . Eq. (7) can be rewritten as : Ln(F/P)=LnQ+Ln(1+R)T

(8)

and its regression form is Ln(F/P)=bo +b 1 T+e

(9)

where Q = e b0~, implying that b o should be positive (negative) for a negative (positive) position change . These four hypotheses, discussed in more detail by Benzion, Rapoport and Yagil (1989), can be summarized as follows : the classical approach advocates that the discount rate is universally uniform across scenario, time, and sum, while the market segmentation approach asserts that it may differ across financial scenarios, but in each scenario it will be independent of time and sum . The implicit risk approach states that even certain (risk-free) intertemporal decision problems are framed by subjects as involving some degree of risk, causing the inferred discount rate to be higher than the risk-free rate of interest, where the implicit risk will be incorporated either by the OPR or MPR hypotheses . The added compensation approach further asserts that a position change will be associated with an additional compensation or premium . These four hypotheses are tested in the subsequent sections . In addition, we investigate whether the magnitude of the impact on the discount rate implied by each of these hypotheses is related to the levels of formal education and economics knowledge .


25

4. METHODOLOGY The questionnaire (fully presented in the appendix) consists of two parts corresponding to two scenarios : postponing-a-receipt and postponing-a-payment . As noted in the previous section, these two scenarios are examined in order to test whether the discount rates inferred from the subjects' responses are different, reflecting different levels of risk premium or compensation for a position change . In the first part of the questionnaire (postponing-a-receipt scenario), subjects are asked to state the amount of dollars required to leave them indifferent about receiving this amount in the present or receiving a higher amount in the future . Three time periods are offered 0 .5, 2, and 5 years, and three sums of $200, $600, and $5,000 . This trade-off between present and future is made with respect to a bank savings account in which the level of risk is practically negligible . The second part of the questionnaire involves a postponing-of-payment scenario. Subjects are asked to state the dollar amount they would be willing to pay in the future for a bank loan made to them in the present, which will make them indifferent about the difference between the dollar sum in the present, and the higher sum in the future . The same parameter values of time and sum are offered as in the first part of the questionnaire . Finally, the subjects are asked to state their highest academic degree (bachelor's degree, master's degree or higher), and specify the discipline or the field of study . The questionnaire was distributed to 117 subjects from the Technion (Israel Institute for Technology) and Haifa University . The sample was drawn from the following populations : undergraduate students in industrial engineering and economics ; graduate students from different disciplines, and those who had earned a master's degree or higher in an academic discipline . All questionnaires were filled out in the same week. Out of the 117 subjects who participated in the experiment, 105 completed all questions in the questionnaire, resulting in the following three subsamples : 45 undergraduates, 30 graduates, and 30 having a master's degree or higher . The disciplines of the last two subsamples are social, exact and natural sciences . The proportion of Economics majors was 59% in the bachelor's-degree subsample compared with only 30% in the mater's-degree subsample . Advantages and disadvantages of experimental studies noted in the literature apply to this study as well . Nonetheless, the questions were examined carefully, and the subjects were given sufficient response time, and were informed of the importance of the topic . Therefore, we believe that the subjects were positively motivated to complete the questionnaire with necessary care and diligence even without being offered monetary incentives,

26


which, psychologists claim, do not necessarily improve performance . Gneezy and Rustichini (2000) found in their experimental study that, in contrast to the economic convention, subjects who were offered monetary incentives performed more poorly than those who were offered no compensation . The analysis of our experimental findings also implies that reliable responses appear to have been part of the subjects' utility functions .

5. RESULTS As noted in the previous section, two decision-making scenarios are tested : (1) postponing a payment ; and (2) postponing a receipt . These two scenarios are examined for varying times periods (0.5, 2, and 5 years), varying dollar sums ($200, $600, $5,000), and three subsamples (economics and industrial engineering undergraduates, and two groups of graduates : those who possess a bachelor's degree, and those who possess a master's degree or higher . The dollar responses of each subject were compiled, and implicit discount rates were derived from the dollar responses . The mean across subjects of the implicit discount rates are presented in Table 1 for the three subsamples by the three dimensions investigated : scenario, time, and sum . A detailed analysis of the pattern of the discount rates with respect to these three dimensions appears in Tables 2, 3 and 4 . Starting with the effect of time on derived discount rates, the major finding exhibited in Table 2 is that the inferred discount rates required decrease with time . The overall mean discount rate across subjects, scenario, and sum (indicated by the last line in Table 2) decreases from 14 .73% to 10 .90% and 8 .99% for 0 .5, 2, and 5 years, respectively . This time pattern of inferred discount rates is consistent with previous findings in the literature (e .g . Benzion, Rapoport & Yagil, 1989) and is different from the time pattern of market actual interest rates that generally increase with time . This difference is investigated later in this section . As demonstrated in Table 2, the decrease of discount rates with time holds across the two scenarios examined (postponing a receipt and postponing a payment), across sum and for all three subsamples . Also, the effect of time on discount rates is found to be decreasing at a diminishing rate . As indicated by the overall mean rate in the last line of Table 2, lengthening the time delay from 0 .5 to 2 years results in a reduction of about 4% in the discount rate, while the time change from 2 to 5 years only reduces the discount rate by about 2% . This "diminishing rate" property also holds across all scenarios, sums and subsamples tested (as seen in Table 2) .

Decisions in Financial Economics Table 1 .

27

Mean Discount Rates by Scenario, Time, and Sum (%) .* Scenario A - Time

Subsample

Sum 200

I

600 5000 200

2

600 5000 200

3

600 5000 200

Total

600 5000

0 .5

2

Scenario B - Time 5

0 .5

2

5

35 .64 (1 .33) 23 .72 (1 .13) 19 .22 (0 .68)

20 .88 (0 .91) 15 .55 (0 .83) 13 .78 (0 .68)

15 .54 (0 .64) 12 .86 (0 .70) 10 .67 (0 .55)

22.62 (1 .14) 17 .90 (1 .09) 19.06 (0 .93)

15 .23 (0.68) 12 .22 (0.56) 12 .97 (0.67)

11 .67 (0.60) 9 .78 (0.63) 10.15 (0.63)

10 .98 (0 .97) 13 .24 (0 .72) 10 .41 (0 .65)

12 .57 (0 .59) 9 .95 (0 .55) 9 .11 (0 .53)

10 .79 (0 .59) 6 .38 (0 .75) 8 .48 (0 .59)

11 .47 (0 .69) 9 .75 (0 .60) 8 .25 (0 .74)

12 .87 (0.50) 9 .50 (0.65) 7 .75 (0.78)

9 .00 (0 .58) 7 .68 (0 .64) 7 .02 (0 .79)

15 .03 (0 .99) 11 .13 (0 .75) 9 .04 (0 .57)

9 .58 (0 .73) 8 .17 (0 .69) 7 .50 (0 .70)

9 .01 (0 .63) 7 .60 (0 .64) 6 .94 (0 .60)

6 .94 (0 .73) 5 .93 (0 .75) 6 .65 (0 .90)

6.69 (0.55) 6.14 (0.59) 5 .66 (0.55)

6 .52 (0 .48) 6 .02 (0 .57) 5 .80 (0 .52)

23 .22 (1 .10) 16 .03 (0 .87) 12 .91 (0 .63)

14 .34 (0 .74) 11 .22 (0 .69) 10 .13 (0 .64)

11 .78 (0 .62) 8 .95 (0 .70) 8 .70 (0 .58)

13 .68 (0 .85) 11 .19 (0 .81) 11 .32 (0 .86)

11 .60 (0.58) 9.29 (0.60) 8 .79 (0.67)

9 .06 (0 .55) 7 .83 (0 .61) 7 .66 (0 .65)

* Number in parenthesis is the coefficient of variation (cov) defined as the standard deviation over the mean . Scenario A represents postponing a receipt, while Scenario B represents postponing a payment .

The effect of the dollar sum involved on the inferred discount rates is demonstrated in both Tables 2 and 3 . Focusing first on the overall mean discount rate across time, scenario and subsample, the last line in Table 3 indicates that the effect of sum on discount rates is negative . The discount rates found are 13 .95%, 10 .75% and 9 .92% for dollar sums of $200, $600 and $5,000, respectively . Similar to the effect of time on discount rates, the effect of sum too was found to decrease at a diminishing rate . As seen in


28

Table 2.

Mean Discount Rates Across Scenario by Sum and Time (%) . Time

Subsample

Sum

0 .5

2

5

Mean

1

200 600 5000 Mean

29 .13 20 .81 19 .14 23 .03

18 .06 13 .86 13 .38 15 .11

13 .61 11 .32 10 .41 11 .78

20 .27 15 .33 14 .31 16 .64

2

200 600 5000 Mean

15 .22 11 .50 9 .33 12 .02

12.72 9.73 8 .43 10.29

9 .89 7 .03 7 .75 8 .22

12 .61 9 .42 8 .50 10 .18

3

200 600 5000 Mean

10.98 8 .53 7.87 9.13

8 .13 7 .16 6.58 7 .29

7 .76 6 .81 6 .37 6 .98

8 .96 7 .50 6 .94 7 .80

Total

200 600 5000 Mean

18.44 13 .61 12.11 14.73

12 .97 10.25 9 .46 10.90

10.42 8.39 8.18 8 .99

13 .95 10 .75 9 .92 11 .54

Table 3.

Mean Discount Rates Across Time by Sum and Scenario (%) .* Sum

Subsample

Scenario

200

600

5000

Mean

1

A B Mean

24.02 15 .61 20.27

17 .38 13 .30 15 .33

14.56 14.06 14.31

18 .65 14 .62 16 .64

2

A B Mean

14.10 11 .11 12.61

9 .86 8 .98 9 .42

9 .33 7 .67 8 .50

11 .10 9 .25 10 .18

3

A B Mean

11 .21 6.72 8 .96

8 .97 6 .03 7 .50

7 .84 6 .04 6 .94

9 .34 6 .26 7 .80

Total

A B Mean

16.44 11 .45 13 .05

12 .07 9 .44 10 .75

10.58 9 .26 9 .92

13 .03 10 .05 11 .54

* Scenario A represents postponing a receipt, while Scenario B represents postponing a payment.

29


the last line of Table 3, an increase in sum from $200 to $600 results in a reduction of about 3% in the discount rate, whereas an increase from $600 to $5,000 only results in a reduction of about 1% . Both the negative impact of sum on discount rate as well as its "diminishing-rate" property holds across all scenarios, time and subsamples tested (as seen in Tables 2 and 3) . Table 4 focuses on the scenario impact on discount rates . Scenario A represents postponing a receipt while scenario B represents postponing a payment . As indicated by the last column in Table 4, the overall mean discount rate is higher for Scenario A (13 .03%) than for Scenario B (10.05%) . As demonstrated by the other findings in Table 4, this scenario effect holds true across both time and sum, and for each of the three subsamples . As investigated shortly, risk factor and added compensation component may explain this scenario effect on discount rates . One of the more important issues examined in this study is whether formal education level and formal economics knowledge affect subjective discount rates derived from temporal decisions made by individuals . Accordingly, as discussed in the previous section, three subsamples were drawn : (1) undergraduates ; (2), graduates having a master's degree or higher mostly in Table 4.

Mean Discount Rates Across Sum by Time and Scenario (%).* Time

Subsample

Scenario

0 .5

2

5

Mean

1

A B Mean

26 .19 19 .86 23 .03

16 .74 13 .47 15 .11

13 .03 10 .54 11 .78

18.65 14 .62 16 .64

2

A B Mean

14 .21 9 .82 12 .02

10 .54 10 .04 10 .29

8 .85 7 .90 8 .22

11 .10 9 .25 10 .18

3

A B Mean

11 .75 6 .51 9 .13

8 .42 6 .16 7 .29

7 .85 6 .11 6 .98

9 .34 6 .26 7 .80

Total

A B Mean

17 .38 12 .06 14 .73

11 .90 9 .89 10 .90

9 .81 8 .18 8 .99

13 .03 10 .05 11 .54

* Scenario A represents postponing a receipt, while Scenario B represents postponing a payment .

30


disciplines other than economics ; and (3) graduates having a bachelor's degree mostly in economics .

The derived discount rates for the three subsamples appear in Tables

1-4 . As noted earlier, there is no difference between the three subsamples in terms of the three dimensions investigated : scenario, time and sum ; that is, for all three subsamples, discount rates decrease with the time delay and the sum of the cash flow . Also, the discount rates are lower for lending (Scenario B)

than for borrowing (Scenario A) . Furthermore, for all three subsamples, the effects of time and sum decrease at a diminishing rate . However, the three subsamples do differ with respect to the

level of the discount rate . As

demonstrated by Tables 1-4, and especially Figs 1 and 2, the level of the discount rates were found to be higher for the undergraduate Subsample 1 than

for the graduate Subsamples 2 and 3 . The overall mean discount rates across

scenario, sum, and time (as indicated by the last column of Table 2) are 16 .64%,

10 .18%, and 7 .80% for Subsamples 1, 2 and 3, respectively . The graduates

discount rates, compared with the undergraduates' rates, appear closer to actual

interest rates which prevailed in the Israeli capital market at the time the experiment was conducted . This result implies that graduates are more familiar

with market rates due probably to higher age, rates of employment and marital status . As a result, they invest more time in acquiring market financial data . The fact that Subsample 3 had even lower discount rates than Subsample 2 appears to indicate that the higher economics knowledge of Subsample 3 more

than offset the higher formal education (mostly in other disciplines than

economics) of Subsample 2 . In other words, the Economics graduates of Subsample 3 are more familiar with market rates and, therefore, their discount

rates are closer to actual rates than Subsample 2 . Indeed, the actual market interest rates on dollar savings and loans were found to be similar to Subsample

3's discount rates, particularly for the larger sum of $5,000 and the longer delay

time of 5 years .

Another interesting result indicated by Table 1 is the variation in the

coefficient of variation (COV) defined as the ratio of the standard deviation over the mean of the subjective discount rate . The findings in Table I demonstrate that not only the level of the mean discount rate but also its

coefficient of variation both decrease with the dimensions of time and sum,

and are lower for Scenario B than for Scenario A . It is also found to be lower for graduates (Subsamples 2 and 3) than for undergraduates (Subsample

1) . These results imply that : (1) the variability across subjects with

respect to their required discount rate decreases with both time and sum ; and (2) the graduate sample is more homogenous than the undergraduate sample in terms of their intertemporal decisions .

Fig. 1 .

Subsample 1

Subsample 2

Years

2

- Subsample 3

5

Mean Discount Rates Across Scenario and Sum by Time for Three Subsamples .

0 .5

,I,

Fig . 2 .

Subsample 1

Subsample 2

Sum($)

600

Subsample 3

5000

Mean Discount Rates Across Scenario and Time by Sum for Three Subsamples .

200


33

REGRESSION ANALYSIS The preceding analysis demonstrates the impact on derived discount rates of the three dimensions investigated : scenario, time and sum . Regression analysis reported below tests the hypotheses directly . The market segmentation approach goes beyond the classical approach by allowing the discount rate to vary across scenarios but implying no time or sum effects . In a regression form: R=bO C+b l T+b2 SA +b3 S B +e

(10)

where : R = discount rate ; C = cashflow sum ; T = time ; SA = Scenario A ; SB = Scenario B ; b's are the OLS coefficients, and e is the error term. By the segmentation hypothesis : b0 = b, = 0, and b2 > b3 > 0. The regression test results of Eq . (10) are presented in Table 5 . The findings re-establish the negative impact of both time and sum, and the positive impact of scenario . The level of relationship is relatively low, as indicated by R-squared, but statistically significant at 1% . Also, the impact of Scenario A (postponing-a-receipt) is higher than that of Scenario B (postponing-a-payment) . These findings confirm the existence of a scenario (segmentation) effect, but do not fully support the segmentation hypothesis, which asserts no further impact on discount rates such as that of time and sum . Table 5.

Regression Test Result of the Segmentation Hypothesis .* b0

Subsample 1 2 3 Total

(t) -0 .00001 (-2 .72) -0 .0006 (-3 .71) -0 .0003 (-2 .46) -0 .0006 (-4 .11)

bi

b,

b,

R2

F

(t)

(t)

(t)

(n)

(a)

-2.31 (-6.52) -0.726 (-3 .95) -0 .423 (-2 .98) -1 .302 (-7 .59)

26 .03 (18 .84) 14 .30 (19.59) 10 .98 (19 .31) 18 .26 (26.70)

21 .99 (15 .51) 11 .98 (16 .17) 7 .09 (13 .90) 15 .06 (21 .77)

0 .070 (810) 0 .075 (540) 0 .083 (540) 0 .053 (1890)

14 .2 (0.01) 10.2 (0.01) 12.1 (0.01) 25 .1 (0.01)

*The regression equation is given by Eq . (10) : R = b,C + b,T + b 2SA +b3 SB + e; where: R = discount rate ; C = cash flow sum ; T = time ; SA = Scenario A ; SB = Scenario B ; b's are the OLS coefficients; e is the error term ; R2 = R-squared; F = F statistic ; values in parentheses are t-statistic, number of observations (n), and the regression significance level ((x) . n is given by the product of the 2 scenarios, 3 time periods, 3 sums, and the subsample size, which is 45, 30 and 30 for Subsamples 1, 2, and 3, respectively .

34


As stated in the previous section, although the experiment is formulated in terms of certainty conditions, the subjects apparently frame it as involving some degree of risk . This may explain the relatively high discount rates found particularly for small sums, short time periods and a postponing-a-receipt scenario . The question is whether the subjects in our experiment incorporate risk in line with the one-period-realization (OPR) risk hypothesis, or the multi-period-realization (MPR) risk hypothesis . The regression form of both Eq. (4) for the OPR hypothesis, and Eq . (5) for the MPR hypothesis is : In (F/P)=b0 +b 1 T+e

(11)

where, as stated earlier, H o by the OPR is : b o =1n (l+d) ;b i = 1n (1+i) while Ho by the MPR is : b=0, and bi=ln[(l+d)(l+i)] Equation (11) was estimated seven times for both Scenario A and Scenario B, and for each of the three subsamples as well as for the total sample . The statistical results are summarized in Table 6 and they indicate first that the intercept (b0) is not statistically different from zero in all seven regressions . Both its relatively low level and absence of significance level give support to the MPR than the OPR in which, as stated before, the risk premium (d) is incorporated in the first period only, while by the MPR, it is incorporated in every single time period . The slope (b 1 ) by the OPR represents the risk-free rate of interest (i) where i = e bI -1, while, by the MPR, the slope represents the discount rate (R) that consists of both the risk premium (d) and the risk-free rate (i ) ; i .e . R = e bi-1 . The findings in Table 6 indicate that b i is positive and highly statistically significant . The mean value of the discount rate implied by the OLS b i coefficient, which is the risk-free rate (i) by the OPR and the risky rate (R ) by the MPR, is 8 .43% across the six regressions in Table 6, ranging between 11 .6% to 6 .18% . This mean value is also close to the 8 .33% value implied by b i for the total sample regression (e 'I -I = e 0 .08 -1 = 8 .33%). This mean value is slightly higher than the interest rate on dollar denominated bank savings in Israel at the time of the experiment . This mean value of 8 .33%, however, is quite close to the mean discount rate implied by the subjects' responses, particularly for high values of time and sum . As indicated by the last column


35

of Table 1 for the total sample, the mean (across subjects) discount rate for Scenario B is 9 .06%, 7 .83% and 7 .66% for sums of $200, $600 and $5,000, respectively . For Scenario A (postponing-a-receipt), the corresponding discount rate values are 11 .78%, 8 .95% and 8 .70% . Recalling the null hypothesis concerning b, by the OPR and the MPR, these findings in conjunction with values of both bo and b, (see Table 6) appear to lend more support to the MPR rather than the OPR hypothesis . To test the added compensation hypothesis (ACH), let us rewrite the formal relationship representing this hypothesis : In (F/P)=lnQ+In (1+R)T=b o +b 1 T

(12)

where Ho by the ACH as discussed earlier, is : bo = In Q, and b, = In (1 +R) Equivalently, Q = e h o, and R = e h '-1 . The regression results of Eq . (12) appear in Table 6, and they demonstrate that the added compensation component (Q) is positive but not statistically significant . The mean value across the first six regressions in Table 6 of the added compensation component (Q) implied by the bo values is 2 .96% (where Q = e h0), ranging between 0 .16% and 6 .18% . The Q value for the total sample (Line 7 in Table 6) is 3 .34% . Both the level of Q and its low significance level imply a weak existence of an added compensation component. This result is also consistent with the reasonable level of Table 6.

Regression Test Result of the OPR vs . the MPR Hypotheses .*

Subsample Scenario I 2 3 Total

A B A B A B

b0

t (b(,)

bi

t (b i )

R2

n

F

0.06 0 .05 0 .03 0 .02 0 .02 0 .02

0 .26 0 .26 0 .16 0 .12 0 .11 0 .02

0 .11 0 .09 0 .08 0 .07 0 .07 0 .06

16.10 16.80 15 .60 13 .40 14.70 18 .70

0 .40 0 .43 0 .48 0 .43 0 .45 0 .56

405 405 270 270 270 270

127 142 121 90 108 168

0 .03

0 .17

0 .08

34.10

0 .39

1890

579

* The regression equation is given by : In (F / P) = b0 + b i T + e ; where : F = future value; P = present value ; T = time ; b 0, b I = OLS coefficients ; e = error term . By the OPR : bo =1n (1 + d) ; b, =1n (1 + i), while by the MPR ; b„ = 0 ; b, =1n [(1 + d) (1 + i)], where: d = risk premium ; and i = risk-free rate of interest . Also t(b) = t-statistic; RZ = R-squared ; F = F statistic; and n = number of observations given by the product of 3 time periods, 3 sums, and the subsample size ; Scenario A represents postponing-a-receipt, while Scenario B represents postponing-a-payment .


36

derived discount rates found for our sample, particularly for high values of time and sum .

6. SUMMARY AND CONCLUSION This paper re-examined the behavior of subjective discount rates across three dimensions : financial scenario, time delay and the monetary sum of the cash

flow, and investigated the extent to which the behavior of subjective discount

rates is affected by the formal education level of the decision makers and their knowledge of financial economics .

The behavior of the subjective discount rates is examined in the context of

four hypotheses : the classical approach advocating universal uniformity of the discount rate ; the market segmentation approach allowing for scenario effect, but no time or sum effects ; the implicit risk approach stating that, due to mental

framing, a risk premium is inherent in the subjective discount rate, and the added compensation approach asserting that position change will be associated with an additional compensation .

Three subsamples of 105 subjects were drawn from undergraduates,

graduates, and those having a higher academic degree - all affiliated with the Technion and Haifa University . The proportion of economics majors was 59% and 30% in the second and third subsamples, respectively .

The pattern of the inferred subjective discount rates found in this experiment

is similar to what has been established previously in the experimental litera-

ture ; that is, the discount rates are decreasing with the time delay and the sum of the cash flow, and are higher for a postpone-a-receipt scenario than for a

postpone-a-payment scenario . The depressing effect of time is probably due, as noted by Benzion, Granot and Yagil (1992), to the subjects' misevaluation of

the exponential function . The negative impact of sum appears related to both

Loewenstein and Prelec's (1992) psychological perception concept, which

asserts that individuals are sensitive not only to relative but also to absolute differences between monetary sums, and to Thaler's (1987) mental accounting

concept . The scenario effect can be due to Kahneman and Tversky's (1992)

"loss aversion" concept, which implies that investors attach higher weights to losses than to gains .

The subjective discount rates, particularly for low values of time and sum,

were found to be much higher than the risk-free rate of interest, implying the existence of an implicit risk premium, which is also consistent with the loss

aversion concept . Furthermore, regression results lend more support to the multiple-period-realization (MPR) than to the one-period-realization (OPR)

hypothesis, particularly for a postponing-a-payment scenario in conjunction with


37

relatively high values of time and sum . For very low values of time and sum, however, the findings concerning the MPR and OPR are mixed . The findings also lend weak support for the added compensation hypothesis . The comparison of the three subsamples demonstrates that formal education and economics knowledge do not appear to have an impact on the pattern of subjective discount rates with respect to scenario, time and sum . In contrast to this dimensional similarity, the level of the discount rates was found to be lower for graduates than for undergraduates, and this lower rate appears closer to actual capital market interest rates . This finding implies that graduates are more familiar with market rates probably due to their age, rates of employment and marital status . As a result, they tend to invest more time in acquiring market financial data . The experimental findings also indicate that higher formal economics knowledge is associated with lower discount rates that are closer to capital market interest rates . This finding may also be related to the "familiarity" factor examined recently by Huberman (2001) . The overall results concerning the impact of scenario, time and sum, which are not perfectly consistent with established economic theories, may indicate possible links between emotions and economics (Elster, 1998 ; Loewenstein, 2000), and psychology and economics (Rabin, 1998) .

REFERENCES Barber, B . M ., & Odean, T . (2000) . Trading Is Hazardous to Your Wealth : The Common Stock Investment Performance of Individual Investors. The Journal of Finance, 55(2), 773-806 . Barber, B . M ., & Odean, T. (2001) . Boys Will Be Boys : Gender, Overconfidence, and Common Stock Investment . Quarterly Journal of Economics, 156(1), 261-292 . Barberis, N ., Shleifer, A ., & Vishny, R . (1998) . A Model of Investor Sentiment . Journal of Financial Economics, 49, 307-343 . Barberis, N ., & Huang, M . (2001). Mental Accounting, Loss Aversion and Individual Stock Returns . NBER working paper No . W1890 . Barberis, N ., Huang, M ., & Santos, T . (2001). Prospect Theory and Asset Prices . Quarterly Journal of Economics, 116, 1-53 . Benartzi, S ., & Thaler, R . (1995) . Myopic Loss Aversion and the Equity Premium Puzzle. Quarterly Journal of Economics, 110, 73-92 . Benartzi, S ., & Thaler, R . (1999). Risk Aversion or Myopia? Choices in Repeated Gambles and Retirement Investments . Management Science, 45, 364-381 . Benzion, U ., Rapoport, A ., & Yagil, J . (1989) . Discount Rates Inferred from Decisions : An experimental study . Management Science, 35, 270-284 . Benzion, U ., & Yagil, J . (1987) . On Discounting Formula for a stream of Independent Risky Cashflows . The Engineering Economist, 32, 337-345 . Benzion, U ., Granot, A ., & Yagil, J . (1992). The Valuation of the Exponential Function and Implications for Derived Interest Rates . Economics Letters, 38, 299-303 .

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Benzion, U ., Granot, A., & Yagil, J . (1994) . An Experimental Test of the IRP, PPP and Fisher Theorems . Journal of Economic Psychology, 15, 637-649 . Daniel, K ., Hirshleifer, D., & Subrahmanyam, A . (1998) . Investor Psychology and Security Market Under- and Overreactions . Journal of Finance, 53(6), 839-885 . De Bondt, W . F. M ., & Markhija, A . (1988) . Throwing Good Money After Bad? Journal of Economic Behavior and Organization, 10, 173-199. Elster, J . (1998). Emotions and Economic Theory. Journal of Economic Literature, 86, 47-74 . Fama, E . F. (1998) . Market Efficiency, Long-Term Returns, and Behavioural Finance . Journal of Financial Economics, 49, 283-306. Ferris, S . P ., Haugen, R . A ., & Makhija, A . K. (1988) . Predicting Contemporary Volume with Historical Volume at Differential Price Levels : Evidence Supporting the Disposition Effects . The Journal of Finance, 58(3), 677-697 . Gervais, S., & Odean, T . (2001) . Learning to be Overconfident . Review of Financial Studies, 14(1), 1-27 . Gneezy, U ., & Potters, J . (1997) . An Experiment On Risk Taking And Evaluation Periods . Quarterly Journal of Economics, 152(2), 631-645 . Gneezy, U ., & Rustichini, A . (2000) . Pay Enough or Don't Pay At All . Quarterly Journal of Economics, 155(3), 791-810. Hirshleifer, D . (2001) . Investor Psychology and Asset Pricing . Journal of Finance, 56(4), 1533-1597 . Huberman, G . (2001). Familiarity Breeds Investment. Review of Financial Studies, 14(3), 659-680 . Kahneman, D ., & Tversky, A . (1979) . Prospect Theory : An Analysis of Decision Under Risk . Econometrica, 47, 359-363 . Kahneman, D ., & Tversky, A. (1992) . Advances in Prospect Theory : Cumulative Representation of Uncertainty . Journal of Risk and Uncertainty, 5, 297-323 . Loewenstein, G . (1988). Frames of mind in intertemporal choice . Management Science, 34, 200-214 . Loewenstein, G ., & Prelec, D. (1992). Anomalies in Intertemporal Choice : Evidence and Interpretation . Quarterly Journal of Economics, 57(2), 573-598 . Loewenstein, G., & Thaler, R . (1989) Intertemporal Choice . Journal of Economic Perspective, 3(4), 181-193 . Loewenstein, G . (2000). Emotions in Economic Theory and Economic Behavior. American Economic Review, 90(2), 420-432 . Rabin, M . (1998) . Psychology and Economics . Journal of Economic Literature, 36, 11-46 . Shefrin, H., & Statman, M . (1993) . Behavioral Aspects of the Design and Marketing of Financial Products . Financial Management, 22(2), 123-134 . Shefrin, H ., & Statman, M . (1994). Behavioural Capital Asset Pricing Theory . Journal of Financial and Quantitative Analysis, 29(3), 323-349 . Thaler, R. H . (1981) . Some Empirical Evidence on Dynamic Inconsistency . Economics Letter, 8, 201-207 . Thaler, R . H . (1987) . Anomalies : Saving, Fungibility and Mental Accounts . Journal of Economic Perspective, 1, 97-201 . Thaler, R. H . (1994) . Psychology and Saving Policies . American Economic Review, 84(2), 186-192 . Thaler, R. H ., Tversky, A ., Kahneman, D ., & Schwartz, A . (1997) . The Effect of Myopia and Loss Aversion on Risk Taking : An Experimental Test . Quarterly Journal of Economics, 152(2), 647-661 . Tversky, A ., & Kahneman, D . (1986) . Rational Choice and the Framing of Decisions . Journal of Business, 59(4), 251-278 .


39

APPENDIX A Questionnaire on Problems in Financial Economics Decisions The purpose of the present experiment is to obtain some estimate of your preferences for paying or receiving an amount of money in the future instead of paying or receiving another amount in the present . The questions presented below have no unique answer . The answers may differ from one person to another according to his/her own investment preferences and his/her own financial position in the present and in the future . The questions you will be given are chosen from a wide spectrum of simple decision problems which can arise in different areas of one's economic behavior . This questionnaire is anonymous and for research purposes only . We ask you to use your best judgment, and to answer all questions sincerely and as best you can . (1)

Upon approaching the maturity date of your bank savings plan, the bank manger offers you the opportunity to reinvest the proceeds in a similar savings plan with a different maturity time . What is the amount of dollars the bank would have to offer you at the maturity date of the new savings plan, so that you will be completely indifferent about receiving the relevant sum today or receiving a larger sum in the future? Please state this future amount of dollars for each of the nine savings plans given in the table below (comprising of 3 different sums and 3 different times to maturity) . Dollar Amount You Require at the End of the Period Dollar Amount Today 0,5 a Year Hence $200 $600 $5,000

(2)

$ $ $

2 Years Hence $ $ $

5 Years Hence $ $ $

You need some amount of dollars today that your bank is willing to lend you . What is the amount of dollars you would be willing to pay back at the end of the period, so that you will be completely indifferent about receiving a certain amount today or paying back a larger amount in the future? Please state this future amount of dollars for each of the nine time and sum options given in the table below .

40

URI BENZION AND JOSEPH YAGIL Dollar Amount You Are Willing to Pay at the End of the Period Dollar Amount Today $200 $600 $5,000

(3)

0.5 a Year Hence $ $ $

2 Years Hence $ $ $

5 Years Hence $ $ $

Finally, please circle your highest academic degree earned : (a) Bachelor's degree (b) Master's degree or higher Discipline or field of study :

Thank you for your cooperation .

INSTITUTIONAL OWNERSHIP, INFORMATION AND LIQUIDITY William W . Jennings, Karen Schnatterly and Paul J . Seguin

ABSTRACT We examine relations between institutional ownership and quoted bid-ask spreads in general, and the adverse-selection component of the spread in particular. For our sample of Nasdaq stocks, we find that high institutional ownership leads to narrower spreads and spreads with a smaller proportion attributable to asymmetric information . Our results are not attributable to institutions' preferences for liquid securities . The relation between spreads and institutional ownership varies significantly across institutional type and is related to the largest block size held by an institution. Our evidence suggests that increases in institutional ownership reduce the costs of liquidity services by ameliorating informational asymmetries .

1 . INTRODUCTION One of the most visible and controversial trends in equity market evolution is the growth of institutional ownership and participation . Institutional ownership of equity represented only 5% of total equity ownership in 1945, but increased

Innovations in Investments and Corporate Finance, Volume 7, pages 41-71 . 2002 Published by Elsevier Science Ltd . ISBN : 0-7623-0897-4 41

42

WILLIAM W . JENNINGS, KAREN SCHNA I I LRLY AND PAUL J. SEGUIN

to 24% by 1965, and to 38% by 1981 . By 1993, over 50% of all equity was held by institutions .

Although this growth has been well documented, little consensus has arisen

concerning its impact on the capital markets' ability to supply immediacy and

liquidity services . Divergences in opinion about the impact of institutional growth on market liquidity can be traced primarily to differences in opinion concerning the impact of the growth of institutional ownership on the

informational environment. For example, Kothare and Laux (1995) argue

that institutions are "informed" and that institutional trades convey superior

information . As a result, an increase in institutional trading raises the costs to

market makers of supplying liquidity . Therefore, the price of liquidity, notably

the bid-ask spread, must rise . Theoretic models of strategic trading (including Admati & Pfleiderer, 1988) predict that the impact of ownership on liquidity

depends crucially on the degree of informativeness of the share owners - an

increase in the presence of uninformed, or "noise" traders, can actually increase the liquidity of a market . Under this paradigm, an increase in participation by

institutions that are, on average, informed relative to market makers, can be viewed as an increase in the demand for liquidity . In contrast, an increase in

participation by institutions that are, on average, less or equally informed

relative to market makers, can be viewed as an increase in the supply of liquidity .

Although liquidity would be affected by the extent to which institutions

themselves were informed, it is possible that the mere presence of institutional ownership, regardless of their degree of informativeness could impact the

informational environment indirectly . For example, Brennan and Subrahmanyam (1995) argue that increases in institutional ownership induces subsequent

changes in analyst following, thus affecting the informational environment . As a result, increases in institutional ownership may have no direct effect

on liquidity, but such increases may lead to greater liquidity indirectly by ameliorating any informational asymmetries .

The objective of this study is to examine the impact of the growth in institutional ownership and trading on the liquidity and immediacy in security

markets . To do so, our empirical approach is to examine cross-sectional and inter-temporal relationships between institutional ownership and bid-ask spreads .

This objective and approach are not novel to this study - at least five other

studies motivate their investigations and model their empirical tests along similar

lines . Fabozzi (1979) represents one of the first attempts to determine empirically those factors that affect spreads . His list of candidates included

institutional ownership . However, for his sample of 239 OTC issues measured

Institutional Ownership, Information and Liquidity

43

over February 1977, he found no statistical relation between institutional ownership and spreads . Chiang and Venkatesh (1988) examined 56 NYSE stocks during 1973, and, like Fabozzi, found no evidence of a relation between institutional ownership and spread width . Kothare and Laux (1995) examined Nasdaq spreads measured over Octobers of 1984, 1988 and 1992 . Using a variety of metrics for the intensity of institutional trading, they find mixed support for a relation between spread width and institutional activity - one specification of institutional trading intensity yielded insignificant results, while their second specification yielded a positive relation between institutional activity and spreads . They conclude that institutional trading is, on average, informed, and that the secular widening in spreads for Nasdaq issues can be partially attributed to the secular growth in institutional participation . Sarin, Shastri and Shastri (1996) reach similar conclusions . Using a sample of 786 NYSE and Amex listed securities during 1985, they find that spreads are positively related to the degree of institutional holdings . In comparison, Brennan and Subrahmanyam (1995) argue that the magnitude of the asymmetric information or adverse selection component of spreads is inversely related to the number of analysts following a stock . Further, they find that changes in institutional ownership of an issue during 1988 are positively related to subsequent changes in the number of analysts following that issue . They conclude that increases in institutional ownership have no direct effects on spreads, but that such increases narrow spreads indirectly by inducing increased analyst following, thus ameliorating the informational environment . In contrast to the relatively small data sets or narrow time frames examined in previous studies, our sample contains institutional holding and spread data over 35 quarters from the first quarter of 1983 through the third quarter of 1991 for Nasdaq-listed firms . Our final sample contains over fifty-thousand observations, so the statistical power of our tests greatly exceeds the power of previous tests . After controlling for numerous variables known to affect the width of the relative bid-ask spread, we find that the proportion of outstanding shares held by institutions is significantly related to relative spread . However, we find that the relation between institutional ownership and spread width is negative . Of course, this partial correlation is consistent with numerous, non-mutuallyexclusive, statistical interpretations . First, it is plausible that this correlation reflects the facts that institutions generally turn their portfolio over ("churn") at rates greater than do individuals and that investors with shorter investment horizons prefer more liquid securities . In other words, narrower spreads lead, or cause, increases in institutional ownership . Second, it is possible that changes in institutional ownership and spreads are jointly and

44

WILLIAM W . JENNINGS, KAREN SCHNATTERLY AND PAUL J . SEGUIN

simultaneously determined . For example, endogenous changes in the informa-

tional environment (e .g . news, regulatory changes, etc .) surrounding a firm may

simultaneously affect both institutional ownership and spreads . Finally, due to

the impact of institutional ownership on the informational environment, changes

in institutional investment could lead, or cause, changes in spreads . Such changes may be attributable to increased monitoring by the institution, its internal ("buy-side") analysts or external ("sell-side") analysts .

To disentangle these interpretations, we perform Granger-causation tests and

find causation in both directions . Finding that changes in spreads cause changes

in institutional ownership is neither surprising nor novel and simply reflects institutional preferences for more liquid securities . However, our finding that increases in institutional ownership "cause" or lead to subsequent decreases in

abnormal spreads is, to our knowledge, a new result, and indicates that our cross-sectional findings are not entirely attributable to institutional preferences

for more liquid securities . Instead, our evidence indicates that changes in the institutional ownership lead to changes in the informational environment surrounding the firm .

We next partition the quoted spread and find a relation between institutional

holdings and the adverse selection component of spreads . Specifically, the adverse selection proportion of the spread is lower for those issues with greater

institutional ownership . Thus, market makers price their services as if greater

institutional ownership mitigates informational asymmetries .

Additionally, we report two regularities concerning the relationship between

spreads and institutional ownership . First, when institutional holdings are

partitioned into the five institutional types, we find a heterogeneous relation

between institutional ownership and liquidity across the types . Specifically, we find that the negative relationship between institutional ownership and spreads

is strongest for commercial banks and insurance companies, while there is no discernible relationship between spreads and the holdings of pensions,

foundations, endowments and ESOPs . This heterogeneity is consistent with the notion that the impact on the informational environment of institutional ownership depends crucially on the nature of the institution. Second, when we separate institutional holdings into the largest block held by an institution and

the holdings of all other institutions, we find that the coefficient associated with

the largest block is positive and significant. This positive coefficient suggests that the presence of a large block exacerbates informational asymmetry .

The major implication of our results pertains to the secular growth in

institutional participation in the market . Specifically, since our evidence suggests

that cross-sectional and inter-temporal increases in institutional holdings are

associated with enhanced liquidity, we cannot support the claim that the secular


45

increase in institutional ownership represents a net drain on liquidity . Indeed, our results instead suggest that any attendant increases in the demand for liquidity or immediacy services are, on average, more than offset by some combination of an increase in the supply of liquidity services and an amelioration of informational asymmetries . As a result, we conclude that increases in institutional ownership should not be interpreted as threats to market cohesion . In the following section, we develop the major hypotheses of this paper and investigate links between institutional participation, informational asymmetries and the costs of providing liquidity services . Section 3 describes our sample . We present the results of our cross-sectional analysis in Section 4, and the results of our Granger-style tests in Section 5 . The relation between institutional ownership and the asymmetric information component of the spread is examined in Section 6 . The final section concludes and discusses some of the implications of our findings .

2. SPREADS, INFORMED TRADING AND INSTITUTIONAL OWNERSHIP The microstructure literature in general, and the bid-ask spread literature in particular, are replete with theoretic models and empirical evidence linking informed trading to the width of the bid-ask spread .' Many of these models share a common intuitive underpinning - assuming that competition (real or potential) - imposes a zero-profit constraint on a market-maker, the marketmaker sets a bid-ask spread that balances the gains from trading against uninformed traders with losses from trading against informed traders . Typically, a trader is deemed to be informed or uninformed based on whether the trader has "superior" information about the intrinsic value of the asset compared to the market-maker. Algebraically, and holding trade size (or quoted depth) constant,' the market maker sets a quote such that : [ 1-Pr { next trader is informed}] [Profit I next trader is uninformed] +Pr(next trader is informed) [Profit I next trader is informed] = 0 One key variable in this trade-off is the probability of whether the "next" trader, that is, the trader who exercises the market maker's written option of trading at either of the market maker's posted quotes, is informed . Typically, it is assumed that the profit - conditional on the next trader being uninformed - is positive, while the profit - conditional on the next trader being informed - is negative. As a result, the bid-ask spread, which is usually the "choice variable" in the

46


algorithm, is related to the probability that the next trader is informed . Simple calculus dictates that as this probability increases, the spread must widen .

Since informed traders engage in "stealth" trading, that is, they show

reluctance to announce their intentions to trade on superior information ex ante,

market makers cannot readily estimate the informed-trade probability directly . Instead, market makers must rely upon a set of observable variables from which they must estimate conditional probabilities . The primary objective of our study is to determine whether market makers include the proportion of shares held by institutional investors in their list of observable variables upon which they

condition their probabilities . To do so, we econometrically investigate whether there exists a discernible relation between quoted spreads, and hence the unobserved or implied probability that the next trader is informed, and the

ex ante proportion of shares held by institutions . In other words, we examine

whether the proportion of shares held by institutions is an informative proxy for the level of asymmetric information . Note that in most of our analyses, we include a number of proxies for the unobservable conditional probability previously identified in the finance literature, including firm size, share price,

number of market makers and past volume . As a result, our primary objective may be more precisely stated as determining whether the proportion of

shares held by institutions provides additional or marginal information after conditioning on an extensive list of alternative observable proxies .

There are numerous reasons why proportional institutional ownership could

proxy for the probability that the next trader is informed . For example, if institutional holdings as of the beginning of a period were correlated with the probability that a trade involved at least one institution during the period, then

the link between institutional ownership and quoted spreads might depend on

the market makers' perception of the information content of institutional trades .' If institutions are "informed" and institutional trades convey superior

information, then an increase in institutional trading raises the costs to market makers of supplying liquidity . Therefore, the price of liquidity, notably the

bid-ask spread, must rise . Alternatively, if institutions are "uninformed" and their trades convey little asymmetric information, then the growth of institutional ownership may be associated with a reduction in the average cost of supplying liquidity .

An additional alternative is one where institutions are only partially informed .

Under the assumption of partial information, theoretical work suggests that institutional activity can destabilize stock prices and create "excess" volatility,

either through herding, where institutions mimic each others' buying or selling decisions, or through positive feedback trading, where institutions buy winners and sell losers (see : Hirshleifer, 1995 ; Devenow & Welch, 1996) . However,


47

empirical evidence supporting these conjectures is sparse . Pound and Shiller (1987) find evidence of positive feedback trading among less risk averse institutional owners . Lakonishok, Shleifer and Vishny (1992) report only limited evidence for the presence of either herding or positive feedback trading . They argue that different pension funds pursue trading styles that are sufficiently different to offset each other . The proportion of shares held by institutions can affect the informational environment indirectly as well . For example, Brennan and Subrahmanyam (1995) argue that increases in institutional ownership lead to increases in analyst coverage. Further, since many institutions employ in-house ("buy side") analysts, increases in institutional ownership could lead to an increase in the competition for information production and evaluation . Regardless of the economic links underpinning the relation between institutional ownership and informational asymmetries, all that our interpretations require is that the proportion of shares held by institutions is positively correlated with the market maker's unobservable conditional probability that the next trader has superior information . If our proxy is poor, that is, the correlation is positive but "low," then the classic errors-in-variables problem is severe . Coefficients associated with this proxy will be biased towards zero, which reduces the power of our statistical tests . As a result, if we are unable to reject the null that our proxy has no explanatory power, we will be unable to determine whether the null is true or whether the proxy is uninformative . Though errors-in-variables adversely affects the power of tests, they do not impact test sizes . Thus, if we are able to reject the null, then we do so despite the errors-in-variables problem, not because of the problem . 4

3. DATA AND METHODS Our sample is comprised of quarterly firm observations for the thirty-five quarters from the first quarter of 1983 through the third quarter of 1991 . For a given quarter, our dataset contains the intersection of the set of firms listed on the Center for Research in Security Prices (CRSP) Nasdaq dataset and the set of firms with at least one institutional ownership stake large enough to warrant coverage by the Spectrum database . We limit our study to Nasdaq-listed securities for three reasons . First, time-series of daily data on bids and asks are more readily available for Nasdaqlisted firms due to CRSP reporting conventions . Second, although the introduction of newer datasets (ISSM, TORQ and TAQ) allow us to capture spreads for NYSE/Amex listed securities, these data sets cover only a fraction of the 35 quarters for which we have institutional ownership data . Our longer

48 WILLIAM W . JENNINGS, KAREN SCHNAI IhRLY AND PAUL J . SEGUIN sample horizon enables us to investigate the direction of causation between institutional ownership and bid-ask spreads, which previous studies have been unable to address . Finally, for Nasdaq listed firms, alternative trading venues and techniques are limited . For example, centralized limit-order trading, which could allow for institutional traders to trade within the quotes (Petersen & Fialkowski, 1994), is not currently feasible on the Nasdaq system . Further, the incidence of trading on regional exchanges or in an anonymous upstairs market like Instinet is virtually non-existent for the majority of firms in our sample . As a result, unlike NYSE or Amex listed securities, there is little or no distinction between the market environments in which individual versus institutional orders are filled, and posted quotes are more indicative of realized or effective spreads .' We use the CRSP daily master file and National Market System (NMS) supplemental file for return, volume, number of trades, price, shares outstanding and number of market makers data . End-of-quarter relative bid-ask spreads were computed as the ratio of spreads deflated by spread midpoints . We eliminated ten observations with negative spreads . (Later we use average spread over the quarter.) Our sample contains only Ordinary Common Shares (versus ADRs, LP units, etc .) of Nasdaq firms that have return data spanning the entire quarter including eight non-zero daily returns . Additionally, only firms that have end-of-quarter prices over $5 are included . We constructed variables measuring different dimensions of institutional ownership using data from the Spectrum files . These files, products of CDA Investment Technologies, contain quarterly information on institutional ownership of NYSE, Amex and Nasdaq listed stocks extracted from 13(f) filings with the Securities and Exchange Commission . These filings, mandated under the 1975 revision to the Securities Exchange Acts, collectively represent "a central depository of historical and current data about (institutional) investment activities," (Loss & Seligman, 1990, p . 2324) and "have become the single most important source of information available to the public about the holdings of major institutional investors" (Heard & Sherman, 1987, pp . 68-69) . Institutions with $100 million or more in accounts over which they exercise discretion are required to file quarterly reports with the SEC within forty-five days of the end of the calendar quarter . Institutions need not report a particular equity holding on the 13(f) if the holding is less than either 10,000 shares or $200,000 in principal and market value . As a result, such holdings may not be included in these data . For each firm and each quarter, Spectrum provides each institution's holdings . Note that observations with no Spectrum information are omitted at this point. If a firm is Nasdaq-listed for a particular quarter, but Spectrum contains no


49

synchronized record, then two possibilities exist . One alternative is that there are no non-exempt institutional holdings, so no 13(f) filings were recorded for that quarter . Under this alternative, institutional holdings should be coded as zero . The second alternative is that there are non-exempt holdings, but they are not recorded by Spectrum for inexplicable reasons .' Under this scenario, institutional holdings should be coded as a missing value . However, we know of no reliable algorithm for distinguishing between these scenarios . Therefore, we omit all such observations . The empirical conclusions we report below are unchanged, however, when we instead code all such observations as zero . Spectrum also records institutional type based on Standard and Poor's definition of the institution's primary line of business . Type 1 is made up of large bank holding companies such as the Bank of New York or Citibank . Type 2 is insurance companies . Type 3, investment companies and their advisors, is made up of mutual funds . Type 4, independent investment advisors, is made up of investment banks or other financial institutions whose primary area of business is not mutual fund management . Type 5, "other," consists of foundations, ESOPs, self-managed pensions and individuals who invest others' money who are not otherwise categorized . For each firm and each quarter we construct the proportion of a firm's outstanding shares held by all institutions, the proportion held by members of each of the five categories, and the proportions held by each of the five largest institutional investors . All proportions are expressed as a percent of shares outstanding . When we merge the Spectrum database with the CRSP database, 59,911 firm x quarter observations common to both remain . We then purge all observations with zero volume, fewer than two market makers, fewer than nine valid bid-to-bid returns, or where the shares outstanding reported in the two databases differed and could not be manually reconciled . This yielded a database with 50,533 observations . In Table 1, we report summary statistics for many of the key variables used in our analysis . For the observations in our sample, the mean and median proportion of shares outstanding held by institutions are 23% and 19%, respectively . Secular growth in institutional ownership appears in our sample of firms . As displayed in Fig . 1, cross-sectional average institutional holdings grew from a low of approximately 15% in the second quarter of 1983 to over 30% by the end of our sample period . Despite the selection bias inherent in using firms with non-zero institutional holdings only, our sample average lies below the cross-exchange average of about 45% . We attribute this discrepancy to the fact that we use only Nasdaq-listed securities . If, as is true within our sample, institutional

50

WILLIAM W . JENNINGS, KAREN SCHNAI IERLY AND PAUL J . SEGUIN

Table 1 . Variable Name

Mean

Market Equity Value 166 .19 (in $ millions) Relative spread (%) 3 .81 Lagged quarterly volume 2181 .88 (000's of shares) Number of market makers 10 .12 at beginning of quarter Return variance 5 .90 (bid-to-bid % z per day) Price per share 19 .37 Institutional holdings/ 23 .21 Shares outstanding (%) by type of institution commercial banks insurance companies investment companies indep . advisors others by institutional block size largest second largest third largest fourth largest fifth largest

Descriptive Statistics . Median

Standard Deviation

Minimum

65 .23

389 .52

1 .44

15,562 .01

2 .85 611 .29

3 .32 6567 .63

0 .11 0 .02

40 .00 192764 .88

8

6.76

2

Maximum

67

3 .35

12.50

0 .02

2027 .50

14 .75 19 .20

58 .67 18 .17

5 .03 < 0 .01

4850 .00 98 .74

6.39 1 .61 2.16 11 .76 1 .29

4 .08 0 .06 0 .47 8 .85 0.31

7 .25 3 .44 3 .76 11 .11 2 .81

0 0 0 0 0

85 .9 98 .63 81 .06 80 .58 94.05

6 .72 3 .33 2 .26 1 .68 1 .30

5 .43 2.96 1 .96 1 .45 1 .09

6 .31 2 .48 1 .79 1 .14 1 .19

< 0.01 0 0 0 0

98.63 41 .76 20.73 10.78 9.76

Note : Our sample is composed of 47,419 quarterly firm observations for the thirty-five quarters

from the first quarter of 1983 through the third quarter of 1991 based on the intersection of the set of firms listed on the CRSP-Nasdaq dataset, and the set of firms with at least one institutional ownership stake large enough to warrant coverage by the Spectrum database . End-of-quarter relative bid-ask spreads were computed as the ratio of spreads deflated by spread midpoints . Our sample contains only Ordinary Common Shares of Nasdaq firms that have return data spanning the entire quarter and prices over $5 . All proportions are expressed as a percent of shares outstanding .

ownership varies with firm size, then the institutional holdings for firms in our sample should be expected to be below aggregate averages . Consistent with this conjecture, the cross-sectional weighted-average proportion of shares held by institutions for our Nasdaq sample, with the market value of equity used as weights, exceeds 35% . Of the five institutional categories, independent investment advisors hold, on average, the greatest proportion of outstanding stock with 11 .8% . Bank holding

10%

15% -

30%

35% -

00

N 00

N 00

N 00

Date (YYQ)

N r 00

N 00 00

00

N

A

N O

Fig. 1 .

N

A A A

A % Institutional Ownership (left scale) - Relative Bid-Ask Spread (right scale)

N 00

A A A

A

•

-

2 .5%

3 .0%

- 3 .5%

- 4 .0%

- 4 .5%

- 5 .0%

- 5 .5%

- 6 .0%

m a

b ;

_a

`z

o'

o

.d •

O

A

O'

5

52

WILLIAM W . JENNINGS, KAREN SCHNAI IhRLY AND PAUL J. SEGUIN

companies are second with 6 .4% . The "other" category has the lowest mean with 1 .3% .

The mean and median holdings of the single largest institutional owner

represents 6 .7% and 5 .4% respectively, of the firm . As mentioned above, our sample selection criteria ensure that the minimum holdings of the largest

institutional holder cannot be zero . However, entries of zero for the second largest holding are feasible for those cases where a stock has but one institutional holder. The top five institutional owners combined hold an average of 15 .3% of each firm, which represents roughly two-thirds of all institutional holdings .

Aside from institutional ownership variables, the other key variable in our

analysis is the quoted bid-ask spread, expressed as a percent of the closing price

or bid-ask midpoint . For our sample, the mean relative spread is 3 .8% with a median of 2 .9% . The large discrepancy between the mean and median reflects a skewed distribution, which should not be surprising for a variable that is

truncated at zero . In Fig. 1, we also plot the cross-sectional average spread for each of the quarters in our sample . As in Kothare and Laux (1995), we find that mean spreads increase throughout our sample period .

Despite the temptation to do so, it is difficult to infer that the secular growth

in the two variables plotted in Fig . 1 are in any way related . Since both variables exhibit significant time-series persistence, measures of correlation are

severely biased (see Plosser & Schwert, 1978) . To circumvent this issue, our

empirical methods will concentrate on either cross-sectional variations in spreads and institutional ownership, or first-differences in these variables .

4. CROSS-SECTIONAL RESULTS We begin by examining the marginal impact of ownership structure on relative spreads . The method we employ to calculate statistical significance is loosely

based on the methods pioneered by Fama and MacBeth (1973) and Fama and

French (1992) . Specifically, for each of the 34 calendar quarters that our data

spans, we compute a separate cross-sectional regression, and then compute statistical significance using the time-series of the coefficient estimates . We use this method for two statistical reasons . First, standard errors from a pooled

time-series cross-sectional regression would be seriously understated due to the lack of independence between residuals for the same firm and adjoining

quarters . Second, as we demonstrated in Fig . 1, our two key variables, relative spread and institutional ownership are not mean-stationary . As a consequence, combining observations from the beginning and end of the sample period may result in parameter estimates that are biased .


53

Once a time-series of 34 parameter estimates is computed, we determine significance using a Fama-MacBeth-style test statistic which is computed as (µ,fri) / s where µ and s are the estimated weighted mean and weighted standard deviation of the series, n is the number of quarters used, and the weights are proportional to the number of observations available in a quarter . These tests statistics are reported in parentheses in Table 2 . Table 2 .

Institutional Ownership as a Determinants of Relative Spread . Model I

Intercept Shares Outstanding (x 10 t° ) Price Squared Price (x 106) Bid-to-Bid Return Variance Number of Market Makers at Beginning of Quarter Prior Quarter Volume (x 10 9) Percent held by Institutions Percent held by Banks Percent held by Insurance Co's . Percent held by Investment Co's . Percent held by Independent Advisors

8 .155 (25 .43) {13 .12) -2.237 (-9.74) {-5 .30) -0.106 (-9.89) {-4.98} 5 .637 (4.75) {2.44} 0.011 (3.12) {1 .76} -0.285 (-34.51) (-21 .26} 1 .292 (21 .04) 115.011

Model 2 8 .266 (26 .41) (13 .76) 2 .241 (-12 .00) (-7 .03) -0 .096 (-9 .51) (-4 .78} 5 .265 (4 .71) (2 .42) 0 .007 (2 .30) (1 .33) -0 .261 (-32 .54) (-19 .92} 1 .298 (19 .91) 114 .06) -2 .132 (-20 .84) (-11 .82}

Model 3

Model 4

8 .309 (26.29) {13 .70) -2.459 (-12.67) (-7 .47) -0.098 (-9.52) {-4.80} 5 .332 (4.72) 12.421 0.008 (2.56) (1 .45} -0.260 (-32.68) (-19.92) 1 .344 (20.21) (14.701

8 .065 (26 .08) (13 .52) -2 .190 (-13 .02) (-8 .12) -0 .093 (-9 .53) (-4 .80) 5 .081 (4 .71) 12 .42) 0.007 (2 .31) {1 .31) -0.249 (-32 .36) (-19 .73} 1 .310 (20.16) (14.59)

-1 .333 (-5.57) {-3.30) -1 .818 (-5.83) ( -3.95) -0.128 (-0.25) {-0.13) -3.130 (-27.59) 1-16.891

54

WILLIAM W . JENNINGS, KAREN SCHNA I I hRLY AND PAUL J . SEGUIN Table 2. Model I

Continued . Model 2

Percent held by Others

Model 3 -1 .410 (-2.78) 1-1 .66}

Percent held by Largest Institutional Owner Percent held by all other institutions Adjusted R2 from pooled regression

0.243

0.256

0.258

Model 4

1 .206 (5 .78) (3 .29) -3 .249 (-25 .48) 1-14 .11) 0 .261

In each specification, the dependent variable is end-of-quarter bid-ask spread deflated by the end-of-quarter bid-ask midpoint . Separate regressions are conducted each quarter . Parameter estimates represent the observation-weighted mean coefficient and are multiplied by 100, unless otherwise noted. Fama-MacBeth t-statistics for the weighted mean quarterly coefficient are reported in parentheses, and Fama-MacBeth t-statistics adjusted for second-order autocorrelation are reported in braces. Note :

Despite our use of Fama-MacBeth-style estimates, rudimentary time-series analysis suggests that estimates of the coefficients are autocorrelated out to lag two . As a result, we also report test-statistics that employ a standard error which accommodates first- and second-order autocorrelation in the estimates :

s

1

n+

2(n-1) 2(n-2) n2 P1 + n 2 P2

These test-statistics are reported in braces in Table 2 . The first specification we consider is our "baseline" model . The independent variables included in this specification are frequently employed in cross-sectional models of the bid-ask spread . Price per share and price-per-share squared are included to capture the often documented regularity that proportional spreads decrease with price, but at a decreasing rate . Coefficients associated with these two variables should, therefore, be negative and positive respectively . Previous literature suggests that spreads are composed of three components a pure transactions cost that covers the dealer's fixed and variable costs,


55

compensation for holding an inventory, and compensation for trading against traders with potentially superior information . Existing research also suggests that the variance of returns is positively related to the inventory risk borne by dealers . Since inventories are not well-diversified portfolios, increases in variances are associated with higher levels of risk borne by market makers . Assuming risk aversion, higher risk commands greater compensation in the form of wider bid-ask spreads . To accommodate this relation, we compute estimates of the variance of daily bid-to-bid returns . The use of bid-to-bid returns avoids overstating variance due to bid-ask bounce . We also include the number of market makers to proxy for the degree of competition in making a market for a particular stock . The relation between the width of the bid-ask spread and trading volume is complex . Recent theoretical and empirical studies of the volume-volatility relation demonstrate the importance of dichotomizing volume into an expected or anticipated component and an unexpected or "shock" component .' We argue that the relation between spread width and expected volume is likely negative ; if anticipated volume is larger, then the number of trades over which a market maker can expect to amortize the fixed cost component increases, reducing the per trade cost. In contrast, we, like numerous previous studies, argue that unexpected volume is associated with the amount of information arriving . Thus, higher unexpected volume indicates a greater amount of information which increases both the asymmetric information component and inventory risk component of the spread . We present estimates of our baseline model in the first column of Table 2 . All coefficient estimates are significant and have the expected sign - price, shares outstanding and the number of market makers are all negative, while the squared price and variance are positive . Consistent with the joint hypothesis that : (i) in our specification, the coefficient captures the covariance between spreads and only the unexpected component of volume ; and (ii) unexpected volume proxies for the flow of information, the estimated coefficient associated with volume is positive and significant .' Numerous studies find a negative association between volume and spreads, contrary to our findings . However, in these previous studies, measures of firm size are generally excluded from regression specifications . To determine whether our results are due to differences in the regression specification employed (or, in particular, due to the inclusion of alternative measures of firm size), or whether are results reflect a fundamental difference in the underlying market structure, we estimate specifications that include volume but exclude measures that are correlated with firm size . In each specification, which are not reported here, the coefficient associated with volume is, consistent with previous studies, negative .

56

WILLIAM W. JENNINGS, KAREN SCHNA I I hRLY AND PAUL J . SEGUIN 4.1 . Effects of Aggregate Institutional Holdings

In the second specification, we add the percent of shares outstanding held by

institutions, or more precisely, the shares held by institutions as captured and

reported by Spectrum deflated by the end-of-quarter shares outstanding. For those firm-quarters where Spectrum reports no institutional holdings, the

observation is treated as missing . This coefficient is negative and significant, both econometrically and economically .

To measure the economic impact, consider that the estimated coefficient

associated with institutional ownership (-2 .132) is roughly 56% of the mean spread, and 75% of the median spread in our sample . Using this estimate and substituting the means of the remaining variables into this equation, we can

infer that a firm with no institutional ownership would be expected to have a

relative bid-ask spread that was 4.45%, while a firm that was entirely owned by institutions would have a relative spread of only 2 .32% . Further, a onestandard-deviation change in institutional ownership (18%) alters the spread by

about 0 .38%, which is 10% of the sample mean spread . Thus, we conclude that

the structure of institutional ownership has as important an impact on spreads

as many commonly employed determinants .

Our primary focus is not on institutional ownership, per se, but on using

institutional ownership as a proxy for market makers' conditional expectation

of the informativeness of trading . Our results are, in this context, consistent with the hypothesis that market makers believe that institutional ownership is

a useful proxy for the degree of asymmetric information in the marketplace . Further, this variable provides explanatory power, even when numerous

alternative proxies for the informational environment are included in the specification .

4.2 . Tests of Robustness Our empirical results are robust to numerous alternative specifications . We have estimated these relations using the natural log of relative spreads, spreads

averaged over the entire quarter, turnover, closing transaction price variance

and market capitalization . We have also replaced price and its square with their inverses (Harris, 1994), replaced volume with number of trades (Jones, Kaul & Lipson, 1993), included squared volume, estimated the specification using

only those observations associated with raw spreads in excess of one tick, and estimated the specification using only those observations where the market value

of outstanding equity exceeds one billion dollars . We have also accommodated potential cross-sectional heteroskedasticity, both by calculating t-statistics using


57

White (1980) heteroskedasticity-consistent standard errors, which provide consistent standard errors for coefficients that are consistent yet inefficient, and using an iterative weighted-least-squares procedure that provides coefficient and standard error estimates that are both consistent and efficient (see Davidian & Carroll, 1987) . In these tests of robustness, modified standard errors were calculated using a pooled time-series cross-sectional specification . In no case is economic or econometric significance affected . Finally, to test whether the relation between spread and institutional ownership is negative throughout the domain of institutional ownership, we replace the continuous institutional ownership variable with a set of ten limited independent variables : 10(i) _- max . {0, min. {% Instit . Ownership - 10 , . l} } for i = 0, . . ., 9 . For example, 10(3) equals zero for all observations where the percent held by institutions is less than or equal to 30%, equals one for all observations exceeding 40% and equals the difference between the proportion and 30% for all other observations . Employing this set of variables ensures a continuous, piece-wise linear specification . Surprisingly, the coefficient associated with 10(8) is significantly positive, suggesting that increases in institutional ownership from 80% through to 90% are associated with wider spreads . Care should be taken in interpreting these results as only 251 firm-quarter observations, or < 0 .5% of all observations, have in excess of 80% institutional ownership . 4.3. The Role of Institution Type Previous research on the impacts of institutional ownership treat institutions as homogenous investors with identical reporting and performance metrics, underlying investors, access to information and trading practices . 9 However, it is possible that the impact of institutional ownership on the information environment varies across the different types of institutions . To investigate this possibility, we partition the percent of outstanding shares held by all institutions into percents of outstanding shares held by each of the five institutional types . By construction, the sum of these five proportions equals the proportion held by institutions employed in the previous specification . Results from this estimation appear in the third column of Table 2 . Despite an only modest increase in the model fit, we still reject the hypothesis that the coefficients associated with each of the five institution types are equal

58

WILLIAM W . JENNINGS, KAREN SCHNAI IERLY AND PAUL J . SEGUIN

(F = 12 .807 > F4165,0 .01 = 3 .435) . Interpreting these coefficients as estimates of market-makers' perceptions of the relative impact of institutional ownership on the information environment, the estimates suggest increased ownership by

independent advisors has the largest effect on the informational environment .

In contrast, changes in ownership by investment companies appear to have little impact on the degree of informational asymmetry .

4.4 . The Role of Institution Blocks Aside from the nature of the institution, it is also plausible that informativeness would vary with the size of individual institutional blocks . For example, it is frequently argued that holders of large blocks have incentives to collect firm-specific information and monitor a firm since the benefits are linear in

the size of the stake, while the costs of monitoring contain a large fixed component . 10 To investigate this possibility, we partition the percent of outstanding shares held by all institutions into the percent of outstanding shares

held by the single largest institutional owner and the percent of outstanding

shares held by all other institutional owners . By construction, the sum of these

proportions equals the proportion held by institutions employed in the second specification .

The results from estimating the specification with the largest institutional

owner separated appear in the fourth column of Table 2 . The coefficient associated with the holdings of all but the largest holder is significantly

negative, and is more negative than any previous estimate . In contrast, the

coefficient associated with the largest block holder is positive and significant . We reject the hypothesis that the coefficients associated with the two measures

of institutional holdings are equal (F = 31 .392 > F166001 = 7 .035) . We again interpret these coefficients as estimates of market-makers' perceptions of the

relative impact of institutional ownership on the information environment . The

estimates suggest that market makers respond to increases in the size of the largest institutional block by widening spreads . This finding is at least

consistent with the joint hypothesis that the largest block holder has particular incentives to collect information and monitor, and that market makers believe that this information collection may give these largest block holders an

informational superiority . Although not reported, we estimated coefficients

associated with each of the five largest block holders . Results were unchanged

-

the coefficient associated with the largest block holder was positive

and significant, while coefficients for each of the other five block holders were negative and significant .


59

5. CAUSATION As we mention above, a finding of a significant relation between institutional ownership and bid-ask spreads in the cross-section can be subjected to numerous interpretations . For example, one such interpretation is that we are documenting institutional preferences for more liquid securities . Under this scenario, our regressions are detecting a correlation between institutional ownership and spreads but our model is misspecified and we are unwittingly running a reverse regression . We agree that institutions prefer more liquid assets, but also believe that this preference does not completely explain our results . In our specifications, we include numerous other determinants of the bid-ask spread along with institutional ownership . Thus, the correlation between spreads and institutional ownership is a partial one . Under the alternative interpretation of our results, this correlation cannot be interpreted as evidence that institutions hold only those securities with narrower spreads, but evidence that institutions hold only those issues with spreads that are abnormally low . Thus, institutions would have to be concerned with spreads that were unusually low after conditioning upon some common and important determinants of spreads, including price, market maker participation, volume and volatility . Although we feel that this behavior is unlikely, we nonetheless proceed by investigating the direction of causation using a modification of the techniques of Granger (1969) . Specifically, we want to determine whether institutional ownership Granger-causes bid-ask spreads . Affirmative results from our Granger-causation tests are consistent with, but, admittedly, do not represent definitive proof that changes in institutional investment cause, in some economic sense, changes in spread . Instead, we provide these tests as additional evidence that the two are linked and that changes in institutional ownership contain information (in a statistical sense) about subsequent changes in liquidity . It is important to first control for changes in those other factors known to affect bid-ask spreads . To purge the effects of these other factors, we employ the residuals from the first specification reported in Table 2 . Thus, we are testing whether changes in institutional ownership Granger-cause changes in abnormal spreads . The first issue in performing a Granger-causation test involves determining whether the test should be performed in levels or in differences, which depends crucially upon whether the variables of interest are integrated . To determine the order of these processes, we perform Dickey-Fuller and Augmented DickeyFuller tests for co-integration with one and three lags for both the institutional

WILLIAM W . JENNINGS, KAREN SCHNAI l1 RLY AND PAUL J . SEGUIN

60

Table 3.

Tests of Unit Roots and Granger-Causality . (Augmented) Dickey-Fuller Unit Root Tests Panel A : Abnormal Relative Spread

AY, =

W

Yr-,

AY, -,

0.056 (5 .66) 0.090 (8.71) 0.167

-31 .239 (-87 .99) -22 .332 (-52 .07) -15 .303

(12.04)

(-22 .94)

AY,

2

Obs .

AY,-3

47337 -25 .620 (-48.74) -38.040 (-42.14)

38323 -20 .024 (-21 .83)

-10.233 (-12.42)

19254

Panel B : Institutional Ownership xc 1 .072

(33 .43) 1 .010 (27 .38) 0 .964 (16 .53)

1

Ax, ~

Ax, 2

Obs .

-1 .885 (-17 .14) -1 .812 (-15 .04) -1 .878 (-11 .12)

47337 -0 .734 (-1 .38) -2 .130 (-2 .74)

38325 -0.118 (-0 .15)

0 .802 (1 .01)

19258

Granger-Causality Tests Panel C: Abnormal Relative Spread with Changes in Percentage Institutional Ownership Ay, =

W

Y,-,

0.066 (6.74) 0.098 (9.51)

0.172 (12.31)

AY,-,

-31 .349 (-88 .28) -22 .430 (-52.31) -15 .342

-25 .623 (-48 .78) -38 .076

(-23 .00)

(-42.19)

Ayt 2

-20 .053 (-21 .87)

Ay, a

Ax,

Obs .

-1 .740 (-7 .69) -1 .651 (-6 .94) -1 .102 (-3 .50)

47337

Ax,_

Obs .

-1 .163 (-4.59) -1 .123 (-4.57) -1 .248 (-3 .70)

38325

-10 .263 (-12 .46)

38325 19258

Panel D : Abnormal Relative Spread with Lagged Changes in Percentage Institutional Ownership Ayt=

W 0 .049 (4 .64) 0 .096 (9 .30) 0 .173 (12.39)

Y,-, -31 .426 (-78 .68) -22.430 (-52.25) -15 .378 (-23 .05)

AY,-,

-25.612 (-48 .74) -38 .066 (-42.18)

Ay,-2

-20 .068 (-21 .89)

AY,-3

-10 .261 (-12.46)

38325 19258

Institutional Ownership, Information and Liquidity Table 3 .

61

Continued .

Note: y = Abnormal Relative Spread ; and x = Percentage Institutional Ownership . Abnormal Relative Spread is the residual from a regression of the end-of-quarter relative spread [(Ask-Bid) / Price] on shares outstanding, price, price squared, bid-to-bid return variance, volume and number of market makers . Price is defined as the average of bid and ask . Percentage Institutional Ownership is shares held by institutional owners divided by shares outstanding . For both the independent and dependent variables, the 0 operator indicates changes over the approximately 62 trading days of the indicated quarter . Parameter coefficients have been multiplied by 100 .

ownership and the abnormal spread variables . The results are presented in the top panels of Table 3 . Regardless of the form of the test, estimated coefficients on the lagged dependent variable are significantly below one . Thus, the presence of a unit root is rejected for both series, suggesting that levels are appropriate. However, first order autocorrelations are close to one, so adjustments for autocorrelations are required . In subsequent panels of Table 3, we test for whether institutional ownership Granger-causes abnormal bid-ask spreads ; that is, we test whether, conditional on the past values of abnormal bid-ask spreads, past values of institutional ownership affect current values of abnormal spreads . To control for the past values of abnormal spreads, we start with the Dickey-Fuller and Augmented Dickey-Fuller specifications used to test for the presence of a unit root in the abnormal bid-ask spread process . We retain these specifications for two reasons . First, by retaining lagged levels and differences in the abnormal spreads, we accommodate its autocorrelation structure . Second, by including an intercept and lagged values, this specification explicitly accommodates secular trends in both institutional ownership and bid-ask spreads . Since we use abnormal spreads throughout, the mean of the spread variable and its differences equal zero by construction . Therefore, the intercept reflects the mean of the first difference in institutional ownership . As a result, our estimates capture the relations between changes in institutional ownership and bid-ask spreads which are deviations from each variable's long-run trend . We then augment this specification by adding past changes in institutional ownership . As a robustness check, we estimate and report specifications using two measures of changes in institutional ownership - the contemporaneous change in institutional ownership and the lagged change in institutional ownership ." The evidence reported in Panels C and D of Table 3 indicate that, regardless of the choice of the institutional ownership variable, changes in

62 WILLIAM W. JENNINGS, KAREN SCHNAI IERLY AND PAUL J . SEGUIN institutional ownership Granger-causes changes in spreads . Across the three Dickey-Fuller specifications and two choices of institutional ownership

variables, all six coefficients associated with changes in institutional ownership are significant at the 1 % level . These results suggest that market makers adjust spread widths in reaction or response to changes in the ownership structure of the stock . Although we have not reported the results, tests of whether changes in spreads Granger-cause changes in institutional ownership were also performed. Results were mixed and were highly specification specific . Nonetheless, the few specifications that were significant support the hypothesis that at least part of the contemporaneous correlation between spreads and institutional ownership is attributable to institutional preference for liquid securities . In other words, these tests offer support for the non-mutually-exclusive hypothesis that institutional ownership reacts to changes in liquidity . Such results provide support for Amihud and Mendelson

(1986) who conjecture that agents with shorter expected holding periods have a relative preference for more liquid securities . Given that institutions churn their portfolio at rates greater than do individuals, this finding is not surprising . We wish to emphasize that Granger-causality is a uni-directional test and Granger-causality does not require the absence of reverse Granger-causality . As

a result, even stronger results of bi-directional Granger-causation would not diminish the impact of our primary finding - that changes in institutional ownership are useful for forecasting both changes and levels of abnormal relative spread .

6. THE ADVERSE SELECTION COMPONENT OF SPREADS To this stage, we have demonstrated that larger institutional ownership is associated with narrower bid-ask spreads and have inferred that this is due to a relation between institutional ownership and the informational environment . However, information content or adverse selection represents only one portion of the bid-ask spread . Therefore, to determine whether the negative relation between spreads and institutional ownership is attributable to adverse selection, we focus on the adverse selection component of the spread . Specifically, we adapt the methods introduced by George, Kaul and Nimalendran ("GKN," hereafter, 1991) to both estimate the adverse selection component and test whether this component varies systematically in the cross-section . In their study,


63

GKN estimated the overall level of adverse selection and examined whether adverse selection varies with size . In contrast, we examine whether that component varies with the percent of stock held by institutions . Following GKN, for each firm and each quarter, we calculate the raw bid-ask spread (measured either as the final spread or the average spread over the quarter), and a measure of the "true" spread :

2N, -Cov(Ret transe

t - Retbid . , Re ttrans-t

I

-Retbid. t- ) I

where : Rettrans and Retbid are returns constructed from last-transaction prices and closing bids respectively . Because the GKN method requires valid transactions prices as well as bid quotes, we restrict our sample to Nasdaq National Market System (NMS) firms when testing for the adverse selection component . CRSP's NMS Supplemental dataset provides data for our sample period with the exception of February 1986 . Accordingly, we eliminate the first quarter of 1986 when studying adverse selection . These two restrictions reduce our data to 19,568 firm-quarter observations . Next, a cross-sectional regression is estimated with the "true" spread as the dependent variable and the raw spread as the independent variable for each quarter . An estimate of the proportion of the spread that is attributable to adverse selection is computed by subtracting the slope coefficient from one . Finally, standard errors and significance levels are determined using the empirical distribution of the time series of cross-sectional estimates . For our sample period, we have a time-series of 33 quarterly estimates of the adverse selection component . To determine whether the adverse selection component varies with firm size, GKN partitioned each cross-sectional sample into size-based portfolios and estimated the coefficient separately for each portfolio . In contrast, we estimate a single cross-sectional regression for each quarter, but allow the estimated adverse selection component to vary across institutional ownership quintiles by including four indicator variables . Thus, we directly estimate the adverse selection component of the quintile with the smallest institutional ownership, and infer the adverse selection components of the other quintiles by adding the estimate of the associated indicator variable . One benefit of this approach is that it allows us to make statistical inferences . Specifically, we test whether the actual proportion of indicator variables exceeds the null of 0 .5 using a one-sided binomial test .

64

WILLIAM W. JENNINGS, KAREN SCHNATTERLY AND PAUL J . SEGUIN

Table 4.

Adverse Selection Component of Bid-Ask Spread and Institutional Ownership Levels . Panel A : Percent of Spread Attributable to Adverse Selection by Institutional Ownership Quintile

Institutional Ownership Quintile Smallest 1O-2 1O-3 1O-4 Largest

Percent of Spread Attributable to Adverse Selection ; Ir = (1-/3)

t-statistic for Hr : a = I

0.54 0.47 0.50 0.47 0.45

(3 .12) (3 .89) (3 .28) (3 .37) (3 .10)

Empirical distribution ; p

Binomial t-statistic for H0 : p = 0 .5

0 .76 0 .72 0 .74 0 .86

(2 .98) (2 .51) (2 .81) (4.15)

Panel B : Percent of Spread Attributable to Adverse Selection by Institutional Ownership Quintile & Calendar Year Quarter Institutional Ownership Quintile Smallest 1O-2 1O-3 1O-4 Largest

Quarter 1 (Jan.- March)

Quarter 2 (Apr.- June)

Quarter 3 (July - Sept.)

Quarter 4 (Oct.-Dec.)

0.54 0.44 0.49 0 .45 0 .42

0 .55 0 .50 0 .51 0.49 0 .49

0 .58 0 .51 0 .53 0 .50 0 .46

0 .48 0 .42 0 .46 0 .43 0 .41

Note: Following George, Kaul, & Nimalendran (1991), we estimate the adverse selection component of bid-ask spread as it = (1-/3) where /3 is taken from : 2,i-Cov (Ret«.,`, , -Reteid . " Ret( , -, t - i - Retn,, , , i ) = a + /3 Spread, + e, where: Ret„ e,,, and Retb ;d are returns constructed from last-transaction prices and closing bids respectively. Regressions were conducted quarterly within institutional ownership quintiles ; reported results are observation-weighted averages . The empirical distribution is the weighted fraction of periods when the estimated err for a quintile is less than -rr for the smallest quintile that quarter . Data for the table is an NMS-only subset of our general data .

6.1 . Adverse Selection Estimates Estimates of the adverse selection component and t-statistics for the quintiles appear in Table 4 . First, we note that our estimates of the adverse selection


65

component are roughly 50% which is consistent with estimates reported by Ho and Stoll (1989), but which are four times larger than those reported in GKN . 12 Of pertinence to this study, however, is the relation between the coefficient estimates . The results are at least consistent with the existence of a relation between the proportion of the spread attributable to adverse selection and the proportion of shares held by insiders . The four quintiles with larger institutional ownership all have lower estimates of the adverse selection proportion and every difference is significantly negative using the binomial t-statistic . The difference in the adverse selection component of the spread across the quintiles is accentuated when the adverse selection component is expressed as a fraction of the share price, rather than expressed as a proportion of the spread . The results in this section suggest that the proportion of the spread attributable to adverse selection is lowest for those firms with the highest levels of institutional ownership . However, as documented above, institutional ownership is negatively related to relative spread . Thus, those firms in the quintile containing the highest institutional ownership also have the narrowest relative spreads . Therefore, as institutional ownership increases in the cross-section, spreads narrow and the proportion of those spreads attributable to adverse selection also narrows . 6.2 . Seasonalities

Non-information based trading, including "window-dressing" or tax-related trading, may contribute to seasonalities in general, and to a reduction in the degree of adverse selection in the fourth quarter in particular . Because our adaptation of the GKN method relies on quarterly cross-sectional regressions, we can readily examine whether there are quarterly seasonalities in the adverse selection component of spreads . As above, we compute inter-temporal averages of estimates of the adverse selection component by institutional ownership quintile . However, in Panel B of Table 4, separate averages are calculated for each of the four calendar quarters . Consistent with an increase in noninformation-based trading around the calendar year end, the proportion of spreads associated with adverse selection are lowest in the fourth quarter and second lowest in the first quarter for each of the five institutional ownership quintiles . Despite these seasonal fluctuations, our conclusions concerning the relation between institutional ownership and adverse selection continue to hold . Across each of the four quarters, estimates of the proportion of the spread attributable to adverse selection are greatest for the quintile containing the lowest levels of institutional holdings, and smallest for the quintile containing the highest levels .

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WILLIAM W. JENNINGS, KAREN SCHNAI I'ERLY AND PAUL J . SEGUIN

7. CONCLUSION AND DISCUSSION The recent growth of institutional share ownership has led many to question whether capital markets can support the attendant increased demand for liquidity . In this study, we empirically examine whether changes in institutional

ownership, both inter-temporally and cross-sectionally, adversely affect one measure of the cost of providing liquidity services - the quoted bid-ask spread . Our results indicate that spreads are, in fact, correlated with levels in institutional ownership, but the correlation is uniformly significantly negative . Further, we provide evidence that changes in institutional ownership Granger-cause changes in spreads, so our results are not simply reflecting an institutional preference for liquid securities . We also perform tests using the adverse selection component of the bid-ask spread . We find evidence that the proportion of the spread attributable to adverse selection declines as institutional ownership increases . These findings have serious implications for the debate surrounding the desirability of institutional ownership . Specifically, in contrast to academic and popular perceptions, there is no evidence that the secular growth of institutional ownership is taxing the supply of liquidity in the equity markets . Indeed, our results suggest that an increase in institutional ownership, across time and/or across firms actually reduces the costs of liquidity services . Although our methods allow us to evaluate the relations between institutional holdings and liquidity, they are not designed to identify the economic link or links underpinning this relationship . Nonetheless, we would like to propose some candidates . First, our findings are consistent with the joint hypothesis that: (i) the beginning-of-quarter institutional ownership is related to the market maker's conditional probability that the next trader is an institution ; and (ii) that institutional trades convey less asymmetric information than non-institutional trades . One obvious possibility, consistent with semi-strong form market efficiency, is that the professional managers who instigate the trades are unsuccessful at collecting or using superior information . Under this scenario, it is these managers who provide liquidity to the markets . An alternative scenario is that the professional managers of these institutions do collect superior information and that a fraction of institutional trades are based on this superior information . However, on average, the proportion of trades based on information is overwhelmed by the proportion of trades that managers must make to meet the liquidity needs of their clients . For example, it is conceivable that for every discretionary trade made by a manager based on information, there are many trades that the


67

manager must execute solely to accommodate both discretionary and non-discretionary cash inflows to and outflows from the fund under management . The decisions that determine the direction and size of these cash flows are not made by the professional managers but are made by managers' clients . In many cases, these clients are individual traders . Under this scenario, it is the clients of these institutions who are the uninformed investors whose decisions convey little information about intrinsic value and merely provide liquidity . A second avenue through which institutional ownership can impact liquidity is through concomitant changes in information collection and processing . Following Brennan and Subrahmanyam (1995) it is plausible that increases in institutional ownership have no direct effects on spreads, but that such increases narrow spreads indirectly by inducing increased analyst following, thus ameliorating the informational environment. Under this scenario, increased institutional ownership actually creates a positive externality for small, uninformed investors . If increased institutional holdings lead to expanded information gathering, a reduction in informational asymmetries, and a reduction in the adverse selection cost of making a market, then the resulting declines in spreads benefit all potential traders . Regardless of the link underpinning this relationship, the results of the study

clearly indicate that increased institutional ownership does not represent a serious threat to the aggregate liquidity of the equity markets . Although increases in institutional ownership across firms or across time may have detrimental effects on demand for liquidity services, associated benefits are sufficient to more than offset them .

NOTES 1 . As a partial list, consider : Copeland and Galai (1983) ; Glosten and Milgrom (1985) ; Ho and Stoll (1983) ; and Stoll (1978, 1989). 2 . This assumption is convenient, but not necessary . If trade size varies between informed and uniformed investors, it could easily be accommodated in the two conditional per trade profitability measures . 3 . In previous drafts of this study, we provided empirical evidence consistent with the hypothesis that institutional ownership at the beginning of a period was highly positively correlated with subsequent institutional trading volume . However, in these earlier versions, we limited our subsequent interpretations of our findings to suggest that we detected only the information content of institutional trades per se, rather than the total effect of institutional ownership on the informational environment . 4 . See : Murphy and Topel (1985, especially p . 375) ; Pagan (1984) ; or Schwert and Seguin (1990, p . 1133) .

68


5 . Using the sample and techniques described in Bessembinder (1996), we estimated the correlations between effective and realized spreads for 1300 NYSE and 1300 Nasdaq firms from April through December 1994 using intra-day data . Although the correlations across firm-month observations between average quoted and effective spreads are high for both market structures, the correlation is greater for Nasdaq firms (0 .980) than the corresponding correlation for NYSE-listed firms (0 .974) . Also, see Demsetz (1995) . 6 . We have manually identified over 1,800 examples of no Spectrum data for a particular firm and quarter even though there are Spectrum-reported holdings for the previous and subsequent quarters . For example, Spectrum reported substantial institutional holdings, including one block of 67,400 shares held by the same institution in both the third quarter of 1985 and first quarter of 1986, for American Aggregates Corp . However, in the intervening quarter (the fourth quarter of 1985), Spectrum contains no record of any institutional holdings . 7 . See Admati and Pfleiderer (1988), Clark (1973), Epps and Epps (1976), and Tauchen and Pitts (1983) for theoretic models, Karpoff (1987) for a review of empirical findings, and Bessembinder and Seguin (1992, 1993), and Bessembinder, Chan and Seguin (1996) for empirical evidence on the differential effects of the expected and unexpected components of volume. 8 . In a multiple regression framework, the coefficient associated with an independent variable estimates the relation between the dependent and that independent variable conditional on the inclusion of the other variables . In this case, we are picking up the relation between volume and spread, conditional on two important predictors of anticipated volume, the number of market makers and shares outstanding, being included in the specification. Thus, the coefficient we estimate must be (and, as we empirically verify, is) identical to the resulting coefficient from a two-step procedure where we first regress volume on number of market makers and shares outstanding. The second step then involves using residuals from the first step regression, which are estimates of unexpected volume, in our main specification . 9 . Exceptions include : Lakonishok, Shleifer, Thaler and Vishny (1991) ; Lakonishok, Shleifer and Vishny (1992) who examine only pension fund managers ; and Hendricks, Patel and Zeckhauser (1993) who examine only mutual funds . 10 . See : Demsetz and Lehn (1985) ; Jensen and Warner (1988) ; Brickley, Lease and Smith (1988) ; Agrawal and Mandelker (1990) ; or Black (1992) . 11 . It is important to point out that the choice of using the current level of abnormal spread or the change in abnormal spread as the regressand is irrelevant . Since we include the lagged level of abnormal bid-ask spread among the regressors, all estimated coefficients and their standard errors will be identical using either option, with one exception . The coefficient associated with lagged abnormal spread will be exactly one unit larger when the level of abnormal spread is used as a regressand than it would be if the change in abnormal spread was used as the regressand. However, the coefficients and standard errors on all remaining variables, including the institutional ownership variable, remain unchanged across the two specifications . 12 . To determine whether this difference is attributable to our error, we asked Professor Nimalendran to verify our code . He did so . He further reported to us that, using a Nasdaq-NMS subsample of his data, he generated estimates that are within one standard error of the estimates we report here


69

ACKNOWLEDGMENTS We thank Gautam Kaul and M . Nimalendran for their econometric help, and George Benston, Brian Bushee, Hendrik Bessembinder, Jay Coughenour, Diane Del Guercio, Mason Gerety, E . Philip Howrey, Michele LaPlante, Wayne Mikkelson, Harold Mulherin, G . William Schwert, Ralph Walkling, Marc Zender, and participants at seminars at Emory University, University of Michigan, University of Minnesota, University of Missouri, Ohio State University, University of Oregon, the University of Texas-Dallas, Vanderbilt University, University of Western Ontario, University of Wisconsin, and the Financial Management Association meetings in Honolulu for their comments . The opinions included are those of the authors and not necessarily those of the U .S . Air Force Academy, the U .S . Air Force or any other federal agency .

REFERENCES Admati, A . R ., & Pfleiderer, P . (1988). A theory of intraday patterns : Volume and price variability . Review of Financial Studies, 1, 1-40 . Agrawal, A ., & Mandelker, G. N . (1990) . Large shareholders and the monitoring of managers : The case of antitakeover charter amendments . Journal of Financial & Quantitative Analysis, 25, 143-161 . Amihud, Y ., & Mendelson, H. (1986) . Asset pricing and the bid-ask spread . Journal of Financial Economics, 17, 223-249. Bessembinder, H. (1996). The degree of price resolution and equity trading costs . Working paper . Tempe, AZ : Arizona State University . Bessembinder, H., & Seguin, P. J. (1992) . Futures-trading activity and stock price volatility . Journal of Finance, 47, 2015-2034 . Bessembinder, H., & Seguin, P. J. (1993) . Price volatility, trading volume, and market depth : Evidence from futures markets . Journal of Financial & Quantitative Analysis, 28, 21-39 . Bessembinder, H ., Chan, K ., & Seguin, P. J . (1996) . An empirical examination of information, differences of opinion, and trading activity . Journal of Financial Economics, 40, 105-134 . Black, B . S . (1992) . The value of institutional investor monitoring : The empirical evidence. UCLA Law Review, 39, 895-939 . Brennan, M., & Subrahmanyam, A . (1995) . Investment analysis and price formation in securities markets. Journal of Financial Economics, 38, 361-381 . Brickley, J ., Lease, R ., & Smith, C . (1988). Ownership structure and voting on antitakeover amendments. Journal of Financial Economics, 20, 267-291 . Chiang, R ., & Venkatesh, P. C . (1988) . Insider holdings and perceptions of information asymmetry : A note . Journal of Finance, 43, 1041-1048 . Clark, P . K . (1973) . A subordinated stochastic process model with finite variances for speculative prices . Econometrica, 41, 135-155 . Copeland, T ., & Galai, D . (1983). Information Effects on the Bid-Ask Spread. Journal of Finance, 38, 1457-1469 . Davidian, M ., & Carroll, R . J . (1987) . Variance function estimation . Journal of the American Statistical Association, 82, 1079-1091 .

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WILLIAM W. JENNINGS, KAREN SCHNAI IhRLY AND PAUL J . SEGUIN

Demsetz, H . (1995) . A non-collusive explanation for the pattern of spreads on the Nasdaq . Working paper, L .A ., CA : University of California. Demsetz, H ., & Lehn, K . (1985) . The structure of corporate ownership : Causes and consequences . Journal of Political Economy, 93, 1155-1177 . Devenow, A ., & Welch, I . (1996). Rational herding in financial economics . European Economic Review, 40, 603-615 . Dickey, D . A., & Fuller, W . A . (1979) . Distribution of the estimates for autoregressive time series with a unit root. Journal of the American Statistical Association, 74, 427-431 . Epps, T . W., & Epps, M . L. (1976) . The stochastic dependence of security price changes and transaction volumes : Implications for the mixture-of-distributions hypothesis . Econometrica, 44, 305-325 . Fabozzi, F . (1979). Bid-ask spreads for over-the-counter stocks . Journal of Economics and Business, 32, 56-65 . Fama, E. F ., & French, K . R . (1992). The cross-section of expected stock returns . Journal of Finance, 47, 427-465 .

Fama, E . F ., & Macbeth, J. D. (1973) . Risk, return and equilibrium : Empirical tests. Journal of Policitical Economy, 81, 607-636 . George, T ., Kaul, G ., & Nimalendran, M . (1991) . Estimation of the bid-ask spread and its components : A new approach. Review of Financial Studies, 4, 623-656 . Glosten, L . R., & Milgrom, P . R. (1985) . Bid, ask, and transaction prices in a specialist market with heterogeneously informed traders . Journal of Financial Economics, 14, 71-100 . Granger, C . W . J . (1969) . Investigating causal relations by econometric models and cross-spectral methods . Econometrica, 37, 424-438 . Harris, L. (1994) . Minimum price variations, discrete bid-ask spreads and quotation sizes . Review • Financial Studies, 7, 149-178 . Heard, J ., & Sherman, H . (1987) . Conflicts of interest in the proxy voting system . Working paper, Investor Responsibility Research Center . Hendricks, D., Patel, J., & Zeckhauser, R . (1993). Hot hands in mutual funds : Short-run persistence of relative performance, 1974-1988. Journal of Finance, 48, 93-129 . Hirshleifer, D . (1995) . The blind leading the blind : Social influence, fads and informational cascades . In : M . Tommasi & K. lerulli (Eds), The New Economics of Human Behaviour . Cambridge : Cambridge University Press . Ho, T. S . Y., & Stoll, H . R . (1983) . The dynamics of dealer markets under competition . Journal • Finance, 38, 1053-1074 . Jensen, M ., & Warner, J . (1988) . The distribution of power among corporate managers, shareholders and directors . Journal of Financial Economics, 20, 3-24. Jones, C . M ., Kaul, G ., & Lipson, M . L. (1993) . Information, trading, and volatility . Journal of Financial Economics, 36, 127-154 . Karpoff, J . M . (1987) . The relation between price changes and trading volume: A survey . Journal • Financial & Quantitative Analysis, 22, 109-126 . Kothare, M., & Laux, P. (1995) . Trading costs and the trading systems for Nasdaq stocks, Financial Analysts Journal, 42-53 . Lakonishok, J., Shleifer, A ., Thaler, R., & Vishny, R . (1991) . Window dressing by pension fund managers, American Economics Association Papers and Proceedings, 81, 227-231 . Lakonishok, J ., Shleifer, A ., & Vishny, R . W . (1992) . The impact of institutional trading on stock prices . Journal of Financial Economics, 32, 23-43 . Loss, L., & Seligman, J . (1990) . Securities Regulation (3rd ed .), Boston, MA : Little, Brown and Co .


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Murphy, K . M ., & Topel, R . H . (1985) . Estimation and inference in two-step econometric models . Journal of Business and Economic Statistics, 3, 370-379 . Pagan, A . (1984) . Econometric issues in the analysis of regressions with generated regressors . International Economic Review, 25, 221-247 . Petersen, M., & Fialkowski, D . (1994). Posted versus effective spreads : Good prices bad quotes . Journal of Financial Economics, 35, 269-292 . Plosser, C. I., & Schwert, G . W. (1978) . Money, income, and sunspots : Measuring economic relationships and the effects of differencing . Journal of Monetary Economics, 4, 637-660 . Pound, J., & Shiller, R . (1987) . Are institutional investors speculators? Journal of Portfolio Management (Spring), 46-52 . Sarin, A ., Shastri, K . A ., & Shastri, K. (1996) . Ownership Structure and Stock Market Liquidity . Working paper, Santa Clara, CA, Santa Clara University . Schwert, G . W ., & Seguin, P. J . (1990) . Heteroskedasticity in stock returns . Journal of Finance, 44, 1129-1155 . Stoll, H . R . (1978). The pricing of security dealer services : An empirical study of Nasdaq stocks . Journal of Finance, 33, 1153-1172. Stoll, H . R . (1989) . Inferring the components of the bid-ask spread : Theory and empirical tests . Journal of Finance, 44, 115-134 . Tauchen, G . E., & Pitts, M . (1983) . The price variability-volume relationship on speculative markets . Econometrica, 51, 485-505 . White, H . (1980) . A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity . Econometrica, 48, 485-505 .

INTERNAL CASH FLOWS AND CORPORATE INVESTMENT DECISIONS Raj Aggarwal and Sijing Zong

ABSTRACT There continues to be much interest in the impact of internal funds on the level of corporate investment activity. While some studies provide evidence that investment decisions of firms that are financially constrained are more sensitive to the availability of internal funds than those of less constrained firms, other studies show the opposite, i .e. investment decisions of the most credit-worthy firms are most sensitive to internal funds availability. This paper tests these opposing propositions for U .S. firms using data for a longer and more recent period than in prior studies . The results support the latter view, i.e . investments at financially constrained firms are less sensitive to internal cash flows than in financially less constrained firms . Regardless, investments levels are found to be positively related to internal cash flows for all types of firms and for all periods examined indicating a pecking order in financing (firms prefer first using internal funds and only then go to external funds) .

Innovations in Investments and Corporate Finance, Volume 7, pages 73-92 . Copyright © 2002 by Elsevier Science Ltd . All rights of reproduction in any form reserved . ISBN: 0-7623-0897-4 73

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RAJ AGGARWAL AND SIDING ZONG

1. INTRODUCTION As shown in Modigliani and Miller (1958) for a company operating in perfect

markets, it has been widely contended and generally assumed in the finance literature that a company's financing policies and capital structure decisions are

irrelevant in maximizing firm value and value maximizing real investment

decisions are independent of financing decisions . However, recent literature has started noting that most firms generally operate in incomplete or imperfect

markets and find that external funds are more expensive than internal funds . There seem to be a number of market imperfections that may lead to such a

situation . Greenwald, Stiglitz and Weiss (1984), Meyers and Majluf (1984) use

asymmetric information about a firm's investment opportunities (managers have

more information than do external providers of funds) as the reason for this

financing hierarchy . Bernanke and Gertler (1990) and Gertler (1992) show that agency costs can also lead to this differential in the costs of external versus

internal funds .

Fazzari, Hubbard and Petersen (1988), Whited (1992), Bond and Meghir

(1994), Gilchrist and Himmelberg (1995), and Lamont (1997), and others have

provided empirical evidence of the pecking order of financing costs and its

impact on firm investment levels . These studies note that investment levels

at the most financially constrained firms are most sensitive to the availability of internal funds . In contrast, Kaplan and Zingales (1997) and Cleary (1999) find empirical evidence that investment levels at the least financially constrained firms are most influenced by availability of internal funds .' This paper attempts to address this controversy using improved methodology and more recent data .

Using data on U .S . firms for the thirteen-year period, 1988-2000, and two

sub-periods 1988-1994 and 1995-2000, this study documents that the least

constrained firms had a stronger cash flow-investment relationship than the most

constrained firms . For the overall period and for both sub-periods, the weakest cash flow-investment relationship was for the most financially constrained firms .

These results support the findings in Cleary (1999) and Kaplan and Zingales

(1997) - findings that are inconsistent with expectations and other prior research,

as classical arguments would expect us to find stronger cash flow-investment relationships for the most financially constrained firms .' Perhaps, more

importantly, investments levels are found to be positively related to internal

cash flows for all types of firms and for all periods examined indicating a pecking order in financing (firms tend to prefer first using internal funds and

only then go to external sources of funds) . These findings should be of interest not only to managers and investors but also to policy makers .

Internal Cash Flows and Corporate Investment Decisions

75

2. FINANCIAL CONSTRAINTS AND THE CASH FLOW-INVESTMENT RELATIONSHIP As shown in the seminal paper by Modigliani and Miller (1958), for a company operating in perfect markets a company's investment levels should be unrelated to the availability of internal cash flows . Indeed, it has been widely contended and generally assumed in the finance literature that a company's financial condition is irrelevant for real investment decisions . However, recent finance literature has noted that most firms operate in incomplete or imperfect markets and that external funds are generally more expensive than internal funds . In such cases, corporate investment decisions are likely to depend on the availability of internal funds . For a number of years following the publication of Modigliani and Miller (1958), the finance literature generally assumed that corporate investment levels were independent of its financing decisions . However, this was not always so . Much literature before 1958 stressed the connection between investment decisions and financing constraints faced by a firm . An example of this early applied research on investment stressing the significance of financing constraints in business investment is Meyer and Kuh (1957) . While modern finance literature does not completely understand the nature of this relationship yet, it has again started to stress the relationship between corporate investment and financing decisions . In the Hubbard (1998) review paper, two main reasons that link internal funds and investment decisions are presented . The "macro" view is that the cyclical movements in investment appear too large to be explained by market indicators of expected future profitability or the user cost of capital . The "micro" view arises from the consequences of informational imperfections in insurance and credit markets where problems of asymmetric information between borrowers and lenders lead to a gap between the cost of external financing and internal financing . This implies that the firms facing no information costs and firms with sufficient net worth to finance their desired capital stock will not be affected . In contrast, all else being equal, firms facing high information costs and low net worth are likely to have a positive relationship between availability of internal funds and investment levels . A modern empirical study that strongly supports this cash flow-investment relationship and financing hierarchy is Fazzari, Hubbard and Petersen (FHP, 1988) . FHP test whether determinants of investment differ between firms for which, a priori, the cost of internal financing and external financing are similar and firms for which the cost external financing exceeds the cost of internal financing . They use Value Line data for 422 U .S . firms over the 1970 to 1984

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time period to analyze differences in investment levels among firms classified

according to earnings retention . The FHP framework can be interpreted as using cash flow to measure the change in net worth . FHP control for the impact of differences in investment opportunity sets by including Tobin's q in their regression :

(I/K), r = f (X/K) 1r + g (CF/K) .r + u, 1 , where :

lit

represents investment in plant and equipment for firm i during period

t; K is the beginning-of-period book value for net property, plant, and equipment ; g (CF/K) is a function of current cash flow that measures the firm

liquidity, f (X/K) is a function of variables related to investment opportunities ; and u « is an error term. Here, f (X/K) is represented by a firm's Tobin's q value . It is argued that firms with higher retention ratios face higher informational

asymmetry problems and are more likely to be liquidity constrained . Indeed, it is documented that the investment levels of such firms are much more sensitive to fluctuations in cash flow than that of mature, high dividend firms .

Whited (1992) and Bond and Meghir (1994) employ an Euler equation

approach to directly test the first-order condition of an inter-temporal

maximization problem to avoid the problems associated with the estimation and

use of Tobin's q . This model also has the advantage of controlling for the influence of expected future profitability in investment spending, while not

requiring an explicit measure of expected demand or expected costs . Both studies support the FHP results .

International evidence of this relationship has also been presented . Schaller

(1993) finds that for Canadian firms the cash flow effects are more pronounced

for young firms, firms with dispersed ownership, and non-group firms . Hayashi and Inoue (1991) use data for 687 quoted Japanese manufacturing firms over the period 1977-1986 while Blundell, Bond, Devereux and Schiantarelli (1992)

use data for 532 U .K. manufacturing firms over the period 1971-1986 . Both studies find that in addition to Tobin's Q, cash flows had a positive and highly significant effect on company investment .

A related study based on problems of asymmetric information in group firms,

Hoshi et al . (1991), found that investment decisions for 24 Japanese firms that

are not members of a keiretsu are much more sensitive to firm liquidity than that of 121 firms that are members of a keiretsu and are presumed to be less financially constrained . The idea is that keiretsu firms have access to external

financing from the group's "main bank", which monitors member firms closely and reduces information cost in external financing . Oliner and Rudebush (1992) examine 99 NYSE-listed firms and 21 OTC firms during the 1977 to 1983


77

period to show that investment is most closely related to cash flow for firms that are young, whose stocks are traded over-the-counter, and that exhibit insider trading behavior consistent with privately held information . However, not all studies support the basic FHP results . Indeed, there now seems to be significant evidence of an opposing view . Kaplan and Zingales (KZ, 1997) challenge the generality of the conclusions reached by FHP and related studies . They use a combination of qualitative and quantitative information extracted from the company annual reports to rank firms in terms of their apparent degree of financial c onstraint . KZ re-examine the sample of low-dividend-payout firms used in FHP . Based on statements contained in annual reports, they divide the firms into categories : "not financially constrained," "possibly financially constrained," and "financially constrained" . They find that the "financially constrained" group actually displays the lowest sensitivity of investment to cash flow of the three groups, a result that contradicts FHP. Based on this finding, they claim that investment-cash flow sensitivities provide no evidence of the presence of financing constraints . KZ are criticized mainly on the small sample size and their classification criteria. FHP (1996) and Schiantarelli (1995) argue the criteria rely on managerial statements about liquidity that may be self-serving and problematic and somewhat subjective operational definitions of what it means for a firm to be financially constrained . FHP (1997) also note that the FHP firm-years KZ classify as most financially constrained are actually observations from years when firms are financially distressed . FHP argue that the sample of KZ study was designed to exclude financially distressed firms, and so very few observations fall into the categories KZ label as "constrained ." As a result, the sample lacks sufficient heterogeneity to identify meaningful differences across their sample . Nevertheless, the KZ results are puzzling because they suggest that firms choose to rely primarily on internal cash flow for investment, despite the availability of additional external funds . Sean Cleary pursues this puzzle in his paper in 1999 . His study follows the approach of KZ by classifying firms according to financial variables that are related to financial constraints . Firm financial status is determined using multiple discriminant analysis, similar to Altman's Z factor for predicting bankruptcy . Unlike the previous studies, Cleary allows reclassification of firm financial status every period, and group composition is allowed to vary over time to reflect changing levels of financial constraints at the level of the firm . Cleary not only improved on KZ's methodology, but also tested a larger, more diversified sample of 1317 U .S . firms for the period 1987-1994 . His results show that while all firms are sensitive to firm liquidity, consistent with the KZ results, firms that are more

78


creditworthy exhibit greater investment-liquidity sensitivity than less creditworthy firms .

As this brief review indicates, there is clearly a controversy here . In theory

we should expect the most financially constrained firms to have the strongest

positive relationship between internal cash flow and investment levels . However, the empirical literature has supported opposing views of the impact of

financial constraints on the cash flow-investment relationship . One set of the literature supports a stronger relationship for the most financially constrained firms while the other set supports a stronger relationship for the least constrained firms . This paper uses improved research methodology and more recent data in an attempt to resolve this controversy and to explore if the recent

democratization of U .S . capital markets making external funds more readily available to firms (Duca, 2001) has changed this internal cash flow investment relationship .

3 . RESEARCH DESIGN AND DATA This study follows Cleary's approach of utilizing a computed Z score (Altman, 1968 ; Altman, Haldeman & Narayanan, 1977) to define the financially

constrained (FC), partially financially constrained (PFC), and not financially

constrained (NFC) firms . However, we use a different and better set of variables in the discriminant function to calculate the Z scores of firms and

data for U .S . firms over a longer period, from 1988 to 2000 . The sample firms are first classified into groups according to their calculated Z scores as a proxy

of the company's financial status before examining the cash flow-investment

relationship with regressions for the total sample and for each group . Next we estimate if the regression coefficients across group are significantly different from each other and if there is any pattern of coefficients across groups .

A. Classification Methodology Cleary (1999) argues that the multivariate classification scheme in his paper

effectively captures desired cross-sectional properties of firms . This may not be true since he chooses the change of dividend payout as the classification/

grouping criteria in the discriminant analysis . According to his scheme, firms

are divided into three groups depending on whether the dividend payout is increasing, decreasing, or does not change from the last year .

Three problems arise from this grouping method . First, the no-change group actually contains two kinds of companies ; companies that pay the same

dividend in two consecutive years and companies that pay no dividend at all .


79

Obviously, these two kinds of companies could be quite different in terms of liquidity but Cleary's grouping scheme fails to distinguish between the two . Second, the change in dividend payout may not be a good proxy for a company's liquidity. Bhattacharya (1979) argues that dividends convey information about the value of the firm that cannot be fully communicated by other means of signaling . Companies, therefore, would choose a certain dividend payout and would be reluctant to change it especially when the liquidity problem is regarded as temporary . Third, the three groups are of vastly different size as very few companies change their dividends in any one year and the largest group consists of companies that did not change their dividend . In order to overcome the difficulties associated with the use of dividend payout for classifying firms, this study uses Fixed Charge Coverage (FCCOV) as the grouping criteria . 3 Fixed charge coverage ratio is defined as EBIT / (interest expenses + preferred dividend payment / (1-tax rate)) . FCCOV is a better and more direct signal of a company's liquidity . Employing multiple discriminant analysis, we classify companies into groups according to a beginning-of-period financial constraint index ZFC . Company classification is allowed to change every year to reflect the fact that financial status changes continuously . The index is similar to Altman's Z factor for predicting bankruptcy . The first step in discriminant analysis is to establish two or more mutually exclusive groups according to some explicit group classification . This study classifies companies as follows : Group 1 : Group 2 : Group 3 :

Companies with the highest FCCOV that are considered not financially constrained Companies with the lowest FCCOV that are likely financially constrained Companies with medium FCCOV (however, Group 3 companies are not utilized for purpose of the discriminant analysis but like other companies, they are assigned ZFC scores and are used in the subsequent regression analysis) .

Discriminant analysis uses a number of variables that are likely to influence the characterization of a firm in one of the two mutually exclusive groups of interest . The hypothesis is that these variables will enable us to assess the degree of financial constraint faced by each firm . As in Cleary (1999), coefficient values for each independent variable' are estimated that best distinguish firms between the two groups according to the following : Z FC = (3 1 CR +)3, SLACK/K + (33 NI% + 134 Sales Growth + 13 5 Debt Ratio

80


where CR is the current ratio ; SLACK/K is slack/net fixed assets ; NI% is the net income margin ; Debt Ratio is (Current portion of long-term debt + long-term debt) / total assets ; Net income margin is net income / net sales ; Net sales growth is (net sales at time t-net sales at time t -1) / net sales at time t-1 ; Slack is cash + short-term investments + 0 .5* inventory + 0 .7* accounts receivable - short-term loans (as in Cleary, 1999) . Companies are classified every year according to their Z FC value to reflect the fact that their financial constraint status is changing continually . The top one-third of the companies ranked by their Z scores each year are categorized as not financially constrained (NFC), the next one-third as partially financially constrained (PFC), and the bottom one-third as financially constrained (FC) . B. Regression Estimation We use a somewhat modified version of the FHP regression equation to estimate the impact of internal cash flow on investment levels controlling for variations in investment opportunity sets : I/Kit _ IM/B (M/B)it + 13cF/K (CF/K) ir + u ir , Where I/K it represents investment in plant and equipment divided by the beginning-of-period book value for net property, plant, and equipment for company i and time period t ; CF represents current period cash flow to the firm as measured by net income plus depreciation plus the change in deferred taxes ; and M/B represents the firms common equity market-to-book ratio based on the previous year's actual market value at year-end . We use market-to-book ratio to proxy the growth opportunities following KZ . Current period cash flow scaled by K is used to measure the liquidity variable . This regression equation follows the specification of most previous studies including FHP and KZ and should facilitate comparison of our results with previous evidence. In order to avoid the problem of time series cross-sectional cross correlations in error terms, consistent with Cleary (1999) this study also uses panel analysis procedures, i .e. the data are demeaned before regression . In this procedure, all variables are transformed by subtracting the industry, firm, and year means from the original observations . In addition, following prior studies (e .g . Cleary, 1999) and to avoid problems with outliers, selected observations are "winsorized" (assign the maximum or the minimum value if the value of a variable exceeds that cutoff maximum or minimum value) .'


81

C. Data Selection

The data set covers 648 U .S . companies for which complete data was available in the COMPUSTAT database for the thirteen-year time period, 1988 to 2000 . We also divide this period into two sub-periods to test the robustness of the results and to compare them with the results obtained by other studies such as Cleary (1999) . The first subset for 1988-1994 is the period used in Cleary's paper . The second subset covers the more recent period, 1995 to 2000 . This more recent data set will provide additional evidence regarding the robustness of the results and if the relationship has changed over time . Regulated firms such as banks, insurance companies, other financial companies, and utility companies are deleted from the sample . Included firms are required to have complete and positive values for sales, total assets, net fixed assets, and market-to-book ratio . Table 1 reports some summary statistics for each of the three data sets, 1988-2000, 1988-1994, and 1995-2000 .

4. RESULTS A . Differences Between Firms Grouped by Financial Constraints

Table 2 shows the correlations among the ten variables and the Z values . In contrast to Cleary's findings, we do not find strong evidence that net income margin has the highest correlation with Z values . Instead, among all variables, SLACK/K seems to correlate most significantly and consistently with Z values in each sub-period . In all three periods, the current ratio, debt ratio, investment scaled by K are the next most correlated with the Z scores . These relationships support the notion that the Z scores used in this study indicate availability of internal funds . We then sort the data sets by Z value of each firm in each year . The top one-third of the companies each year are categorized as not financially constrained (NFC), the next one-third as partially financially constrained (PFC), and the last one-third as financially constrained (FC) . We expect the number of firms in each group be different each year. Table 3 shows the mean of selected variables of different groups . It is interesting to note that the PFC (instead of NFC) firms have the lowest slack over K among the three . B. Regression Estimates of the Cash Flow-Investment Relationship

Regression results for the three data sets are reported in Table 4 . As expected, CF/K is positive and significant in each group in each time period . These results

0.02 0.00 -1 .00 -0 .95 -17 .56 0.04 0 .00 -5 .00 -95 .44 -48 .34

8424 8424 8424 8424 8424 8424 8424 8424 8424 8424

N

4536 4536 4536 4536 4536 4536 4536 4536 4536 4536

CR DEBTR FCCOV NIM SALEG SLACKK K 1/K CFL/K MKBKF

Variable*

CR DEBTR FCCOV NIM SALEG SLACKK K I/K CFL/K MKBKF

02 .00 -1 .00 - .91 -17 .56 .04 .00 -5 .00 -61 .60 -7 .23

Minimum

Minimum

N

Variable*

2 .2162 0 .2106 4 .005E-02 0 .1141 1 .7634 1138 .0172 0 .2213 0 .3962 2 .3395 0 .4245

1 .3640 0 .1610 0 .1150 0 .2159 7 .8171 4312 .8152 0 .1358 0 .6088 2 .7418 0 .7396

Std. Deviation

10.00 1 .68 1 .00 1 .09 73 .34 63864.00 2 .00 5 .00 10 .00 2 .14

Maximum 2 .2603 0 .2132 3 .061E-02 0 .1132 1 .5554 960 .8444 0 .2157 0 .3433 2 .1052 0 .1629

Mean 1 .3821 0 .1695 0 .1214 0 .2110 2 .9454 3840 .6251 0 .1324 0 .5990 2 .5494 0 .3795

Std . Deviation

Panel B: Data Set 1 (1988-1994)

10.00 1 .68 1 .00 1 .09 606 .54 94043 .00 2 .00 5 .00 10.00 3 .30

Mean

Panel A : Data Set 1 (1988-2000)

1 .910 2 .872E-02 1 .475E-02 4 .454E-02 8 .675 14750401 .392 1 .752E-02 0 .359 6 .499 0 .144

Variance

1 .861 2 .592E-02 1 .322E-02 4 .663E-02 61 .107 18600375 .246 1 .844E-02 0 .371 7 .518 0 .547

Variance

Descriptive Statistics for the Data Set .

Maximum

Table 1.

2 .136 1 .689 -3 .013 1 .315 11 .240 9 .596 2 .838 -1 .305 -8 .617 -3 .789

Skewness**

2 .190 1 .287 -2.802 1 .267 58 .247 9 .954 2 .844 0 .350 -9.690 -35 .755

Skewness**

6.880 7 .196 34.150 4.903 210.786 118.745 22.772 32 .480 191 .670 65 .485

Kurtosis**

7.265 4.827 35 .870 4.774 4309.799 136.858 23 .055 26.899 283 .781 2275 .79

Kurtosis**

G) N 0 z 0

C7

Z

M

N

N

0 .10 0 .00 -1 .00 -0.95 -1 .30 0 .07 0 .00 -5 .00 -95 .44 -111 .20

Minimum

Continued .

10 .00 0.80 1 .00 1 .08 606 .54 94043 .00 2.00 5 .00 10.00 2.34

Maximum

Std . Deviation

2 .1648 1 .3410 0.2075 0 .1505 5 .107E-02 0 .1060 0.1152 0 .2215 2.0061 11 .0538 1344.7188 4797 .6655 0.2277 0 .1394 0.4578 0 .6143 2 .6127 2 .9273 4.372E-03 2 .0353

Mean

Panel C : Data Set 3 (1995-2000)

Table 1 .

1 .798 2.265E-02 1 .123E-02 4.908E-02 122.186 23017593 .887 1 .944E-02 0.377 8 .569 4 .143

Variance

2 .261 0.585 -2 .375 1 .216 44.343 9 .883 2 .848 2 .136 -10 .686 -44 .217

Skewness**

7 .792 -0.059 38 .134 4 .619 2331 .291 135 .853 23 .313 20 .646 346.797 2322 .430

Kurtosis**

Note : Data Set 1 includes 8424 observations of 648 companies in the time period of 1988-2000 . Data Set 2 includes 4536 observations of 648 companies in the time period of 1988-1994 . Data Set 3 includes 3888 observations of 648 companies in the time period of 1995-2000 . Descriptive statistics are reported for the two data sets in A and B . * All variables are defined in the text . ** All skewness and Kurtosis are significant .

CR 3888 DEBTR 3888 FCCOV 3888 NIM 3888 SALEG 3888 SLACKK 3888 K 3888 I/K 3888 CFL/K 3888 MKBKF 3888

Variable*

c'

b ft

m

0

a o

A

0

Q y

A

CR

1 .000 0 .000 0 .000 0 .000 0 .000 0 .000 0.000 0.000 0.381 0.000

Variable*

CR DEBTR NIM SALEG SLACKK K IK CFLK MKBKF Z

-0.202 1 .000 0 .000 0 .002 0 .000 0 .446 0 .000 0 .000 0 .000 0 .000

DEBTR

-0 .213 1 .000 0.000 0.359 0.000 0.247 0.000 0.000 0.000 0.000

1 .000 0.000 0.000 0.000 0.000 0.000 0 .000 0 .000 0 .000 0 .000

DEBTR NIM SALEG SLACKK K IK CFLK MKBKF Z

CR

DEBTR

CR

Variable*

0.114 -0.171 1 .000 0 .000 0 .876 0 .031 0 .004 0 .000 0 .000 0 .000

NIM

0 .112 -0 .172 1 .000 0 .000 0 .000 0.000 0.072 0.000 0.000 0.000

NIM

Table

0 .168 -0 .080 0 .077 -0 .028 1 .000 0 .000 0 .000 0 .000 0 .501 0 .000

SLACKK

0 .058 -0 .047 0 .137 1 .000 0 .005 0 .000 0 .000 0 .000 0 .000 0 .000

SALEG 0.276 -0.121 -0.002 0.042 1 .000 0 .000 0 .000 0 .000 0 .771 0 .000

SLACKK

-0.178 -0.011 0.032 -0.058 -0.098 1 .000 0 .000 0 .001 0 .744 0 .005

K

-0 .183 -0 .013 0 .048 -0 .014 -0 .045 1 .000 0 .000 0 .000 0.000 0.000

Panel B : Data Set 2 (1988-1994)

0 .040 -0.010 0.139 1 .000 0.009 0.197 0.000 0.000 0.000 0.000

SALEG K

Correlations among Variables .

Panel A : Data Set 1 (1988-2000)

2.

0 .088 -0 .168 0 .042 0 .195 0 .270 -0 .075 1 .000 0.000 0.000 0.000

IK

0 .090 -0 .191 0.020 0.161 0.172 -0.081 1 .000 0 .000 0 .000 0 .000

IK

0 .143 -0 .226 0 .468 0 .199 0.252 -0.051 0.132 1 .000 0.000 0.000

CFLK

0 .187 -0.242 0.434 0.165 0.277 -0.060 0.198 1 .000 0 .000 0 .000

CFLK

-0 .013 -0 .117 0 .080 0 .146 0 .004 0 .005 0 .129 0 .122 1 .000 0 .003

MKBKF

-0 .043 -0 .085 0 .115 0 .133 -0 .007 0 .057 0.132 0.142 1 .000 0.271

MKBKF

0 .569 -0 .380 0 .327 0 .092 -0 .545 -0.041 -0.082 0.069 0.044 1 .000

Z

0 .443 -0 .200 0 .095 0.131 -0.791 -0.057 -0.064 -0.069 0 .012 1 .000

Z

A

QC

1 .000 0 .000 0 .000 0 .212 0 .000 0 .000 0 .000 0 .000 0 .000 0 .103

CR DEBTR NIM SALEG SLACKK K IK CFLK MKBKF Z

-0.231 1 .000 0.000 0 .024 0 .000 0 .427 0 .000 0 .000 0 .004 0 .694

DEBTR 0.117 -0.171 1 .000 0.000 0 .000 0 .000 0 .266 0 .000 0 .000 0 .000

NIM 0 .020 0 .036 0 .143 1 .000 0 .001 0 .130 0 .000 0.000 0.000 0.000

SALEG

K -0.188 -0.013 0.059 0.024 -0.039 1 .000 0 .000 0 .000 0 .000 0 .286

SLACKK 0.175 -0.087 0.124 -0.055 1 .000 0 .014 0 .000 0 .000 0 .334 0 .000

0 .097 -0 .220 -0 .018 0 .126 0 .175 -0 .092 1 .000 0 .000 0 .000 0 .000

IK 0 .248 -0 .263 0 .383 0 .130 0 .340 -0 .077 0 .263 1 .000 0 .000 0.000

CFLK

-0 .069 -0 .047 0 .143 0 .121 -0 .016 0 .092 0 .129 0 .148 1 .000 0 .304

MKBKF

-0 .026 0 .006 -0 .073 0 .099 -0 .987 0 .017 -0 .149 -0 .285 0 .017 1 .000

Z

expenditures during period t . Cash flow, investment, and slack are all scaled by net fixed assets at the beginning of fiscal year t . The discriminant score (Z) is calculated using discriminant analysis according to equation ZFC = 0 1 CR + /3, SLACK/K + /3~ NI% + 04 Sales Growth +,6, Debt Ratio . A full decription of the variables is included in the text . Panel A, B and C reports the correlations of the whole data set and two subsets separately . * Above the diagonal are the Pearson correlation coefficients, and below the diagonal are the significance probabilities .

Note : All financial variables are for the beginning of the fiscal year, except cash flow and investment, which represents firm cash flow and capital

CR

Variable*

Continued .


Table 2.

W

zz

z.

b

y

p p

0

A


86 Table 3 .

Selected Financial Ratios for Financial Constraint Groups . Panel A : Data Set 1 (1988-2000)

Variable Current Ratio Debt Ratio Net Income Margin Sales Growth Slack/Net Fixed Assets Net Fixed Assets Investment/Net Fixed Assets Cash Flow/Net Fixed Assets Market-to-Book Z Score Valid N

Mean of Total

Mean of FC Group

Mean of PFC Group

Mean of NFC Group

2.2162 0.2106 4.005E-02 0.1141 1 .7634 1138 .0172 0.2213

1 .3264 0.2975 4.915E-03 6.254E-02 2 .3647 1834 .3583 0.2160

1 .8463 0 .1969 4 .531E-02 0 .1236 1 .1964 1315 .2496 0 .2155

3 .4761 0.1373 6.993E-02 0 .1564 1 .7290 264.4436 0.2323

0 .3962

0 .3177

0 .3820

0 .4887

2 .3395 0 .4245 8424

2 .3190 2.183E-02 2808

2 .3965 0 .3752 2808

2 .3029 0 .8764 2808

Panel B : Data Set 2 (1988-1994) Mean of Total

Mean of FC Group

Mean of PFC Group

Mean of NFC Group

Current Ratio Debt Ratio Net Income Margin Sales Growth Slack/Net Fixed Assets Net Fixed Assets Investment/Net Fixed Assets Cash Flow/Net Fixed Assets Market-to-Book Z Score Valid N

2 .2603 0 .2132 3 .061E-02 0 .1132 1 .5554 960 .8444 0 .2157

1 .5400 0 .3221 -1 .5225E-02 8 .739E-02 2 .2135 1140 .9423 0 .2119

1 .8349 0.1985 3 .828E-02 0 .1153 1 .0198 1388 .0809 0.2100

3 .4059 0 .1192 6 .877E-02 0 .1370 1 .4328 353 .5099 0 .2252

0 .3433

0 .2654

0 .3293

0 .4353

2 .1052 0 .1629 4536

1 .9348 0 .07 1512

2.1408 0.1493 1512

2 .2401 0 .4822 1512

Variable

Mean of Total

Mean of FC Group

Mean of PFC Group

Mean of NFC Group

Current Ratio Debt Ratio Net Income Margin Sales Growth

2 .1648 0 .2075 5 .107E-02 0 .1152

1 .7953 0 .2665 1 .458E-02 5 .600E-02

1 .7334 0 .2128 5 .470E-02 0 .1072

2 .9658 0 .1431 8 .393E-02 0 .1824

Variable


87


Table 3 .

Continued .

Panel C : Data Set 3 (1995-2000), continued . Variable

Mean of Total

Mean of FC Group

Mean of PFC Group

Mean of NFC Group

Slack/Net Fixed Assets Net Fixed Assets Investment/Net Fixed Assets Cash Flow/Net Fixed Assets Market-to-Book Z Score Valid N

2 .0061 1344 .7188 0.2277

4 .0216 909 .6240 0.2499

0 .9206 2022 .6465 0 .2105

1 .0760 1101 .8859 0 .2228

0 .4578

0 .5749

0 .3837

0 .4147

2.6127 4.372E-03 3888

2 .5877 0.02 1296

2 .6540 0 .1130 1296

2 .5964 0.4254 1296

Note : All financial variables are for the beginning of the fiscal yeat t, except cash flow and investment, which represent firm cash flow and capital expenditures during period t . K is the firm's beginning-of-period net fixed assets value . The discriminant score (Z) is calculated using discriminant analysis according to equation Z,,, = /3 i CR + /3, SLACK/K + /33 NI% + /3, Sales Growth +05 Debt Ratio . A full description of the variables is included in the text . The FC, PFC, NFC groups are formed by sorting all form according to their discriminant scores . Every year, the firms with the lowest discriminant score (the bottom one-third) are categorized as financially constrained (NFC) . Panel A, B and C report the selected financial ratio means for the two data sets .

Table 4.

Regression Results for Internal Cash Flow Impact on Investment Levels . Panel A : Data Set 1 (1988-2000)

CF/K Total Sample

2.011E-02 8 .394* FC 1 .430E-02 4 .081 PFC 1 .849E-02 3 .669* NFC 2.856E-02 5 .448** Dummy for CF/K between FC and PFC : -1 .139

MB

Adjusted R Square

3 .889E-03 0 .016 7 .684** 2 .080E-03 0 .009 3 .184** 9 .205E-03 0 .022 6 .635** 1 .355E-02 0 .046 8 .309** Dummy for CF/K between FC and NFC : -3 .782**

Durbin-Watson

Number of Observations

2 .020

8424

1 .973

2808

2 .030

2808

2 .065

2808

Dummy for CF/K between PFC and NFC : -1 .853

88


Table 4.

Regression Results for Internal Cash Flow Impact on Investment Levels . Panel B: Data Set 2 (1988-1994)

Total Sample FC PFC NFC

CF/K

M/B

Adjusted R Square

Durbin-Watson


1 .247E-02 3 .842** 0 .3452E-02 0 .758 2.469E-02 2 .951** 2.644E-02 3 .447**

3 .858E-03 5 .555** 1 .828E-03 2 .036* 1 .189E-02 5 .835** 1 .833E-02 6 .990**

0.010

1 .948

4536

0.002

1 .849

1512

0 .032

1 .970

1512

0.052

2 .087

1512

Dummy for CF/K between FC and PFC : -2.596**

Dummy for CF/K between FC and NFC : -4 .017**

Dummy for CF/K between PFC and NFC : 0 .665


Total Sample FC PFC NFC

CF/K

M/B

Adjusted R Square

Durbin-Watson


2.876E-02 8 .115** 2 .879E-02 5 .664** 2.905E-02 3 .366** 2 .138E-02 2 .256*

3 .833E-03 5 .190** 5 .244E-03 3 .494** 1 .267E-03 1 .522 9 .171E-03 5 .079**

0 .026

2 .001

3888

0 .036

1 .984

1296

0 .010

2 .070

1296

0 .025

1 .978

1296

Dummy for CF/K between FC and PFC : 0 .204

Dummy for CF/K between FC and NFC : 0 .361

Dummy for CF/K between PFC and NFC : -0 .064

Note: Reported coefficients are the within fixed firm and year estimates over the 1988-2000 sample period (t-statistics are below the regression coefficients) . Capital expenditure divided by net fixed assets 11K is the dependent variable . The firm's market-to-book ratio and cash flow/net fixed assets are the independent variables . The FC, PFC, and NFC groups are formed by sorting all firms according to their discriminant scores . Every year, the firms with the lowest discriminant scores (the bottom one-third) are categorized as financially constrained (FC) ; the next one-third are categorized as partially financially constrained (PFC) ; and the top one-third are categorized as not financially constrained (NFC) . Panel A, B, and C report the regression result for the whole period and the two subsets respectively . ** Correlation is significant at the 0 .01 level. * Correlation is significant at the 0 .05 level .


89

show that investment levels and internal cash flows are positively related for all sub-groups of companies . This is clear evidence that company investment levels are higher (lower) when internal cash flows are higher (lower) indicating that most firms operate in imperfect or incomplete markets . The results presented here also allow for comparisons of the relative strength of this cash flow-investment relationship among firms with varying degrees of financial constraints . For the overall period (Panel A), this relationship gets stronger (higher coefficients for CF/K) with decreasing financial constraints, a result consistent with Cleary (1999) ; and Kaplan and Zingales (1999) but not consistent with other studies such as FHP (1988) . For the two sub-periods (Panels B and C) however, while this relationship is the same for data set 2 as for the overall period, data set 3 nominally shows that least financially constrained firms have the lowest coefficient for the cash flow term . However, these differences are not significant . C. Discussion

In summary, this study presents some significant and interesting results . There is clear and strong evidence that corporate investment levels are positively related to the availability of internal cash flows . While agency costs and asymmetric information have been given as some reasons for this finding, additional research would be very useful in resolving the exact reasons for this finding . In addition, for U .S . firms over a thirteen-year period, this study documents that the least constrained firms had the strongest cash flow-investment relationship . For the overall period and for both sub-periods, the weakest cash flow-investment relationship was for the most financially constrained firms . These results support the findings in Cleary (1999) and Kaplan and Zingales (1997) - findings that are inconsistent with theoretical expectations as classical arguments would expect us to find a stronger cash flow-investment relationship for the most financially constrained firms . These findings seem consistent with an agency cost explanation - managers of the least constrained firms are most able to avoid the extra monitoring required in using external funds . There is also some evidence that differences in the strength of the investment-cash flow relationship among the three groups may depend on the time period being examined (indicating the possible presence of business cycle or other temporal influences) . As an example, the results of the second period especially may have been influenced by the sharp rise in stock prices and their effects on corporate investment levels . Clearly additional research would be useful to resolve the puzzle provided by these findings (and those in Cleary (1999) ; and Kaplan & Zingales (1997)) .

90

RAJ AGGARWAL AND SUING ZONG

5. CONCLUSIONS For a company operating in perfect and efficient markets, it has been widely

contended and generally assumed in the finance literature that a company's

financing policies are irrelevant for its investment decisions . However, recent

literature has started noting that most firms operate in incomplete or imperfect markets and find that external funds are more expensive than internal funds .

There seem to be a number of market imperfections that may lead to such a

situation (e .g . asymmetric information and agency costs) . A number of studies

have provided empirical evidence of the pecking order of financing costs

(internal funds are cheaper and preferred over external funds) and its impact

on corporate investment levels . These studies note that investment levels at the most financially constrained firms are most sensitive to the availability of internal funds . In contrast some more recent empirical studies find that

investment levels at the least financially constrained firms are most influenced

by availability of internal funds . This paper presents new and additional evidence that addresses this controversy .

First, there is strong evidence that investment levels are positively associated

with the availability of internal funds for all sets of firms in all periods exam-

ined - a finding that is not consistent with classical assumptions in finance

theory . Indeed, the finding that investments levels are found to be positively related to internal cash flows for all types of firms and for all periods examined indicates a clear pecking order in financing (firms tend to prefer first using internal funds and only then go to external sources of funds) .

Second, using data on U .S . firms for a recent thirteen-year period and for

two sub-periods, this study documents that the least constrained firms had the

strongest cash flow-investment relationship . For the overall period and for both sub-periods, the weakest cash flow-investment relationship was for the most

financially constrained firms . These findings seem consistent with the fact that managers of the least constrained firms are best able to avoid the additional

external monitoring required when using external funds . These results support the findings in Cleary (1999) and Kaplan and Zingales (1997) - findings that are inconsistent with theoretical expectations as classical arguments would expect us to find a stronger cash flow-investment relationship for the most

financially constrained firms . These findings seem consistent with an agency

cost explanation - managers of the least constrained firms are most able to avoid the extra monitoring required in using external funds .

These results should be of interest not only to managers, investors, and

bankers, but also to economists and policy-makers . The determinants of corporate investment levels are an important policy input in the management


91

and growth of an economy . Investors and other providers of external funds to a company need to understand this ability of managers of the best firms to avoid what they might consider excessive external monitoring (leading to adverse selection among their customers) . These findings also have some implications for corporate dividend policies and capital structure decisions as policies that require high cash payouts are likely to be better for owners .

NOTES 1 . See Hubbard (1998) for a recent survey of this burgeoning literature . 2 . There is some evidence that at least for some sub-periods, the strongest cash flow-investment relationship is for partially financially-constrained firms (may be due to the smaller sample sizes) . Clearly additional research is needed to resolve the puzzle posed by the empirical findings presented in Cleary (1999) ; Kaplan and Zingales (1997), and in this paper. 3 . Investment is scaled by the total asset of the company to avoid the bias toward large companies . 4. The use of dummy variables for different industries in the Z value function does not result in coefficients that are significantly different . 5 . The following rules used : (1) . Assign a value of 100% (-100%) if growth in sales is greater (less) than 100% (-100%) ; (2) . Assign a value of 2 (-2) if investment/net fixed assets is greater (less) than 2 (-2) ; (3) . Assign a value of 5 (-5) if cash flow/net fixed assets is greater (less) than 5 (-5) ; (4) . Assign a value of 10 if market-to-book is greater than 10 ; (5) . Assign a value of 10 if current ratio is greater than 10; (6) . Assign a value of 100% (-100%) if net income margin is greater (less) that 100% (-100%) ; (7) . Assign a value of 100 if fixed charge coverage is greater than 100 and a value of -0 .1 if it is less than 0 .

REFERENCES Altman, E . I. (1968) . Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy . Journal of Finance, 23(4), 589-609 . Altman, E. I., Haldeman, R . G ., & Narayanan, P . (1977) . Zeta Analysis : A New Model to Identify Bankruptcy Risk of Corporations . Journal of Banking and Finance, 1(1), 29-54 . Bernanke, B ., & Gertler, M . (1989) . Agency costs, Net Worth, And Business Fluctuations . American Economic Review, 79(1), 14-31 . Bhattacharya, S . (1979) . Imperfect Information, Dividend Policy, and `The Bird in the Hand' Fallacy . Bell Journal of Economics, 10(1), 259-270 . Blundell, R . W ., Bond, S ., Devereux, M ., & Schiantarelli, F . (1992) . Investment and Tobin's Q: Evidence from Company Panel Data. Journal of Econometrics, 51(1-2), 233-257 . Bond, S ., & Meghir, C . (1994) . Dynamic Investment Models and the Firm's Financial Policy. Review of Economic Studies, 61(2), 197-222 . Cleary, S . (1999) . The Relationship between Firm Investment and Financial Status. Journal of Finance, 54(2), 673-692 .

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Duca, J . W . (2001) . The Democratization of America's Capital Markets . FRB Dallas Economic and Financial Review (Quarter 2), 10-19 . Fazzari, S ., Hubbard, R . G ., & Petersen, B . C . (1988) . Financing Constraints and Corporate Investment . Brookings Papers on Economic Activity, 1, 141-195 . Fazzari, S ., Hubbard, R . G ., & Petersen, B . C . (1996) . Financing Constraints and Corporate Investment : Response to Kaplan and Zingales . NBER WP, No . 5462 Gertler, M . (1992) . Financial Capacity and Output Fluctuation in an Economy with Multi-Period Financial Relationship . Review of Economic Studies, 59(3), 455-472 . Gilchrist, S ., & Himmelberg, C . (1995) . Evidence for the Role of Cash Flow in Investment . Journal of Monetary Economics, 36(3), 541-572 . Greenwald, B ., Stiglitz, J . E ., & Weiss, A . (1984) . Informational Imperfections in the Capital Market and Macroeconomic Fluctuations . American Economic Review, 74(2), 194-199. Hayashi, F . (1982) . Tobin's Marginal Q and Average Q : A Neoclassical Interpretation . Econometrica, 50(1), 213-224 . Hayashi, F ., & Inoue, T . (1991) . The Relation F growth and Q with Multiple Capital Goods: Theory and Evidence from Panel Data on Japanese Firms . Econometrica, 59(3), 731-753 . Hoshi, T ., Kashyap, A ., & Scharfstein, D . (1991) . Corporate Structure, Liquidity and Investment : Evidence from Japanese Industrial Groups. Quarterly Journal of Economics, 10(1), 33-60 . Hubbard, R . G . (1998) . Capital Market Imperfections and Investment . Journal of Economics Literature, 36(1), 193-225. Kaplan, S . N ., & Zingales, L . (1997) . Do Financing Constraints Explain Why Investment Is Correlated with Cash Flow? Quarterly Journal of Economics, 112(1), 169-215 . Lamont, O . (1997) . Cash Flow and Investment : Evidence from Internal Capital Markets . Journal of Finance, 52(1), 83-109 . Mayer, C . P . (1988) . New Issues in Corporate Finance. European Economic Review, 32(5), 1167-1189 . Meyer, J . R., & Kuh, E . (1957). The Investment Decision, Cambridge : Harvard University Press . Myers, S ., & Majluf, N . (1984) . Corporate Financing and Investment Decisions when Firms Have Information that Investors Do Not Have . Journal of Financial Economics, 13(2), 187-221 . Modigliani, E., & Miller, M . H. (1958). The Cost of Capital, Corporation Finance and the Theory of Investment. American Economic Review, 48(3), 261-297 . Oliner, S . D ., & Rudebusch, G . D . (1992) . Sources of the Financing Hierarchy for Business Investment. Review of Economics and Statistics, 74(4), 643-654 . Schaller, H . (1993). Asymmetric Information, Liquidity Constraints, and Canadian Investment . Canadian Journal of Economics, 26(3), 552-574 . Schiantarelli, F . (1995) . Financial Constraints and Investment : A Critical Review of Methodological Issues and International Evidence . Working paper, Boston College . Whited, T . (1992). Debt, Liquidity Constraints, and Corporate Investment : Evidence from Panel Data. Journal of Finance, 47(4), 1425-1460 .

PREFERENCES ON RELATIVE RETURN : A POTENTIAL EXPLANATION FOR SOME PRICING ANOMALIES Beni Lauterbach and Haim Reisman

ABSTRACT We derive a version of the CAPM in which investor preferences depend only on the mean and variance of the ratio between the portfolio return and a reference return. The reference return is specific to each investor, and can also be interpreted as a proxy of the consumption of his neighbors . That is, investors in our economy care about how much they consume relative to their neighbors . The model provides a rational potential explanation for the home bias enigma and for other pricing anomalies .

1 . INTRODUCTION The standard approach in asset pricing models assumes that the preferences of economic agents are defined over the distribution of random consumption streams . This paper derives an asset pricing model, assuming that the preferences of each economic agent are defined on the distribution of the agent's consumption relative to a reference consumption, for example, relative to the Innovations in Investments and Corporate Finance, Volume 7, pages 93-103 . Copyright © 2002 by Elsevier Science Ltd . All rights of reproduction in any form reserved . ISBN: 0-7623-0897-4 93

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BENI LAUTERBACH AND HAIM REISMAN

consumption bundle of a group of other agents . In our setting, agents care about how much they consume relative to their neighbors . Preferences in our style of "catching up with the Jones' " (see Abel, 1990) were first introduced by Deusenberry (1949) . Recently, Campbell and Cochrane (1999) used such preferences to explain the equity risk premium puzzle . In our model, an agent views unfavorably a deviation of her portfolio return from that of her neighbor's . That is, an agent considers part of the risk of her neighbor's portfolio as desirable, and will hold a portfolio partially resembling that of her neighbor . This generates reference portfolios that investors wish to hold mainly because they do not want to be too different from their neighbors . The reference portfolios are hedging portfolios, assuring the correlation between individual j and her group's returns . We show that under preferences on relative return the classic CAPM must be modified . Our corrected CAPM formula deducts from the classic CAPM a term that is proportional to the covariance between the asset return and the return on the portfolio of reference portfolios . Preferences on relative return generate a demand for reference portfolios . Thus, in equilibrium, stocks included in (or correlated with) the aggregate (sum of all investors') reference portfolio offer lower expected returns than the classic CAPM predicts . Our model offers interesting insights into several pricing phenomena, described in the literature as "anomalous". Consider, for example, the home bias puzzle, presented in Kang and Stultz (1997) . They observe that although barriers to international investment have fallen dramatically in recent years, investors hold more securities from their home country than implied by portfolio optimization . Coval and Moskowitz (1999) and Huberman (2001) report the existence of a domestic home bias as well . Investors tend to hold in their portfolios stocks of companies from their geographic region, for example, their local baby-Bell companies . Assuming the future performance of a baby-Bell is correlated with that of the region it serves, this investor behavior is inconsistent with principles of diversification implied by portfolio theory . Another, and perhaps most puzzling, example of a home bias is the evidence in Benartzi (2001) that workers hold non-trivial amounts of their company's stock in their retirement accounts, increasing the correlation of their future wealth with the future value of their company . In our setup, the home bias phenomenon, described above, is natural and not a puzzle at all . Because of the relative preferences, an investor wants to be (to some extent) like his neighbors in terms of consumption and wealth . Consequently, country, geographical location, and even occupational home biases emerge in individual investor portfolios, as everybody tries to link his future economic welfare to that of his neighbors, peers and reference group .

95

Preferences on Relative Return

Other pricing anomalies can also be examined in light of relative preferences . The main insight is that whenever some group of stocks becomes a reference portfolio, it would offer a lower expected return than other "small", "neglected", or "illiquid" stocks . The paper is organized as follows . Section 2 introduces the economic setting . Section 3 derives our version of the CAPM, and Section 4 discusses how the model might help explain several asset pricing "anomalies" .

2. THE ECONOMIC SETTING Assume there are N assets in the economy with random returns R i (i = 1, . . ., N), and a risk free asset (asset N + 1) that returns R f . These assets are traded at time zero and pay off at time one . Investors can choose portfolio returns of the form : N

N

R P =~c _ i ~.R.+(1-~c ~ _ r ~.) Rr.

(1)

where ci is the weight of asset i in the portfolio, and there are no restrictions on portfolio weights . Note that we define returns as payoffs, i .e . as 1 + the rate of return . We assume that investor's wealth at time one equals the portfolio return, i .e . there is no private income or endowment at time 1 . The standard assumption, made in the CAPM literature, is that preferences are defined on the mean and variance of portfolio returns . We assume, instead, that each investor considers the mean and variance of: WA (1 + Rp)/(1 + X A)

(2)

where W A is the initial wealth of investor A ; and XA is the return of a reference portfolio that investor A cares about . The reference portfolio may represent the portfolio of investor A's friends, neighbors, peers or socioeconomic layer . This is the group investor A belongs to and whose standards of living she would like to follow . Equation (2) focuses on relative future consumption . The idea of modeling preferences using relative consumption was first introduced by Deusenberry (1949) . More recently, it has been studied by Campbell and Cochrane (1999), who assume that preferences are defined on the ratio between the consumption and a weighted-average of aggregate past and future consumption of all investors (representing a consumption habit) . Whereas in their models the "Jones' " are all investors, in our setting the reference


96

consumption may also be that of a subset representing the "neighbors" of our investor. Now, define : EA = E[(1 + RP )/(1 + XA )]

(3)

QA

(4)

=

Q[(1

+ RP )/(1 + XA )]

and assume (for simplicity, without affecting the basic results) that the investor's utility function, UA , is linear in QA and E A . That is : U A(fA, EA ) _

- QA

+2kA EA

(5)

where 1/kA , is the risk aversion of investor A . Note that because WA is known, defining preferences on (1 + RP)/(1 + X A), as we do in Eq . (5), yields equivalent results to defining preferences on W A (1 + RP)/(1 + X A) . The only difference is a re-scale of the utility function . We assume that other investors have utility functions similar to that of investor A (possibly with different risk aversions) . In addition, each investor optimizes her future wealth relative to a reference return specific to her . The reference return of each investor is correlated with but not necessarily identical to that of his neighbor. 3. THE PRICING MODEL Assume that X A and RP are close to 1 . Then, for each portfolio-return R P , we can use the approximation : (1 + R P )/(1 + XA ) = 1 + RP -X A

(6)

Given the investor's mean variance utility function, Eq . (6) suggests that the investor wishes to maximize the expected value and minimize the variance of RP -XA . On reflection, the investor problem in a world with relative preferences is to determine the optimal deviation from X A , the reference portfolio . Obviously, if the investor chooses a portfolio R P = XA , i .e . to invest all wealth in the reference portfolio, she minimizes the risk of her objective (specified in Eq . (6)) . However, the investor may choose a different portfolio than the


97

reference portfolio, because the expected value of her objective, and not only its risk, enter her utility function . A simple solution to the investor's concerns about XA is to borrow and invest the proceeds in X A (i .e . hedge against XA ) . The chosen portfolio return is thus : RP = XA -Rf +

R

(7)

NH

where RNH is the return on the non-hedging component of the investor's portfolio . Substituting Eq . (7) into (6) changes the investor's objective to : I +R NH -R _

(8)

The investor's problem has transformed into choosing a portfolio NH that maximizes the expected excess return while minimizing the excess return variance . This is the familiar and standard problem in mean variance analysis . The investor chooses a mean variance efficient portfolio of risky assets and combines it with the risk-free asset . Denote the portfolio on the efficient frontier of risky assets with the highest Sharpe measure (the tangency portfolio) as MV . Then, investor A would choose a non-hedging portfolio NH with a return of: RNH

=

(1

a

A )R

f+

a AR

(9)

MV

where R MV is the return on portfolio MV ; and a A is the proportion of portfolio MV in investor's A optimal portfolio . Substituting Eq . (9) into (7), we obtain that the return of the optimal overall portfolio of investor A is given by : RP = X A -R f + (I-

a

A ) Rf + a AR

MV =

a A( R

MV -Rf) +

XA

( 10)

For utility functions with the special form assumed in Eq . (5), the optimal aA can be computed explicitly - see the Appendix, and it is given by : aA =k A G

(11)

G = (E[RMV]-Rf)/ Var[R MV]

(12)

where

98


Formulae similar to Eq . (10) hold for all other investors in the economy . Other investors may differ from investor A only in a and X . Differences in a, the proportion invested in the mean variance efficient portfolio MV, emanate from differences in risk aversion . The differences in X evolve because of differences in the reference portfolio. Note however, that we assume that all investors have homogeneous expectation . They agree and invest the non-hedging component of their portfolios in a combination of the risk free asset and portfolio MV the tangency mean variance efficient portfolio . Given that MV is a mean variance efficient portfolio, individual asset returns are on a security market line, and can be written as : E[R .)-Rf = X Cov(RMV , R)

(13)

X = (E[RMVI-Rf)/ Var(R MV)

(14)

where

Equation (13) is similar to the classic CAPM except that here, the mean variance efficient portfolio of risky assets, MV, replaces the market portfolio of risky assets . Now, let's consider the equilibrium in the economy . If each investor k demands a portfolio return R of the form of Eq . (10), then the aggregate demand P portfolio return is : k

= (Wk / W) (ak(RMV-R) + X k )

( 1 5)

where Wk is the wealth of investor k in the economy ; and W is the aggregate wealth of investors in the economy . On the other hand, the aggregate supply portfolio return is RM , the value weighted return on the market portfolio of all assets (including the risk-free asset), i .e . : N+I

RM = Ei=1w- R

I

(16)

where w1 is the proportion of the value of security i in the market . In equilibrium, the aggregate demand and aggregate supply portfolios are identical . Thus : RM = a(RMV - Rf) + Rx

( 17 )


99

where a = 1: (Wk/W)a k k

reflects the aggregate risk aversion, and R x = 1 (Wk/W)Xk k

is the aggregate reference portfolio in the economy . Rearranging Eq. (17) yields the following expression for RMV : RMV = Rf + (R M -R x)/a

(18)

Substituting RMV (Eq . (18)) into the covariance term of Eq . (13) renders the following pricing formula : E[R .]-Rf = ( X/a) Cov(RM , R .)-(X/a) Cov(Rx , R)

(19)

It is noteworthy that : X/a = E(RM -R x)/ Var(R M -R x )

(20)

To derive Eq . (20), consider an asset whose return R 1 = R M -Rx + R V and plug R, into Eq . (19) . Equation (19) is the main pricing result of our study . It is essentially a modified CAPM model, taking into account preferences on relative consumption and the existence of reference portfolios . The insight provided by Eq. (19) is that the expected return on an asset is determined not only by its covariance with the market portfolio, but also by the covariance of its return with Rx , the aggregate reference portfolio . The aggregate reference portfolio represents the aggregate investors' demand generated by their desire to resemble their neighbors . According to our model, an asset that is broadly and relatively heavily used for "catching up with the Jones"' (has a large positive weight in the aggregate reference portfolio, or is strongly positively correlated with it), would have a markedly lower expected return (all other things equal) . This is because if asset j serves frequently in the investors' reference portfolios, its demand and price would increase, and its expected return would decrease . Essentially, we have a kind of hedging demand for asset j, which justifies its lower expected return .

100


4. CAN OUR MODEL EXPLAIN ASSET-PRICING ANOMALIES? 4 .1 The Home Bias Enigma The home bias enigma is the most puzzling investor behavior anomaly uncovered lately . It appears that investors under-diversify . Despite the clear theoretical and empirical demonstrations that diversification can improve the risk-return tradeoffs of their portfolios, investors impose restrictions on diversification, that is do not diversify enough . Individual and professional investors prefer to hold, and tilt their portfolio weights towards, stocks of companies that are familiar and (geographically) close to them . The home bias was first detected in international finance, where it is referred to as the home country bias . French and Poterba (1991) demonstrate the strong tendency of investors in the U .S ., U .K., and Japan to hold domestic securities . Although many explanations have been offered - see Lewis (1999) for a review, few have undergone empirical testing and none has provided a complete account for the insufficient international diversification phenomenon . For example, Kang and Stulz (1997) note that although the barriers to international investment have fallen dramatically, foreign ownership of shares remains extremely limited . Thus, barriers on international investments and transaction costs offer, at best, only a partial explanation for the home country bias . A home bias is present in the portfolio of domestic securities as well . Coval and Moskowitz (1999) report that U .S . money managers prefer to hold in their managed portfolios stocks of firms whose headquarters are geographically close to the money manager's office . Huberman (2001) finds that in almost every state of the U .S . shareholders prefer (overweight in their portfolios) stocks of the local regional Bell company . The within-country regional home bias evolves, in the opinion of Huberman (2001), because of the cognitive preference for the familiar, discussed in Heath and Tversky (1991) . Coval and Moskowitz (2001) offer an alternative explanation . They show that money managers earn excess returns on local holdings, and argue that informational advantages motivate investors to favor nearby investments . Interestingly, it could be argued that familiarity and information advantages play a role in generating the home country bias as well . The most perplexing home bias anomaly is the within-firm home bias . Benartzi (2001) documents that employees invest about a third of their retirement funds in their own company stocks . Some of these investments can be attributed to discounts that employees receive from the company when investing in company stocks . However, Benartzi (2001) finds that about a quarter

101


of the employees' discretionary funds are also invested in the company stocks . Benartzi (2001) claims that this home bias is due to excessive extrapolation, as evidenced by his finding that employees of companies that experienced the worst (best) stock performance over the preceding 10 years allocate about 10% (40%, respectively) of their discretionary contributions to the company stock. The superior information explanation is not successful in explaining the within-firm home bias . Benartzi (2001) finds that allocations to company stock do not predict future stock performance . In the present paper, we offer a possible rational explanation for the home bias phenomena . The basic idea is that if investors care about their consumption relative to that of their neighbors, they would bias their portfolios in the direction of securities held by their neighbors, thus increasing the correlation between their own and their neighbors' wealth . We formalize this idea by assuming that economic agents have mean-variance preferences on the ratio of their return to a reference portfolio return . Under such conditions we show that investors would hedge by investing in the reference portfolio a portfolio that resembles that of their neighbors . A question arises about why investors cannot agree on the market portfolio as the reference portfolio, in which case the home bias would vanish . There are at least two answers to this question . First, if we begin with geographically segregated or segmented investment patterns, the equilibrium appears stable, and there are no major forces that can convince individual investors to change their reference portfolio to the market portfolio . Second, given non-tradable assets such as restricted stocks and options that employees receive in their workplace, investors would have to bias their reference portfolios towards local stocks . Moreover, given that local employment, wealth, and consumption is better reflected by the prices of local company shares, tilting the portfolio towards local stocks affords higher correlation with your neighbors consumption and wealth . In sum, if you want to be correlated with your neighbors, buy the stocks of the companies they work for along with other local company stocks that best track the local economic conditions . A home-biased portfolio follows . 4 .2. Other Anomalies Reference portfolios exist in practice not only because of preferences in the style of "catching up with the Jones"', but also because of money management norms and quality-control designs . Money managers are typically evaluated on the basis of their performance relative to a benchmark index . Thus, the benchmark index is the reference portfolio of money managers . Our model

102


predicts that money managers would be reluctant to deviate from their benchmark portfolio, and would hedge by investing in it . This prediction is borne out by empirical evidence . Lakonishok et al . (1997) show that money managers do not take positions that deviate considerably from the benchmark they are expected to beat . Now, suppose that many money managers use the S&P 500 as the benchmark (or reference portfolio in our paper's terms) . Then, in equilibrium, because of the preference (hedging demand) of money managers for S&P 500 stocks, S&P 500 and perhaps other large stocks (that are strongly correlated with the S&P 500 index) would have a lower expected return than small-cap stocks (all other things being equal) . This could contribute to the small firm high return anomaly, found by Banz (1981) . Further, in general, stocks that are relatively heavily represented in (or strongly correlated with) the aggregate reference portfolio, should have higher trading activity, hence, superior liquidity . This is because the routine daily rebalancing of portfolios due to changes in wealth necessitates larger transactions in these stocks . The resulting pricing effect is that relatively liquid stocks would have lower expected returns, as found by Amihud and Mendelson (1986) . In sum, our model of reference portfolios suggests that small, illiquid, or neglected stocks, which are infrequently used in reference portfolios, would have abnormally high expected returns, relative to the predictions of the standard CAPM model . Future research should examine in more detail the empirical implications and relevance of our modified CAPM model .

ACKNOWLEDGMENT We are grateful to Yakov Amihud for his many helpful comments .

REFERENCES Abel, A . (1990). Asset Prices Under Habit Formation and Catching Up With the Jones' . American Economic Review, 80(2), 38-42 . Amihud, Y ., & Mendelson, H. (1986) . Asset Pricing and the Bid-Ask Spread . Journal of Financial Economics, 17, 223-249 . Banz, R . (1981) . The Relationship Between Return and Market Value of Common Stocks . Journal of Financial Economics, 9, 3-18 . Benartzi, S . (2001) . Excessive Extrapolation and the Allocation of 401(k) Accounts to Company Stock . Journal of Finance, 56, 1747-1764. Campbell, J., & Cochrane, J . (1999) . By Force of Habit : A Consumption-Based Explanation of Aggregate Stock Behavior . Journal of Political Economy, 107, 205-251 .

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Coval, J ., & Moskowitz, T . (1999) . Home Bias at Home : Local Equity Preference in Domestic Portfolios . Journal of Finance, 54, 1-39 . Coval, J ., & Moskowitz, T . (2001). The Geography of Investment : Informed Trading and Asset Pricing . Journal of Political Economy, (forthcoming). Duesenberry, J. (1949) . Income Saving, and Theory of Consumer Behavior . Harvard University Press . French, K., & Poterba, J . (1991) . Investor Diversification and International Equity Markets . American Economic Review, 81(2), 222-226. Heath, C ., & Tversky, A . (1991) . Preferences and Beliefs : Ambiguity and Competence in Choice

Under Uncertainty . Journal of Risk and Uncertainty, 4, 5-28 . Huberman, G. (2001) . Familiarity Breeds Investment . Review of Financial Studies, 14, 659-680 . Kang, J ., & Stultz, R . (1997) . Why is There a Home Bias? An Analysis of Foreign Portfolio Equity Ownership in Japan . Journal of Financial Economics, 46, 3-28 . Lakonishok, J., Shleifer, A ., & Vishny, R . (1997) . What do Money Managers Do? Working Paper, Harvard Business School . Lewis, K. (1999) . Trying to Explain Home Bias in Equities and Consumption . Journal of Economic Literature, 37, 571-608 .

APPENDIX Given the agent's utility function, specified in Eq . (5), we need to solve Max{U A = - Var(l + R P -X A) + 2kA E[l + R P -XA] }

(Al)

Substituting Eq . (10), Rp a A (RMV -Rf) + XA , into Eq . (Al), transforms the problem into : Max{U A =

-CL

Var(RMV -Rf) + 2cXA kA E(R MV -Re), a A is real)

(A2)

The only investor choice variable in the maximization problem Eq. (A2) is aA, the proportion invested in the mean variance efficient portfolio whose return is RMV . All other variables in Eq . (A2) are pre-determined . Solving the maximization problem, we obtain (from the first order conditions) that : cc A = kA G, where G = (E[RMv]-Rf)/ Var[RMV] These are Eqs (11) and (12) in the text, respectively .

CAPITAL MARKET EFFICIENCY AND ITS IMPLICATIONS FOR THE INVESTOR : A CASE OF A SUPERIOR PRODUCT MISMARKETED John F. Gaski

ABSTRACT The subject of this manuscript is marketing of a financial concept, or principle. Despite copious empirical support and scientific acceptance, the concept of capital market efficiency has not gained widespread appreciation among the general investing public . Reasons for this apparent communication failure are examined, and an integrated remedial program is outlined. If the financial academic community wants the market efficiency idea to be noticed, let alone accepted, by investors, then better marketing, primarily promotion, will be needed.

INTRODUCTION In 1970 Eugene F . Fama published the landmark article, "Efficient Capital Markets : A Review of Theory and Empirical Work," which formally described the concept (then hypothesis) of financial market efficiency . As understood and Innovations in Investments and Corporate Finance, Volume 7, pages 105-125 . Copyright © 2002 by Elsevier Science Ltd. All rights of reproduction in any form reserved . ISBN : 0-7623-0897-4 105

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widely accepted in the allied fields of financial economics and investment science, an "efficient" market is one that fully reflects all available information in the sense that prices of assets, such as securities, incorporate that information and thus may be considered "fair" (Fama, 1970, pp . 384-385) . As prices adjust rapidly to available information, it is not possible for investors to earn above-market returns by purchasing assets they feel are undervalued or selling those considered overvalued . The market will have already bid down the latter prices and bid up prices of the former category to an equilibrium level, because of its "efficient" information processing capability, so that such investor trading opportunities are not generally present . As a result of this market efficiency, with respect to an individual security (e .g. a stock) or a portfolio of all such securities (the "market," proxied by a diversified mutual fund), an investment strategy of buy-and-hold produces the highest expected return . The apparent existence of this condition, or something very close to it, also explains why the overwhelming majority of actively managed mutual funds fail to equal the market performance level . (The acknowledged exception to this rule of so-called "semi-strong form" efficiency (Fama, 1970, 1991, pp . 1599-1602) is the possession of genuine insider information, rarely held by investors) . The modestly-named "efficient market hypothesis" - perhaps it is overdue to confer the label "principle" in view of the abundant empirical support (Fama, 1991) - contains practical implications for the investor that are noteworthy . If no trading strategy can be found to outperform the most naive possible approach of buying and holding a diversified "market" portfolio, then is not any alternative trading strategy, such as trying to "time" the market, suboptimal or even irrational? If individual stock market investors, for instance, were to learn that a strategy of random portfolio selection, i .e . throwing darts blindfolded at the stock listings (or hiring chimpanzees to do the same) and then holding indefinitely, delivers returns greater than those achieved by 75%-80% of professional money managers and stock mutual funds (Jensen, 1968 ; Malkiel, 1995), would that not be of great strategic interest to them? What are the pragmatic benefits of adhering to the investment strategy indicated by the efficient market phenomenon, i .e . buy-and-hold a market index? Because of several decades of empirical research on the subject, investors have been presented with no less than the means of maximizing expected investment returns, or at least an approach that provides the greatest risk-adjusted expected return . In other words, research on market efficiency would seem to have discovered the "holy grail" for investors . Investors wish to earn the highest possible returns, given a risk tolerance or asset class, and the method by which that can be done is now known (as well as anything in human behavioral science

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can be "known") . Investors have had the optimal trading strategy revealed to them, and it turns out to be the easiest, most straightforward strategy imaginable - a real "no-brainer ." One would think investors would appreciate what might be considered the ultimate in financial guidance . The question becomes, therefore, why has information of such obvious benefit not been more widely accepted in practice? Based upon evidence of the number of investors, institutional as well as individual, who continue to pursue an active trading approach in contravention of efficient market principles (proxied by the proportion of actively-managed mutual fund assets to those in index funds), apparently the market efficiency-related discoveries and corollary implications have remained either uncomprehended or rejected by the investing public, by and large .' If the optimal investment strategy (of all those known) is at hand, but grossly underutilized, why are investors so determined, or at least predisposed, to throw their money away on alternative, losing strategies? When an inherently beneficial product is not accepted by intended customers, suspicion naturally turns to problems of marketing .

EFFICIENT MARKET MARKETING OVERVIEW The development and publication of the concept of capital market efficiency as has originated in the finance literature over the past quarter century can be interpreted as a classic manifestation of social marketing, i .e . dissemination of an idea (Kotler & Zaltman, 1971) . Such "idea marketing" differs from conventional marketing of goods and services in some important respects . First, the product - an idea - is completely intangible . Even so-called services include a tangible aspect in most cases, e .g . air travel may include a meal, medical services relate to the explicitly tangible among the five senses, leasing or rental involves taking temporary possession of a physical product . Moreover, while marketing customarily is a matter of exchange of one thing for another (such as money), with social marketing there is virtual identity across the two sides of the transaction . The idea is the product being offered, and all the idea marketer seeks in return is acceptance of the idea . Alternatively, the marketer has the idea, and wants the customer to have the same idea . It is special circumstances such as these that pose some unique problems for the social marketer, in this instance those who would endeavor to foster understanding of capital market efficiency . (This material may resonate with readers who are also teachers, as they reflect upon difficulties encountered in getting students to "buy" what they are selling .) Perhaps capital markets scholars have never thought of themselves as marketers, and would consider such a role inappropriate or even repugnant . After all, is not the function of science and

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scholarship the creation of knowledge, with acceptance at the discretion of the audience (in some cases including certain segments of the general public)? Actually, and to the contrary, most scholars approve the dissemination of knowledge as part of their job description,' so marketing - literally social marketing - is a suitable functional paradigm for one aspect of the scholar's professional mission . The proposition that "science is marketing" (at least in part) is not new, and has received credible endorsement (Peter & Olson, 1983) . Could the problem of public non-acceptance of the objectively profound market efficiency idea be attributable to lack of effort on the part of scholars in the area? Is it that finance scholars simply have not tried to influence the general investing public, concentrating exclusively on a different target market, the academic audience? There is some evidence contesting this explanation (e .g . Malkiel, 1981 ; Mann, 1991), but even if it were largely true that technical scholarship in the field of investment science has been guilty of excessively narrow audience targeting, with insufficient concern for ameliorating the real world of investors (short of becoming registered financial planners, of course), there appear to be other marketing problems, beyond producer (of ideas) default, impeding any prospective or extant effort to market market efficiency . 3 The following sections attempt to diagnose this curious social marketing conundrum, and also describe a corrective marketing plan . The Competitive Environment

One potential difficulty afflicting almost any marketing program is the presence of competition . What kind of competition could there be opposing the efficient market hypothesis (EMH) in the marketplace for ideas? Who has a vested interest in suppressing EMH information, one should ask . What would be the consequences for the investment industry if their customers were to learn about, and accept, the EMH and its practical implications? Were investors to realize that no trading strategy betters one of "buy and hold the market portfolio," would not discretionary trading volume, and brokerage commissions, plummet? Demand for actively managed mutual funds, and for the services of money managers, likewise would be expected to evaporate . (As Samuelson put it, "[M]ost portfolio decision makers should go out of business - take up plumbing, teach Greek . . . . drop dead," 1974, p . 18 .) Clearly, money managers, mutual funds, stockbrokers, and investment advisers have a financial stake in defeating the EMH and would be motivated to do everything in their power to resist its propagation and adoption. In fact, many functionaries in the investment industry are acquainted with EMH, having been familiarized through training, and are armed to argue against it . The author has


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noted a few occasions over the years in the general financial media - a rare handful to be sure, but a few - in which investment industry representatives were confronted with EMH-related information or questions, typically just the skeleton of the concept, and were well-prepared to pounce on it in reply with disingenuous or sophistic rebuttal . Apparently, industry counter-propaganda has been winning the battle for hearts and minds (such as they are) so far, but then the other side, the investment science community, may not have truly mobilized for a war effort yet . For that, a marketing strategy may be needed . Strategic Fundamentals

The first step in the development of a marketing strategy, and the first phase of performing a marketing audit to diagnose an apparent marketing failure, rightfully should be examination of the nexus between product offering and need . In other words, what is the need that the product attempts to satisfy, and does the product genuinely provide the benefits to fulfill need satisfaction? In the case of the capital market efficiency knowledge-product, this relation may be straightforward . Normally, marketers conduct extensive research to excavate customer needs, wants, or preferences . If the purveyors of EMH-related knowledge have not presumed to venture beyond their primary realm of finance expertise and become practicing market researchers, are they to be condemned for dereliction? No ; primary research is not necessary in every marketing setting . The guiding principle is whether the research contributes information of enough value to justify its cost .' Some marketing answers are already known a priori with high enough confidence as to render research superfluous . Sometimes mere inspection of available secondary data is sufficient to answer a given research question . The focal question concerning the market for investment information appears to be an issue of that kind . Because of voluminous research done over many years, including proprietary work, along with common sense understanding of human nature, little mystery attaches to the basic motivation of investors . Varied underlying purposes are possible such as accumulating funds for retirement or for a major purchase such as higher education, or any intended future consumption, but a fair representation of the primary benefit sought from capital investment is return (Hirshleifer, 1965) - operationally expressible as maximizing return or optimizing risk-adjusted return (i .e . maximizing return subject to a risk-class constraint) . Therefore, discrepancy between customer need and central product characteristics does not appear to be a plausible candidate for the source of

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market non-acceptance in the case of this particular idea-product . EMH is a good product by any standard . It delivers the primary benefit customers are seeking by revealing the optimal trading (actually non-trading) strategy, according to a preponderance of evidence . We must look elsewhere for the cause(s) of the observed symptom . Market Segmentation and Targeting Given a market definition of general investing public, and the nature of the product as information, two distinct market segments can be identified based upon cognitive status of potential customers : those who understand market efficiency and those who do not . Investors who understand efficient market principles and yet reject the derivative strategic implications probably should not be targeted as a market . Anyone who volitionally chooses a 75%-80% probability of earning below-market returns as the price of a 20%-25% chance of above-market returns exhibits such a pronounced risk-preference that s/he may not be persuasible . Marketing science and practice have long recognized that it tends to be more efficient to offer a product that conforms to existing attitudes than to attempt the difficult task of changing attitudes in the direction of a marketed product . Targeting the informed-yet-resisting investor segment would require such inefficient effort . Therefore, the appropriate target market would be those who do not have an understanding of capital market efficiency . (The ones who do understand and accept it are already converted and need not be targeted .) That segment includes investors who would benefit from such EMH information and its implied strategic guidance, but may not have a firm attitudinal predisposition against the EMH product . Selection of that segment as a target comes with a built-in advantage for the information marketer . When the communication objective is one of creating awareness or mere cognition, rather than overcoming established attitudes, the promotional task is simplified . One is allowed to write on a blank slate, as it were .

MARKETING THE IDEA OF MARKET EFFICIENCY :

STRATEGIC SPECIFICS The Product

A product is fundamentally a bundle of benefits, a need satisfier, a problem solver . From the earlier discussion of product-need correspondence, the core benefit offering associated with EMH information seems to be well in


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order, matching the customer need precisely enough objectively to merit consideration for a product quality award - if award-conferring bodies were ever to recognize that EMH is a product . Given our intention to understand the anomalous popular resistance to an ostensibly superior product, the full range of product features also needs to be scrutinized. A now-classic exposition of the factors influencing the rate of a product's adoption was formulated by Rogers (1962) . Product characteristics affecting adoption by a market include

• relative advantage, the degree of superiority over other available products [some of the entries are analytically obvious] ;

• divisibility or trialability, the extent to which a product can be sampled on a limited basis ;

• communicability, or the ease and convenience of transmitting information about the product ;

• compatibility, how well a product fits in with the full spectrum of a customer's life ;

• complexity, or inherent difficulty of comprehending the product . The first four in the series enhance the rate of adoption, the last retards it . How does the EMH product measure up in terms of possessing the adoption facilitators? As has been established, an abundance of scientific evidence supports the "relative advantage" of EMH-enlightened capital investment . That is, the buy-and-hold-the-market approach delivers results demonstrably superior to any known rival strategy (subject to occasional and usually ephemeral anomalies of questionable practical import ; Fama, 1991, pp . 1588-1602 ; most recently Gruber, 1996 ; Haugen, 1996 ; cf. Shanken & Smith, 1996) . Likewise, the efficient market idea certainly could be tried on a limited basis (divisibility) in an investment portfolio . A small proportion of assets could be allocated to a single index fund, with its performance used as a yardstick against which other investments would be measured (if an investor chose not to believe the overwhelming volume of historical evidence available whose magnitude would dwarf the kind of trial or quasi-test described) . It is also difficult to imagine that the lower returns produced by alternative trading strategies could be more compatible with most investors' overall frames of reference than the higher returns yielded by EMH guidance . At first sight, communicability and complexity might not seem to be problematic either . As one professor of finance known to the author (and to the field through his distinguished scientific record) has expressed it, "Everything you need to know about how to optimize investment strategy can be learned

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in five seconds : buy and hold a market portfolio ." His variation of the punch line is, "Buy a Vanguard index fund." However, when investors have their own real money on the line, they may need more than a naked directive . It would be understandable for an investor to demand support, or underlying basis for the normative rule. Therein may lie some complexity . Elaboration of the EMH and its analytical and empirical underpinnings has involved a number of articles in technical scholarly journals (as cited earlier, among others such as Samuelson, 1989) . Full comprehension of that material may lie beyond the ken of the average layman investor, and possibly a fair proportion of money managers . Yet the relevant essentials of EMH are reduceable to : most professional fund managers fail to equal the success of a strategy of chimpanzees throwing darts blindfolded (cf. Fama, 1970, p . 413) and no trading strategy outperforms buy-and-hold . So perhaps complexity is not the most plausible suspected culprit in the strange case of general non-acceptance of the EMH . Moreover, given the dramatic and graphic language into which EMH can be distilled, as above, the idea-product actually lends itself to communicability contrary to a common difficulty associated with the marketing of ideas (see Rothschild, 1979, p . 12) . (The advertising creative department would have a field day!) So maybe the problem is not communicability, but the enactment of communication . Though limited efforts have been made by the creators of EMH to reach out and bring the product to the attention of the general public (e .g . Mann, 1991), their voices surely have been drowned out by the noise or propaganda of industry-vested interests, as alluded to in the section on competition . Better, or more aggressive, promotion may indeed be a major part of the answer for any who might wish to achieve popular acceptance for EMH and its practical investment significance, and that issue will be addressed in due course . Other Product Dimensions As the intrinsic EMH product seems conducive to market adoption in at least most respects, secondary product aspects such as branding and packaging should be reviewed as well . What could branding (i .e . identifying by name or symbol) possibly have to do with the EMH? Though science in the branding area may be underdeveloped, certain rules have gained consensus status regarding desirable qualities of a brand name, qualities which actually may aid a product in gaining acceptance (Kotler, 1984, p . 487) . Efficient market hypothesis and capital market efficiency may be perfectly acceptable "brand names" for the academic audience, but if one desires to impact the behavior of the general investing public, something "sexier" may have to be considered .


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If that proposition has not irretrievably alienated financial-oriented readers, a laundry list of attributes of an effective brand name includes the following . It should be short, simple, distinctive, memorable, easy to pronounce and recognize, compatible with the product, and should express something about the product's features or benefits . Action-oriented words can be helpful in some cases and the name must not impart any negative connotations . The names that have been in use for the EMH product when targeting the academic market may be adequate for the general investor segment on most counts, but some problems loom . Though the traditional brand-naming criteria surely were not among the leading concerns of the inventors of the terms "efficient market" and "market efficiency" and the associated construct, those terms may not be the most evocative with the more general audience . The deficiency clearly seems to be in not transmitting the practical features or benefits integral to the product . "Efficiency" does not quite "say it" to the investor, one can reasonably hypothesize . The scientific approach to addressing the possibility of a weak brand name would involve : (1) generating a large number of alternative names ; (2) screening out the poorer alternatives via presentation of the entire set to multiple focus groups ; and (3) testing the remainder through experimental research to identify which brand names produce the most desirable attitudinal and behavioral effects . This may not be the best forum for a detailed exposition of that process, but the beginnings of the first stage can be proposed and illustrated . Prospective audience-grabbing terms suggesting the pragmatic benefits of adopting the EMH perspective might be "optimal performance," "rational investment," "return maximizing," "beat the pros," "buy and hold your way to wealth," or "leave the ignorant losers in your dust" strategy . Perhaps, after adequate testing, applying one of those tentative brand names (or something else entirely) to the EMH idea - for the general audience only - would succeed in provoking more public attention and reaction to the product which, of course, would accrue to the investing public's own benefit .' Because of the intangibility of the EMH product, packaging will be interpreted liberally as the auxiliary and even subjective accoutrement of a product . As used here it means the constellation of perceived product attributes, as intended by the marketer, beyond the objective features (short of the ultimate benefits) - closely akin to what is known in marketing as positioning . Some creative packaging of EMH might also be helpful in gaining acceptance for it in the real-world market, as suggested in the following . A basic human need is that of self-esteem . Countless products are positioned to create the appearance of fulfilling that motive for the customer, from durable goods like automobiles to financial services such as home mortgages and other intangibles like self-improvement courses . Again, a comprehensive approach to

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"packaging" or positioning would be based upon extensive research to discern the complete range of buying motives, in addition to objective return on investment, among potential EMH customers, but a good starting point for investigation would be focus on self-esteem as a relatively safe bet to be almost ubiquitously present as a buyer motivation . Therefore, some kind of self-esteem enhancement could appropriately be made part of the total EMH package . One can speculatively foresee selling points that capitalize on that theme . Future purveyors of EMH could invoke the connection between : (a) adherence to the practical strategic implications of EMH ; and, (b) superior achievement or even personal superiority . Adopters of EMH might derive satisfaction (the purpose of any product consumption) from knowing that a result of that "purchase" is to put themselves ahead of the game, and ahead of other people, in terms of likely financial gain . To embellish for dramatic emphasis : `Be the first one on your block to understand market efficiency, or the `return-maximizing' strategy" ; "Apply the EMH investment approach and you will not only keep up with the Joneses, you will outperform at least 75-80% of the Joneses who use any other trading strategy" ; or even "Nine out of ten doctors (of finance) recommend EMH ." This intersection of Wall Street and Madison Avenue could be dangerous . Even the traditional definition of packaging may be relevant to the EMH situation . The primary functions of packaging are protection (of the product), economy, and convenience . Though physical protection of an intangible product would not be an issue per se, the EMH product is a way to preserve and protect financial assets . The EMH product is literally descriptive information and derived prescriptive strategy, but investment return would be a fair proxy for it from the customer's perspective . Adoption of the EMH (the purchase decision sought by the marketer), according to most available evidence, is the best known way of protecting investment returns . The virtual no-decision, no effort, "no-brainer," simplified approach to investing offered by EMH certainly is compatible with the economy and convenience packaging functions. In other words, the highest-return-likelihood core product comes wrapped in an economical, low transaction cost, low turnover, minimal trading tactical execution (as testified to by the comparatively low operating costs of index mutual funds) along with the ultimate convenience of a policy that eliminates any need for frequent decision making and discretionary trading . Such attributes should be made known to potential customers . The Price

Initially, it may appear as though acceptance of EMH comes at no price to the investor-customer . If anything, the price would seem to be negative as


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adoption of EMH-derived strategy produces financial gain . Yet customer acquisition of this product does come at a cost, if not a strictly financial one or monetary price . First, some effort and time may be involved in comprehending EMH sufficiently - even developing rudimentary understanding of the simplified expressions of it seen herein - so that the investor will be confident and comfortable with the naturally derived strategy . The most significant cost to individual investors, however, is the opportunity cost of forgoing alternative strategies or alternative views of financial markets . What opportunity cost accompanies abandonment of a losing strategy? For the extreme riskpreference segment, there is the loss of that 20%-25% chance of exceeding the market return . At the hedonic or emotional level, acceptance of EMH represents a form of "spoiling the fun ." Laymen become disappointed or even angry upon learning of EMH and its strategic implications . As Malkiel describes the reaction, "Telling an investor there is no hope of beating the averages is like telling a six-year-old there is no Santa Claus" (1981, p . 339) . No longer can those people feel like they are really "with it," sophisticated, and accomplishing something through their fatuous active trading . Even for the institutional investor, acceptance of "you cannot beat buying and holding a randomly selected portfolio" may take an emotional toll . After all, what career function or purpose in professional life can they have post-EMH adoption? Recall again Samuelson's advice to take up plumbing or drop dead . What can EMH marketers do to reduce the price and lessen the severity of "sticker shock" or buyer's remorse - a common customer malady when financial stakes are high? Applying their teaching talents in some way to the problem of educating the public about EMH and its significance would be a good place to start . Anything that can be done to streamline the investor learning process to mitigate the time and effort requirement may be particularly helpful . In addition, arguing the triviality of the kind of subjective emotional concerns suggested above would be a similar contribution - literally reducing the denominator's weight in the critical value/price ratio that determines a product's utility . (The risk-preferring segment is probably a lost cause, an unattractive market target as hypothesized earlier) . Issues of communication (i .e . education) and persuasion, however, are primarily the domain of promotion, addressed in a section to follow . Distribution

The distribution function, likewise, may not at first seem germane in the social marketing setting . Typically, the marketing of ideas proceeds through a direct

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channel from producer to recipient end-user . Yet distributors may indeed be utilized in EMH marketing, in a fashion, by converting opinion leaders first, in the hope that they in turn will market the idea to others . Opinion leaders in the retail investment world would include the likes of financial planners, investment advisers, book authors (other than textbooks/university professors, as the general investing public seems to pay little heed to them), financial media personae such as columnists and television commentators (e .g . Louis Rukeyser), and stockbrokers. The last may be a challenging but rewarding target market . Stockbrokers, and possibly money managers, might ordinarily be the least inviting EMH market to target, because normal self-interest would dictate rejection of the product for reasons reviewed earlier . Who would want to put themselves out of business? It may prove viable nevertheless to demonstrate a self-interest justification for accepting EMH strategy and perspective to a leading-edge cadre of financial products dealers . A variation of the "be the first on your block" theme is proposed, involving the premises : (1) Retail customers might appreciate and favor dealers who have the integrity to proffer profitable information, though it is ostensibly contrary to their own interests (i .e . no one can better the blindfolded dart-throwing approach, so skip brokers and money managers and dump everything into index funds) . (2) If the EMH idea ever catches on with the masses, the brokerage, money management, and mutual fund industries will be more than decimated anyway . (3) To inoculate or insure against that prospect, and enhance the likelihood of being among the survivors, it would be prudent for a dealer to be out in front of the curve on the EMH issue by pioneering advocacy of EMH-derived investment strategy . (Some stockbrokers and money managers will have a legitimate role in an EMH world after all, without having to "drop dead" .) Such targeting of a hostile segment may appear to be a matter of quixotic attempted attitude modification . However, the strategy fundamentally relies upon an existing attitude, self-preservation, perhaps the strongest motivator of all . Once again, after verging on promotional issues, explicit attention to that subject is due . Promotion

Aspects of a nascent strategy for promoting the idea of capital market efficiency (and especially its practical significance) have been previewed in the sections on the inherent communicability of the product, packaging/positioning, pricing qua lowering the perceived cost through persuasion, and even distribution, i .e . reaching opinion-leading intermediaries as conduit to a larger audience . (All of this is accurately suggestive of the integrated nature of good


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marketing strategy .) Much of the message content part of promotion strategy has already received elaboration, therefore . One related method for the EMH marketer to seriously consider is intensification of the message . If the personal importance of an issue can be raised for the audience (increasing the involvement level, in the lexicon of marketing), it can be more motivating of attitude and behavior . The investment implications of EMH naturally lend themselves to dramatic portrayal in a way that should evoke personal connections with investors, and maybe even higher blood pressure . Consider the following sample argument from a prospective promotional message designed for the general audience . We bring startling news to the investor about a scandal brewing in the securities industry - a scandal that may have important implications for one's own investment performance and success . The information concerns a revelation about investment strategy that could, and should, revolutionize the investment world and discredit the securities industry . Curiously, the information is well-known among those who study investment scientifically, but appears almost universally unknown within the general investing public . The news is simply this : In the stock market, it is not possible to outperform a strategy of buy-and-hold (actually buy-and-hold a market portfolio) . The most common initial reaction to this assertion is to cite the many mutual funds and money managers who report performance superior to "buy-and-hold" or the unmanaged market portfolio. The answer to that objection is succinct : The number of funds and managers who outperform the market index is far less than would occur by random chance . In other words, if all these investment managers used a strategy of throwing darts blindfolded to select stocks, they would do much better, on average, and there would be many more who earned returns in excess of the market average (the ultimate randomly selected portfolio) by chance, of course . The underlying reason for this surprising reality is what is known as the "efficient market" principle . Essentially, this means the market instantly absorbs all available information and reflects it in stock prices . Favorable information causes price to rise, unfavorable news causes price to fall - before an investor can do anything about it . Therefore, it is not possible to gain by selling one stock (already bid down) to buy another (already bid up) over the long run, except by chance . Over the long term, of course, chance gains and losses cancel out, leaving the investor in the initial buy-and-hold position except for transaction costs such as commissions and taxes . Transaction costs are the main reason active traders such as fund managers, in the aggregate, fail to match the market/buy-andhold (or dart-throwing) performance level . This efficient market idea is well-established and is supported by a mountain of evidence . The strategic implication is clear : It is irrational, in the sense of sub-optimizing, to sell a stock (from an adequately diversified portfolio) - unless one has genuine insider information that the market does not possess . Of course, there can be constraints such as a cash need that force one to sell . Discretionary trades, nevertheless, are losing events (absent chance exceptions) relative to the simple buy-and-hold alternative! The upshot of all this for the investing public, and you, is that it has the elements of a major scandal . Naturally, the brokerage industry would be motivated to withhold such information from the public because of the severely depressing impact there would be on

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trading volume, and commissions, if investors were to understand that nothing can beat buy-and-hold. One can anticipate that many securities dealers would go out of business if this information became known . Either the brokerage firms and investment companies of the world are aware of the efficient market principle, or they are not . If they are not, they are guilty of gross misfeasance. If they are, they are guilty of malfeasance tantamount to fraud - i .e. to the extent that they represent themselves as offering something better than throwing darts blindfolded .

Visualize something like the preceding copy delivered in the format of an op-ed column, a public service announcement, or a television talking head . Suggestion of scandalous or lurid dimensions of industry practice may qualify as an expose, or at least create that atmosphere - appropriately, based on the evidence - from which the public may be expected to take notice, for once . Empirically-based marketing theory also furnishes some guidance for message structure, as well as content . The circumstances defining the EMH marketing setting favor a conclusion-drawing approach, meaning a conclusion is expressly stated in the message as opposed to being left implicit (e .g . "Use Brand X laundry detergent; it gets your clothes whiter and brighter, therefore your family will appreciate you") . Expressing a conclusion if and when promoting the benefits of EMH-driven investment would be reasonable because the message involves some complexity and the audience is relatively unsophisticated with respect to the subject matter (Hovland & Mandell, 1952) . Examples in context might be "Use buy-and-hold rather than active trading to maximize expected return and therefore not undermine your own investment efforts," and "Buy an index fund, not an actively managed fund, to enhance your performance and therefore not appear a fool to those who understand capital markets ." It also appears that a two-sided message may be appropriate in this case . A two-sided message is one in which the marketer/promoter says something negative about the product offering, as well as something positive, e .g . "Sure our brand costs more, but it is worth it ." A crude contextual illustration would be "Abide by buy-and-hold or indexing because of the profit potential, even though you will no longer enjoy the thrill of being a `wheeler-dealer' who makes a lot of transactions ." Conditions dictating this type of technique include : (1) an audience that is already opposed to the communicator's position ; (2) an environment that involves competitor counter-propaganda (to be inoculated against) ; and (3) an audience sophisticated enough to think of the negative side on their own (Hovland, Lumsdaine & Sheffield, 1948) . The first two points clearly are present in the EMH market setting to some extent . Most investors do not believe the EMH-implied "buy and hold the market" philosophy, if they are aware of it, and most adhere to a completely contrary orientation in practice . The prevailing information environment is dominated by the


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promotion of actively managed funds and their stockbroker and money manager fellow travelers . Though the investing public may not be sophisticated financially, by the standards of EMH scholars, the limited down-side of strategic EMH adoption (as above) is probably not beyond their cognitive capabilities . Media Now that some ideas for creating a promotional message have been "run up the flagpole" (in caricature language any finance-oriented readers might stereotypically expect in a marketing discussion), modes of message delivery next need to be decided . The standard promotional tools to choose from are advertising, sales promotion, personal selling, and public relations . Advertising (which is paid and definitively sponsored), whether electronic or print, is probably not a viable option in general for use in getting the EMH message out . One can imagine an FMA- or AFA-sponsored full-page ad in The Wall Street Journal explaining the content and implications of Fama (1970, 1991), Samuelson (1974, 1989), and Malkiel (1981) - at a cost that may be exhorbitant even for an organization with "Finance" in its name . More realistically, the world's newest medium could be utilized : a World Wide Web site could be created to transmit the same information . What are known as "advertising specialties" would not be amenable to the task at hand either . Information adequate to explain EMH would be difficult to fit on a matchbook cover, a ball-point pen, or the back of a bowling shirt . Likewise with billboards, and the limited attention audiences are able to give them. Similarly, it would be a challenge to find a place for sales promotion, a miscellaneous category of short-term incentive-oriented tactics such as coupons, premiums (e .g . the prize inside a box of cereal), sweepstakes, and trading stamps . Rather, public relations, especially the generation of publicity, would play a leading role in effective promotion of the EMH idea . Specific tactics could include the following .

• The contribution of op-ed pieces and letters to major market and national

newspapers, as well as feature articles for business magazines, which would clarify EMH implications in a way that is comprehensible to the layman . • Issuance of press releases announcing market efficiency-related empirical studies and, especially, their practical investment indications . (Closer cooperation between university faculty and university publicity offices might be advisable in general, not only for EMH promotion purposes . Greater public dissemination of newsworthy findings could produce selling points for fundraising .)

120

JOHN F . GASKI

• Leading EMH scholars could make themselves available for media interviews, especially if the two preceding suggestions achieve some success . The author found it somewhat surprising when this very thing was done in the Mann (1991) piece . Essentially, the Fidelity Investments magazine ran an article revealing that it is virtually pointless to deal with Fidelity Investments . Apparently the firm correctly foresaw that the published revelation would not register with readers and would have no impact . Perhaps an integrated communication campaign, rather than isolated contacts, would be the remedy . The moderate amount of publicity surrounding Fama's 1970 article appears to have had insufficient long-term effect, as predicted by Samuelson (1974) . If a Nobel prize is in Professor Fama's future, that event could serve as fulcrum for a comprehensive promotional campaign . • Legal action, such as a class action suit alleging massive fraud by the securities brokerage industry, might be an attention-getter . Publicity as a marketing tool comes equipped with some distinct advantages . First, it is potentially more effectual because it is perceived as unbiased (i .e. originating with a third party) and therefore more credible . Also, note that most of the above listed tactics impose no out-of-pocket costs, requiring only minimal time and effort to execute . Personal selling (i .e . in-person contact), on the surface, would also seem to have limited applicability to EMH marketing . If every finance professor in the world devoted all discretionary time to the mission of explaining market efficiency to every friend, acquaintance, and button-holed passer-by, would the activity have more than minor impact compared to more efficient mass media publicity? However, once again, if opinion leaders were targeted, personal selling may not have to be ruled out . To illustrate, an anecdote from the author's own observation may be meaningful . The occasion was a country club encounter between a finance Ph .D . and an ophthalmologist on the subject of investment strategy . After receiving explanation of the efficient market implications as detailed here, the latter completely revised his portfolio the next day reallocating most financial assets into a handful of index funds . Not to generalize from a single case, the episode is sufficient to hypothesize a lesson : some laymen are coachable, especially if highly sophisticated . And who better than a physician to lead opinion subsequently on the topic of investment? Such high-end converts can be invaluable in social marketing . Are we not overlooking the obvious? What about the most fundamental kind of "personal selling" university professors do? Students in business schools have been exposed to information on market efficiency, along with the finest instruction, for decades now . Is this not precisely the kind of personal contact


121

with potential opinion leaders that is sought? Yes, but apparently it has been inadequate - possibly because of prevalent student disbelief that information from academia, textbooks, and professors could have anything to do with the

real world . The ratio of assets in index funds to those in managed funds (about

6%, though growing ; The Wall Street Journal, 2000) may be a fair indicant of the poor success rate of the existing state of EMH "marketing ."

CONCLUSION Tentative explanation for the limited public acceptance of market efficiency

and its investment implications has been put forth heretofore, including :

(1) insufficient effort by those knowledgeable of EMH ; (2) counter-efforts by industry special interests ; (3) communication barriers ; (4) inherent investor incapacity ; and (5) poor marketing strategy (vs . effort) by promoters of EMH knowledge . Conservatively assuming that each problem is present to some

degree, a corrective marketing program is forwarded with elements addressing those respective conditions .

Should the finance professoriate even concern itself with transmission of

important, beneficial knowledge to the general public, or proselytize with the aim of influencing behavior? Is not the scientist's role only the development of knowledge, with the "engineering" of that knowledge rightfully left to others?

Surely not. The revealed consensus within higher education is that the ultimate

purpose of the enterprise is the betterment of the human condition (Boyer, 1990 ; Hutchins, 1930, p . 7 ; Lynton & Elman, 1987) . If said professoriate possesses knowledge that would benefit investors if acted upon by them, it is no less than a noble undertaking to share that knowledge . If the collective effort to impart

that knowledge has been less than successful, a different approach deserves

consideration . Objection to these non-controversial propositions may be cavil-

lous . (Coincidentally, the idea marketing campaign proposed here roughly parallels that recommended by famous TV astronomer, the late Carl Sagan, for

combating superstition and scientific illiteracy . Dr. Sagan's P. R . prescription is for the most telegenic physical scientists to make more public appearances

to explain the wonders of their work (Sagan, 1996) . Overcoming the virtual superstition practiced by the investing public would be a crusade of equivalent cosmic magnitude .)

What purposes could be served by the present developers and holders of EMH knowledge not sharing it? Two can be hypothesized, with some reluctance . (1) As long as the general investing public remains ignorant of the

financial benefits derivable from understanding of market efficiency, that group

1 22

JOHN F . GASKI

may continue to be a source of amusement to the cognoscenti . (2) Or could finance faculty have more tangible self-interest in mind? With so many financial market participants self-defeating through excessive trading, there may be more money "left on the table" for those with better understanding of the markets . This relates to the paradox referred to earlier, i .e . efficient markets depending for price signals on trading by those who do not understand market efficiency . If market efficiency theorists do not wish to give the appearance (surely incorrect) of such unsavory motives, a more energetic effort to educate the general financial audience may be required . Some real societal benefits could accrue from better public understanding of market efficiency . Better understanding and better investment returns would naturally tend to evoke greater investor satisfaction from investing activity which, in turn, could motivate increased saving . This outcome would be especially welcome in national economies with low aggregate saving rates, such as the U .S . Suppose broader comprehension of EMH and appreciation of its implications were to depress the investor proclivity toward excessive selling, as opposed to the buy-and-hold EMH indication . Less selling pressure, ceteris paribus, would produce a rise in financial asset prices generally and, therefore, more total wealth . Even without that possible dynamic effect of unknown size, managed mutual funds impose an average decrement in annual return of 0 .43% compared with index funds, according to 1994 aggregates reported by Gruber (1996, pp . 787-790) . Extended across the $2 .1 trillion in all mutual funds, that is an annual wealth loss to investors of $9 billion, over $3 .6 billion in equity funds alone, caused by public rejection of market efficiency . Reflections Essentials of the marketing program for the strategic ideas surrounding the notion of capital market efficiency are hereby highlighted .

• Investment scientists must realize that they are confronted with a marketing problem, rather than unilaterally disarming and surrendering to the competition of industry special interests . • A new, or alternative, "brand name" for the construct may be advisable . • Strategic positioning of the EMH product should attempt to link adoption to the most powerful of motives and needs, such as self-esteem and economic self-interest . At the same time, the customer's perceived cost of adoption should be lowered . • A two-step comunication strategy is indicated, targeting opinion leaders (idea distributors) as well as end-users .


123

• Conventional marketing theory and practice provide guidance for design

of promotional messages and selection of media, based on the special circumstances of the EMH information market setting . • Publicity would be a crucial promotional tool in this situation . This comprehensive scheme amounts to an exploratory hypothesis, in effect . It is proposed that implementing such a plan would produce improved investor understanding and results . Since little or nothing - beyond product creation seems to have been done to address the (possibly unrecognized) problem in the past, an exploratory treatment is suitable . ("Build a better mousetrap" is not a viable strategy, generally, as testified to by the widespread ignorance of EMH practical significance .) Ideally, marketing research would be used to validate, refine, and augment some of the strategic elements suggested . The overarching hypothesis, nevertheless, is that an optimal marketing strategy for the EMH idea would resemble the content offered here, and even that exact content would be an improvement over the prevailing condition .

NOTES 1 . How is it that the market can be such an efficient processor of information yet so high a proportion of market participants have not processed that very information? This is the paradox of market efficiency . For asset prices to reflect current information depends on opportunities for continual discretionary trading by buyers and sellers who do not necessarily realize that such active trading is suboptimal from their own micro perspectives . The market in the aggregate appears to be efficient, but individual (or institutional) traders appear not to share that knowledge . Again, it is not held that any asset market is perfectly efficient, yet anomalous deviations from the prototype tend toward negligible practical value in terms of profit potential (Roll, 1994, p . 71) . So the efficiency hypothesis is not only a close approximation of reality, but a reasonable strategic basis for the average investor . This paper is premised on the prevailing belief, and in turn evidence, that exceptions to capital market efficiency or empirical "anomalies" are not of pragmatic utility to most investors . 2 . For support, inspect any university or business school statement of purpose or Boyer (1990) and Lynton and Elman (1987) . 3 . It will be necessary to endure different usages of "market," as in the financial market versus the market for information about financial markets, along with the verb "to market" to that customer group and the derived verbal (that is, the part of speech known as a "verbal," e .g . gerund or participle) "marketing ." 4 . Fund managers whose private information value fails to keep pace with its costs (Fama, 1991, p . 1607 ; Elton et al ., 1991), as confirmed by sub-index level returns, should be reminded of this, perhaps by taking a remedial course in marketing research . 5 . To underscore the importance of brand naming, unconvinced readers (especially those who do not come from a marketing tradition) should ponder whether their

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JOHN F . GASHI

reaction to the foregoing material would have been any more favorable had a public education program, rather than marketing campaign, been advocated .

A more operational question is, who will sponsor and execute such marketing functions on behalf of an idea? The American Finance Association and Financial Management Association might be likely prospects . A less plausible patron would be the federal government, e .g . the SEC or Commerce Department sponsoring a program of public information promulgation . Unfortunately, using taxpayer money to promote one industry sector (index mutual funds) over another (managed funds) would be philosophically problematic as well as tactically difficult in the face of likely industry opposition.

ACKNOWLEDGMENTS The author recognizes his Ph .D . mentor Michael J . Houston, then of the University of Wisconsin, for radical advice imparted a quarter century ago : "When selecting a minor (field of study), try to actually learn something and then do something with it." The accompanying article is an outgrowth of that direction . (To answer the natural question, the author was a finance Ph .D . minor while majoring in marketing .)

REFERENCES Boyer, E. L. (1990) . Scholarship Reconsidered. Priorities of the Professoriate . Princeton, NJ: Carnegie Endowment for the Advancement of Teaching . Elton, E . J ., Gruber, M . J., Das, S ., & Hlavka, M . (1991) . Efficiency with Costly Information : A Reinterpretation of Evidence from Managed Portfolios. Unpublished paper, New York University. Fama, E . F . (1970). Efficient Capital Markets : A Review of Theory and Empirical Work . The Journal of Finance, 25(May), 383-417 . Fama, E. F . (1991). Efficient Capital Markets : H . The Journal of Finance, 46(December),

1575-1617 . Gruber, M. J . (1996) . Another Puzzle : The Growth in Actively Managed Mutual Funds . The Journal of Finance, 51(July), 783-810. Haugen, R . A . (1996). Finance from a New Perspective . Financial Management, 25(Spring), 86-97 . Hirshleifer, J . (1965) . Investment Decision Under Uncertainty : Choice-Theoretic Approaches . The Quarterly Journal of Economics, 79(November), 509-536 . Hovland, C . I ., Lumsdaine, A . A., & Sheffield, F. D . (1948). Experiments on Mass Communication (Vol . 3) . Princeton, NJ: Princeton University Press. Hovland, C. I., & Mandell, W. (1952) . An Experimental Comparison of Conclusion-Drawing by the Communication and by the Audience . Journal of Abnormal and Social Psychology, 47(July), 581-588 . Hutchins, R . M . (1930) . The Spirit of the University of Chicago . The Journal of Higher Education, ](January), 5-12 . Jensen, M. C. (1968). The Performance of Mutual Funds in the Period 1945-1964 . The Journal of Finance, 23(May), 389-416.


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Kotler, P . (1984) . Marketing Management : Analysis, Planning, and Control (5th ed) . Englewood Cliffs, NJ : Prentice-Hall, Inc . Kotler, P ., & Zaltman, G. (1971) . Social Marketing : An Approach to Planned Social Change . Journal of Marketing, 35(July), 3-12 . Lynton, E . A ., & Elman, S . E . (1987). New Priorities for the University . San Francisco: Jossey-Bass . Malkiel, B . G . (1981) . A Random Walk Down Wall Street (2nd ed.) . New York : W . W . Norton & Co. Malkiel, B . G. (1995). Returns from Investing in Equity Mutual Funds 1971 to 1991 . The Journal of Finance, 50 (June), 549-572 . Mann, C . C . (1991) . Fama's Market . Investment Vision, (October/November), 52-55 . Peter, J . P ., & Olson, J . C . (1983) . Is Science Marketing? Journal of Marketing, 47(Fall), 111-125 . Rogers, E . M . (1962) . Diffusion of Innovations . New York: Free Press . Roll, R . (1994) . What Every CFO Should Know About Scientific Progress in Financial Economics : What Is Known and What Remains to be Resolved . Financial Management, 23(Summer), 69-75 . Rothschild, M . L . (1979) . Marketing Communications in Non-business Situations or Why It's So Hard to Sell Brotherhood Like Soap . Journal of Marketing, 43(Spring), 11-20. Sagan, C . (1996) . The Demon-Haunted World . New York : Random House . Samuelson, P . A . (1974) . Challenge to Judgment . The Journal of Portfolio Management, ](Fall), 17-19 . Samuelson, P. A . (1989) . The Judgment of Economic Science on Rational Portfolio Management : Indexing, Timing, and Long-Horizon Effects . The Journal of Portfolio Management, 16(Fall), 4-12 . Shanken, J., & Smith, C . W. (1996) . Implications of Capital Markets Research for Corporate Finance . Financial Management, 25(Spring), 98-104 . The Wall Street Journal (2000) . Facing the Future of Funds . (January 10), R1 .

HOW DIFFERENT IS THE LONG-RUN PERFORMANCE OF MERGERS IN THE TELECOMMUNICATIONS INDUSTRY? Stephen P. Ferris and Kwangwoo Park ABSTRACT Using a sample of telecommunications mergers during the 1990-1993 period, we find that acquiring firms underperform relative to their size and industry-matched control firms . The annual cumulative abnormal returns (CARs) to these firms are significantly negative for five years following the merger. Shareholders of the acquiring firm suffer a wealth loss of nearly 20% over the five-year post-merger period . We obtain similar results from three- and five-year holding period returns (HPRs). Our findings are consistent with those of earlier studies and indicate that regulated industries also experience post-merger underperformance . We do find upon disaggregation of the sample that larger mergers exhibit positive long-run performance while the mid-size and smaller mergers underperform relative to their control firms . We further observe that conglomerate mergers demonstrate superior long-run performance while that for non-conglomerate mergers is consistent with the aggregate sample findings and suggests significant underperformance . Innovations in Investments and Corporate Finance, Volume 7, pages 127-144 . Copyright © 2002 by Elsevier Science Ltd . All rights of reproduction in any form reserved . ISBN : 0-7623-0897-4 127

128

STEPHEN P . FERRIS AND KWANGWOO PARK

INTRODUCTION Mergers represent one of the most widely analyzed issues in corporate finance,

with the empirical literature including dozens of studies . The focus of most of these studies has been the nature of the announcement period returns and the

relative price responses of the acquiring and target firms' equity . But only a few studies (Langetieg, 1978 ; Asquith, 1983 ; Magenheim & Miller, 1988 ;

Agrawal, Jaffe & Mandelker, 1992 ; Loderer & Martin, 1992) examine the

long-run performance of acquiring firms after the merger . But within this small group of studies, none has focused on the potentially confounding issues

associated with mergers involving regulated firms . Yet, merger and acquisition

in regulated industries is an important public policy issue . The question of consolidation in these industries continues to remain controversial because of

the uncertain impact it can have on product innovation, quality of service and consumer pricing .

The present study will seek to address this void by examining the long-run

performance of mergers involving telecommunications firms operating with the

regulated 4800 standard industrial classification (SIC) code . Additionally, this study will have the advantage of updating existing empirical work in the area

by examining mergers occurring over the 1990-1993 period . This study will

help to provide further insight into what persists as an unsettled question : "Is the post-acquisition performance of acquiring firms inconsistent with market efficiency and does it provide evidence of the wastefulness of corporate mergers?" Indeed, some studies refer to findings of long-run post-merger

underperformance as an anomaly . The results of this analysis will help us to better understand if this is truly an anomaly and whether regulatory oversight exerts any identifiable influences on post-merger long-run performance .

We limit our analysis to a study of the post-acquisition performance of firms

contained in the telecommunications industry due to several considerations . First, as illustrated in Table 1, there are relatively few mergers occurring in the other

regulated industries such as electric, gas, water and waste . The small samples in these industries would limit our ability to make meaningful cross-sectional statistical comparisons . Such is not the case with the telecommunications

industry, where there are an abundance of mergers during our sample period . Further, the telecommunications industry is a highly innovative industry,

frequently leading in the utilization of new technology . Mergers in the telecom-

munications also often involve information technology firms, implying the potential for value creation and superior long-term performance .' Thus, a study focusing on mergers in this industry would provide highly useful findings regarding the ultimate economic usefulness of business mergers .

Long-run Performance of Telecommunication Mergers

129

In addition to documenting the nature of post-merger performance, we propose two hypotheses regarding the long-term performance of regulated firms following a merger . First, we propose that the size of the merger will impact the long-term profitability of a merger . Because larger mergers might be more capable of generating economies of scale in both research and production, we hypothesize that larger mergers are more likely to generate positive long-run performance than mergers between smaller firms . Second, because of the stabilized profit margins of regulated firms, we hypothesize that mergers between regulated firms are more likely to result in positive long-run performance than mergers between a regulated and a non-regulated firm . Since our methodology for examining this hypothesis involves separating firms on the basis of the standard industrial classification (SIC) codes of the acquirer and target, we are also able to examine the issue of post-merger performance based on whether the merger is a conglomerate (inter-industry) or a non-conglomerate (intra-industry) business combination .

DATA AND METHODOLOGY Data We construct our sample by examining M&A Rosters, which is extracted quarterly from the Mergers and Corporate Transactions Database of Securities Data Company (SDC) . The M&A Rosters provide basic information on all completed and reported takeover transactions in excess of $5 million . Each section of the roster is organized under the four-digit SIC code of the acquiring firm . The sample period for our analysis is from 1990 to 1993 . We select this period to emphasize recent mergers and to allow sufficient and complete data for the 60 months of post-merger analysis that we perform . We obtain all accounting data from Compustat using the firm's CUSIP, a unique numerical identifier . In Table 1 we present a time-series of the sample of telecommunications mergers for the 1990-1993 period . For purposes of comparison we also present the number of mergers across all industries as well as the number of mergers in other regulated industries within the 4800 SIC code . Over this four-year period, telecommunications represents 3 .45% of all mergers in Table 1 . Mergers in the other utilities were less common, with electric, gas, water and waste accounting for only 2 .5% of all mergers . In Table 2 we further provide description of telecommunications mergers by examining the top six telecommunications mergers over the 1990-1993 period . One factor that is prominent in this listing is the relative size of the

516(3 .8)

45

20 11 6 8

Electnc

70

22 12 9 27

Gas

9

3 2 0 4

Water

Utilitities

129

32 30 22 45

Waste

253(2 .5)

77(3 .1) 55(3 .2) 37(1 .3) 84(2 .7)

Total (%)

Note : This table shows the completed mergers and acquisitions in regulated industries for the period 1990-1993 . The sample is constructed by examining Mergers and Acquisition Rosters, which is extracted quarterly from the Mergers and Corporate Transactions Database of Securities Data Company (SDC) . The industry is identified based on the two-digit SIC Code. SIC groupings are 4800 for the telecommunications industry and 4900 for the utility industry such as electric, gas, water, and sanitary service . The numbers in parentheses represent the percentage of the merger cases accounted for by each SIC-based industry segment using the number of deals criteria . The percentage of mergers based on the size of the deal (not reported) suggests that a telecommunications merger is, on average, much larger than a utilities industry merger .

10,059

Total

122(4 .9) 57(3 .3) 63(2 .2) 105(3 .4)

(%)

U.S. Deals

2,445 1,711 2,807 3,096

Telecommunications

Total No . of

Mergers and Acquisitions in Regulated Industries for the Period, 1990-1993 .

1990 1991 1992 1993

Year

Table 1 .

W O

2(1) 3(3) 5(3) 8(4)

9(8)

10(2)

1991 1991 1993 1990

1993

1992 Bell Atlantic Corp .

22,240 .9

20,237 .3

51,228 .4 30,778.4 11,933.2 3,091 .2

Market value of equity ($ mil.)

Acquirer Firm

AT & T Corp . GTE Corp . Sprint Corp . McCaw Cellular Communications Inc . U .S . West Inc .

Name

Metro Mobile CTS Inc .

Time Warner Entertainment Co.

2,500.0 (25 .5%) 2,464.0

7,525 .7 6,754 .3 3,967 .0 3,375 .0 (42 .0%)

Market value of equity ($ mil .)

Target Firm

NCR Corp Contel Corp . Centel Corp . LIN Broadcasting Corp .

Name

is constructed by examining the Mergers and Acquisitions Rosters, which is extracted quarterly from the Mergers and Corporate Transactions Database of Securities Data Company (SDC) . The market value of equity is obtained from the COMPUSTAT database . The number in parentheses in the rank column shows the over-all U .S . rank during the merger completion year, which is provided in the first column . The number in parentheses next to the target firm's market capitalization (market value of equity) indicates the percentage of interest in the acquisitions, if the interest is below 50% .

Note : This table shows the six largest mergers and acquisitions in the telecommunications industry for our 1990-1993 sample period . The sample

Rank

Year

Table 2. Largest Six Mergers and Acquisitions in the Telecommunications Industry for the 1990-1993 Sample Period.

132


telecommunications mergers . In column 2 we present the merger's ranking relative to other telecommunications mergers as well as its ranking during the year compared to other mergers in all industries . In most cases, the specific

telecommunications merger is one of the top ten largest of all mergers for the

year. Column 4 contains the market value of equity for the acquiring firm while

column 6 contains the corresponding value for the target firm in Table 2 . The results indicate that these acquiring firms are indeed large, often five to ten times as large as their target.

Estimating Long-Run Performance The long-term under-reaction hypothesis has been extensively tested in finance

literature in recent years .' Studies focusing on the long run performance of merged firms however are almost non-existent . Thus we measure the long-run performance of mergers and estimate an aftermarket return following

completion of the merger. The aftermarket period examined in this study consists of the 60 months following the completion date of the merger. We use an industry and size matched sample (Barber & Lyon, 1996, 1997) of

non-merging regulated firms as a benchmark to calculate our measures of differential aftermarket performance .

We select the matching set of non-merging regulated firms by identifying all

firms on the

Center for Research in Security Prices (CRSP) database possessing

the same SIC code as that of the issuers . Then for each of the new mergers completed over the sample period, we select that firm classified by the same

SIC code and with the closest equity market capitalization as the matched firm . The difference in monthly rates of return between the newly created firm and its match is then cross-sectionally averaged to obtain an average

monthly-adjusted return for the 60 months following completion of the merger. The cumulative adjusted return through event month

t is calculated as the sum t. As originally

of the average monthly-adjusted returns for months 1 through

noted by Conrad and Kaul (1993), there is a bias in abnormal long-term

performance measures calculated by cumulating short-term abnormal returns . Consequently, we also estimate holding period returns for both our sample of new firms and non-merging firms as well as the cumulative differences .

As a final measure of long-term performance, we estimate a time-series of

annual wealth relatives . Similar to Ritter (1991), Loughran and Ritter (1995),

and Spiess and Affleck-Graves (1995), we define the wealth relative as the ratio of one plus the average holding period return for the newly created firms to one plus the average holding period return for the matched firms . Thus, a wealth


133

relative less than unity is evidence that the portfolio of issuing firms has underperformed relative to the portfolio of matched firms .

EMPIRICAL RESULTS Firm Size

Effects

on Long-Run Performance of Telecommunications Mergers

In panel A of Table 3 we note that the CARs are consistently negative and generally statistically significant over the various intervals examined during our 60-month aftermarket period. Indeed, over the entire five-year period, shareholders of the acquiring telecommunications firms suffer a statistically significant wealth loss of about 20% . In panel B we further explore these results to determine if the wealth loss from these mergers can be explained by the size of the merger . More specifically, it might be that smaller mergers fail to provide the production and research economies of scale or new market penetrations that can make a merger profitable . To test this conjecture, we stratify our sample of mergers into tiers based upon the market value of equity of the acquirer firm as of the first fiscal year after the merger . The results contained in panel B suggest that there is a size effect present in the long-run performance of telecommunications mergers . The CARs estimated for various 12-month periods are generally significantly positive for the largest mergers . For the overall 60-month period, shareholders of acquiring firms in the upper tier enjoy a wealth gain of nearly 12 .5% . These findings are in contrast to the smallest acquiring firms . The CARs for these mergers are uniformly negative and statistically significant for most of the 12-month periods examined in this study . For the entire five-year study period, the shareholders of the smallest (bottom tier) acquiring firms suffer a wealth loss of 59 .4% . Because of the bias in abnormal long-term performance measures calculated by cumulating short-term abnormal returns, we estimate holding period returns for our sample of acquiring and control firms . As a final measure of long-term performance, we calculate a time-series of annual wealth relatives . Similar to Ritter (1991) and Spiess and Affleck-Graves (1995), we define the wealth relative as the ratio of one plus the average holding period return for the newly created firms to one plus the average holding period return for the matched firms . Thus, a wealth relative less than unity is evidence that the portfolio of issuing firms has underperformed the benchmark portfolio of industry and size-matched firms . Panel A of Table 4 contains the mean holding period return (HPR) for the acquiring firm and its matched control firm estimated over both a three-year

65 66 67 67 68 68 69 69 70 70 70 69

1 2 3 4 5 6 7 8 9 10 11 12

0 .58* -1 .23 -2 .76* -2 .32 -2 .75* 0 .31 -1 .2 -1 .45 -1 .28 2 .55* 0 .57 -2 .17*

%AR,

0.58* -0.65* -3.41* -5 .73* -8 .48* -8.16 -9.36 -10 .81 -12.09* -9 .54* -8 .97* -11 .14*

%CAR,

Upper Tier

21 22 22 22 22 22 22 22 22 22 22 22

No . 1 .49* 0.72* -2 .38 2 .36 0.91 2 .59 0.44 0.28 0.28 -2 .56 -2.09 -0.33

%AR,

Middle Tier

Panel A : Aggregate sample results

1 .49* 2 .21* -0 .17* 2 .19 3 .10 5 .69 6 .13 6 .41 6 .69 4 .13 2 .04 1 .71

%CAR, 24 24 24 24 23 23 24 24 24 24 24 22

No . 1 .97* 0.01 1 .85 -5 .20* -4.89* 0.63 2.57 1 .70 1 .70 4.46 0.14 -1 .06

%AR,

Lower Tier

1 .97* 1 .98* 3 .83* -1 .37 -6.26 -5 .63 -3 .06 -1 .36 0.34 4.80 4.94 3 .88

%CAR,

20 20 21 21 23 23 23 23 24 24 24 25

No.

-1 .88* -4.68* -8 .62* -3 .56* -4.10 -2 .20 -6.71* -6.14* -5 .69* 5 .33 3 .44 -5 .35

%aAR,

-1 .88* -6 .56* -15 .18 -18 .74* -22 .84* -25 .04 -31 .75* -37 .89* -43 .58* -38 .25 -34 .81 -40 .16

%CAR,

Panel B : Result based on the merged firm size

Average Abnormal Returns Following the Completion of Mergers in the Telecommunication Industry for the Period 1990-1993 for the Aggregate Sample and the Size-Controlled Sample .

No .

Table 3.

Month

a x

O O o

G)

x

d

z

x

.b

69 62 57 56 56 56

1-12 13-24 25-36 37-48 49-60 1-60

-11 .14* -2.45* -0.13* -2 .03* -4 .05* -19 .80*

%AR,

-11 .14* --13 .59* -13 .72* -15 .75* -19 .80* -19 .80*

%CAR, 22 22 21 20 20 20

No . 1 .71 7 .33* -0 .38 2.71* 1 .12* 12 .49*

%AR,

Middle Tier CAR,

1 .71 9 .04* 8 .66* 11 .37* 12 .49* 12 .49*

%a

22 21 19 19 18 18

No.

Continued .

3 .88 -9 .65* -0 .41 -3 .92 -1 .41 -11 .51*

%AR,

Lower Tier

3 .88 -5 .77 -6 .18 -10 .10 -11 .51* -11 .51*

%CAR, 25 19 17 17 18 18

No .

-40 .16 -3 .85 0 .45 -4 .33 -11 .48 -59 .37

%AR,

-40 .16 -44 .01 _43 .56* -47 .89* -59 .37* -59 .37*

%CAR,

Panel B : Result based on the merged firm size

where R ncqui rer,. i, is the total return on the acquirer firm i in event month t ; and R is the total return on the control firm of the acquirer firm i in event month t . Control firms are identified based on the market value of equity and the industry two-digit SIC code . Panel A shows yearly and aggregate sample results . Panel B shows the results based on the market value of equity in the acquirer firms as of the first fiscal year-end after merger . Upper tier firms include market values of equity of $5,000 million or over, with a median market value of equity of $19,908 million . Middle tier firms have a market value of equity of $500 million or more, but less than $5,000 million, with a median market value of equity for $2,529 million . Finally, lower tier firms have a market value of equity below $500 million, with a median market value of $62 million . Statistical significance at the five percent level is represented by an * .

(1/n) , . _

Note : This table shows average monthly abnormal returns (AR) and cumulative abnormal returns (CAR) for acquirer firm i for the 60 months following the completion of the merger . Average monthly abnormal returns (AR) are calculated as :

No .

Month

Upper Tier

Panel A : Aggregate sample results

Table 3.

b

w

0

136


and a five-year time horizon . The mean HPR for the control firms exceeds that

of the acquiring firms over the entire examination period . This finding is additional evidence to support the claim of poor long-run performance by

telecommunications mergers . We also present results for a wealth relative which is the ratio of one plus the mean holding period return for the acquiring firms

divided by one plus the mean control firm holding period return . Values of the wealth relative ratio less than one are consistent with underperformance by the

acquiring firms . The wealth relative ratio calculated for three year holding period returns is 0 .964 while that for five year holding period returns is 0 .845 .

In Panel B we present our findings from a HPR analysis of size-based tiers

for the acquiring firms . Our findings are consistent with those obtained from

an analysis of the monthly CARs reported in Table 3 . That is, larger mergers

are associated with superior performance relative to their control firms . Mergers in the top tier exhibit larger mean HPR than their control firms . Correspondingly, their wealth relative ratios are in excess of one . The results based upon

the five-year HPRs provide stronger evidence for a size effect in the long-run

performance following telecommunications mergers . Indeed, for the five-year HPRs, the wealth relatives monotonically decline from a value of 1 .032 for the largest mergers to 0 .603 for the smallest mergers .

Industry Effects on Long-Run Performance of Telecommunications Mergers It might be that conglomerate mergers are less likely to succeed because

managers of acquiring firms do not understand the target industry, economies of scale are less feasible or that such acquisitions represent the waste of free

cashflow (Jensen, 1986) . In Table 4 we examine this issue by creating merger

subsamples bases on the target firm's SIC . Telecommunications mergers

occurring solely within the 4800 SIC code (i .e . both acquiring and target firms)

are termed non-conglomerate mergers. Mergers where the target has a SIC code

other than 4800 is classified as a conglomerate merger .' The small size of the conglomerate merger subsample limits the statistical analysis that is possible, so we advance these findings as suggestive rather than definitive .

In Table 5 we observe that non-conglomerate mergers continue to

demonstrate a pattern of significantly negative CARs for the 60 months

following the merger . The results are stronger than those estimated for the entire

sample reported in Table 2 . The results for the conglomerate mergers however fail to exhibit the same kind of negative pattern . All of the CARs are positive, though statistically insignificant . This finding is consistent with that

reported by Agrawal, Jaffe and Mandelker (1992) who conclude that "the

55 .10

1990-93

1 .008 0 .932 0 .958

0.964

1 .010 0.808 1 .093 0.931

Wealth Relative

0 .409 0 .480 0.480

0 .473

0 .429 0 .500 0 .438 0 .522

Fraction Underperform

108 .09 97 .81 17 .06

77 .69

77 .69 91 .60 86.55 102 .02

Acquirer Firms %HPR

101 .51 138 .39 93 .81

110.39

110.39 56 .99 82.33 175 .85

Control Firms %HPR

1 .032 0 .829 0 .603

0.845

0.845 1 .220 1 .023 0 .580

Wealth Relative

Five Years

0 .350 0 .611 0 .714*

0 .480

0 .389 0 .363 0 .615 0.529*


l

-1

[rI7`

(1 + R11)-1]* 100%

tier firms have a market value of $500 million or more, but less than $5,000 million, with a median market value of $2,529 million . Finally, the lower tier firms have market values below $500 million, with a median market value of $62 million . A statistically significant difference from 0 .50 at the five percent level is represented by an * .

where R,, is the return on acquirer firm i on the tth day after the merger completion date . Ti is the number of days from the merger completion date (usually 756 days and 1260 days for the three- and five-year periods, respectively) . The wealth relative ratio is defined as the ratio of 1 plus the mean holding period return for the acquirer firms divided by I plus the mean control firm holding period returns. Panel A shows yearly and aggregate sample results . Panel B shows the results based on the market value of equity in the acquirer firms as of the first fiscal year end after merger . Upper tier firms include market values of equity of $5,000 million or over, with a median market value of $19,908 million . Middle

(1/n) Y, ;'_

Note : The table shows average holding period returns (HPRs) for post-merger three and five years . Average holding period returns (HPRs) are calculated as

Panel B : Results based on the merged firm size Upper Tier 22 62 .97 Middle Tier 25 64 .05 Lower Tier 25 23 .24

72

61 .67 75 .88 28 .54

36.76 76.15 46 .83 62.94

Panel A : Yearly and aggregate results 1990 21 38 .18 1991 12 42 .38 992 16 60 .43 1993 23 51 .76 49 .55

%HPR

No . of Firms %HPR

Year

Control Firms

Three Years

Long-run Stock Returns for Acquirer Firms in the Telecommunications Mergers Between 1990-1993 . Acquirer Firms

Table 4.

w

0

rr

10 11 12

1 2 3 4 5 6 7 8 9

Month

Table 5.

57 58 59 58 59 59 60 60 61 61 61 60

No . -0 .27* -1 .46 -3 .55* -3 .48* -3 .62* -0 .05 -2 .12 -1 .85 -1 .64 2 .92* 0 .77 -3 .12*

%AR, -0 .27* -1 .73* -5 .28* -8 .76* -12 .38* -12 .43* -14 .55* -16 .40* -18 .04* -15 .12* -15 .89* -19 .01*

%CAR,

Panel A: Results from non-conglomerate mergers

8 8 8 8 9 9 9 9 9 9 9 9

No .

6 .65 4 .19 3 .04 5 .20 2 .94 2 .66 4 .93 1 .15 1 .15 0 .06 -0 .78 4 .19

%AR,

6 .65 10 .84 13 .88 19 .08 22 .02 24.68 29 .61 30.76 31 .91 31 .97 31 .19 35 .38

%CAR,

Panel A : Results from conglomerate mergers

Average Abnormal Returns Following the Completion of Conglomerate and Non-Conglomerate Mergers in the Telecommunications Industry for the 1990-1993 Period .

w 00

Continued .

60 53 48 47 47 47

1-12 13-24 25-36 37-48 49-60 1-60 -19 .01* -3 .52* -0 .51* -5 .99* -4 .71* -33 .74*

%AR,

-19 .01* -22 .53* -23.04* -29 .03* -33 .74* -33 .74*

%CAR

9 9 9 9 9 9

No . 35 .38 .15 1 2 .39 2 .93 2 .55 44 .40

%AR,

35 .38 36 .53 38 .92 41 .85 44 .40 44 .40

%CAR,

Panel A : Results from conglomerate mergers

a)

where R is the total return on the acquirer firm i in the event month t and R , is the total return on the control firm of the acquirer firm i in event month t. Control firms are identified based on the market value of equity and the industry two-digit SIC code . Panel A shows the results from non-conglomerate mergers. Panel B shows the results from conglomerate mergers . A non-conglomerate merger indicates a merger between two firms with the same two-digit SIC code (i .e . 4800) . A conglomerate merger indicates that either the acquirer or target firm has nontelecommunications two-digit SIC groupings . Non-conglomerate mergers have a median market value of $1,067 million while conglomerate mergers have a median market value of $4,409 million . A statistically significant difference at the 5% level is represented by an * .

(lln,) Y_"' , (R ,. v,d..,,

following the completion of merger while controlling for conglomerate (inter-industry) and non-conglomerate (intra-industry) merges . Average monthly abnormal returns (AR) are calculated as

Note : This table shows average monthly abnormal returns (AR,) and cumulative abnormal returns (CAR) for acquirer firm i for the 60 months

No .

Month

Panel A : Results from non-conglomerate mergers

Table 5 .

1.0

w

00 I

O

n

O

m n

p

0

b

140


underperformance of acquirers is worse in non-conglomerate mergers than in conglomerate mergers ." A plausible explanation for this pattern of poor performance in non-conglomerate mergers and superior performance in conglomerate mergers can be found in Ahtiala (2000) . Ahtiala argues that a high risk of relative price volatility will lead to diversification in the firm's business scope, while firms perceiving low risk will emphasize focus . Thus, for non-conglomerate mergers, acquirers tend to perceive low risk, but at the same time the long-run equity performance is less than that of their non-merging control firms . In Table 6 we present our results from a HPR analysis of acquiring firms based upon merger type . These results are consistent with our earlier findings that conglomerate-type merges tend to outperform non-conglomerate mergers . The non-conglomerate mergers demonstrate lower HPRs for the acquiring firms relative to their control firms for both the three and five-year estimation periods . The difference in HPRs between the acquiring firm and its control firm becomes more negative when we compare the longer, five-year estimates . Likewise, the wealth relative is 0 .923 for the three-year HPRs, but declines to 0 .775 when calculated using the five-year HPRs . The HPR results are dramatically reversed for the subsample of conglomerate mergers . The HPRs for the acquiring firms are much larger than those observed for their controls and the wealth relative ratios are in excess of one . Indeed, the wealth relative ratio is 1 .30 for the three-year HPRs, but rises to 1 .83 for the five-year HPRs .

CONCLUSIONS A number of previous studies such as Langetieg (1978), Asquith (1983), Magenheim and Miller (1988) and Agrawal, Jaffe and Mandelker (1992) report that acquiring firms experience significantly negative abnormal returns after an acquisition . Yet, none of these studies explicitly considers the special dynamics that are possible when a regulated firm is involved in a merger . Because these firms enjoy regulatory protection of their profit margins, it might be that the long-run performance of merged regulated firms differs from that of merged, but non-regulated firms . The results presented in this analysis of mergers in the telecommunications industry, however, are generally inconsistent with such a conclusion . We find for our aggregate sample that the acquiring firms underperform their size and industry-matched control firms . The various annual CARs are significantly negative . Shareholders of the acquiring firm suffer a wealth loss of nearly 20% over the five-year period following the merger . We obtain similar results when we examine HPRs over three and five year periods . Thus, our primary finding

Acquirer Firms %HPR

46 .12 73 .56

33 .09

58 .25 1 .304

0.923

Wealth Relative

0 .111

0 .508*


106.01

70.28

Acquirer Firms %HPR

12 .54

119 .51

1 .8305

0.775

Wealth Relative

Five Years Control Firms %HPR

0.000

0 .524*


where R, is the return on acquirer firm i on the tth day after the merger completion date . T is the number of days from the merger completion date (usually 756 days and 1260 days for the three-and five-year periods, respectively) . The wealth relative ratio is defined as the ratio of I plus the mean holding period return for the acquirer firms divided by 1 plus the mean control firm holding period returns . Panel A contains the results for non-conglomerate (intra-industry) mergers . Panel B shows the results from conglomerate (inter-industry) mergers . Non-conglomerate mergers are mergers between two firms with the same two-digit SIC code (i .e. 4800) . A conglomerate merger indicates that either the acquirer or the target firm has non-telecommunications two-digit SIC groupings . Non-conglomerate mergers have a median market value of $1,067 million while conglomerate mergers have a median market value of $4,409 million . A statistically significance difference from 0 .50 at the 5% level is represented by an * .

[fr

Note : The table shows average holding period returns (HPRs) for post-merger three and five years . Average holding period returns (HPRs) are calculated as (1/n) '_ (1+R,.,)-1]*100%

9

Panel B : Conglomerate merger results

63

Control Firms %HPR

Three Years

Long-run Stock Returns for Acquirer Firms Following the Completion of Non-Conglomerate and Conglomerate Mergers in the Telecommunications Industry Between 1990-1993 .

No . of Firms

Table 6.

Panel A : Non-conglomerate merger results

0

aa b

N a

142


of long-term underperformance for mergers involving telecommunications firms is consistent with much of the literature analyzing the post-merger profitability

of non-regulated firms . It is, however, inconsistent with the claim that the regulatory apparatus provides an insulation to corporate profitability and thereby enhances the long-run performance of mergers involving regulated firms .

We also examine two hypotheses regarding the influence of merger

characteristics on subsequent firm performance . The first concerns the size of the acquiring firm . This hypothesis contends that larger mergers are more likely

to generate economies of scale . Consequently, such mergers will enjoy

positive long-run performance . Our findings show that larger mergers

exhibit positive long-run performance, while mid-size and smaller mergers

underperform relative to their control firms . The source of the long-run

superior performance of larger mergers can not be determined from the current

empirical analysis, but certainly economies of scale and other production

efficiencies are more likely to be present .

The second hypothesis we examine involves an industry comparison of the

acquirer and the target . There are actually two variants of this second

hypothesis, but our sample design allows us to test both . The first variant of this hypothesis claims that mergers between regulated firms will exhibit greater

long-run profitability because of the protected profit margins of regulated firms . The second variant asserts that conglomerate type mergers between firms are

less profitable because of managerial inexperience in the target industry and

greater difficulty in achieving production economies of scale across different

industries . We partition our sample into two subsamples based on industry SIC . The first group consists of mergers where both the acquirer and the target possess the same 4800 SIC code . Thus, these mergers are mergers between two regulated firms in the same industry and are by definition, non-conglomerate mergers . The second subsample consists of mergers across SIC codes and is

necessarily conglomerate in nature . It is also an artifact of our subsample

creation that none of the targets in these mergers are regulated firms . Thus we can simultaneously test both variants of the industry effects hypothesis .

We find that conglomerate mergers exhibit superior long-run performance

relative to their control firms . The long-run performance of the nonconglomerate mergers is consistent with the overall sample results and

indicates significant underperformance . This result is also consistent with that reported by Agrawal, Jaffe and Mandelker (1992) for a sample of large mergers over the 1955-1987 period.

We conclude from this study that consolidation through merger in a

regulated industry like telecommunications will not necessarily generate the

increased profitability suggested at the time of the merger negotiations . Indeed,


143

in general such mergers are associated with a significant wealth loss to the shareholders of the acquiring firms . It does appear however, that larger mergers and mergers across industry groups can produce the promised profit enhancement. Consolidations may or may not make good public policy, but it is highly uncertain whether they make good business investments .

NOTES 1 . For instance AT&T purchased NCR Corp and 20% of Sun Microssystems, Inc in 1991 and then acquired Teradata Corp in 1992 . 2 . The topics of studies on long-term equity performance include initial public offering (IPO) stocks (Ritter, 1991), seasoned equity issues (Loughran & Ritter, 1995 ; Spiess & Affleck-Graves, 1995), venture capital IPOs (Hamao, Packer & Ritter, 1998), and convertible debt offerings (Kang, Kim & Stulz, 1999 ; Spiess & Affleck-Graves, 1999) . 3 . None of the targets for the conglomerate mergers was a regulated company .

REFERENCES Agrawal, A ., Jaffe, J ., & Mandelker, G . (1992) . The post-merger performance of acquiring firms : a re-examination of an anomaly . Journal of Finance, 47, 1605-1621 . Ahtiala, P. (2000) . Conglomerate mergers as defense against the risk of relative price variability . Review of Economics and Statistics, 82, 160-163 . Asquith, P . (1983) . Merger bids, uncertainty, and stockholder returns . Journal of Financial Economics, 11, 51-84. Barber, B ., & Lyon, J. D . (1996) . Detecting abnormal operating performance : the empirical power and specification of test statistics . Journal of Financial Economics, 41, 359-399 . Barber, B ., & Lyon, J . D . (1997). Detecting long-run abnormal stock returns : the empirical power and specification of test statistics . Journal of Financial Economics, 43, 341-372 . Conrad, J ., & Kaul, G . (1993) . Long-term market overreaction or biases in computed returns? Journal of Finance, 48, 39-63 . Hamao, Y ., Packer, F., & Ritter, J . (1998) . Institutional affiliation and the role of venture capital : evidence from IPOs in Japan. Staff Reports, 52, Federal Reserve Bank New York, New York, NY . Jensen, M. C . (1986) . Agency costs of free cash flow, corporate finance and takeovers . American Economic Review, 76, 323-329. Kang, J . K ., Kim, Y . C., & Stulz, R. (1999) .The underreaction hypothesis and the new issue puzzle : evidence from Japan . Review of Financial Studies, 12, 519-534 . Langetieg, T. (1978) . An application of a three factor performance index to measure shareholder gains from merger . Journal of Financial Economics, 6, 365-383 . Loderer, C ., & Martin, K . (1992) . Postacquisition performance of acquiring firms . Financial Management, 21, 69-79 . Loughran, T ., & Ritter, J . (1995) . The new issues puzzle . Journal of Finance, 50, 23-51 . Magenheim, E ., & Mueller, D . (1988) . Are acquiring firm shareholders better off after an acquisition? In : J. Coffee, L . Lowenstein & S . R. Ackerman (Eds), Knights, Raiders and Targets (Oxford University Press, Oxford, England), 171-193 .

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STEPHEN P. FERRIS AND KWANGWOO PARK

Ritter, J . (1991). The long-run performance of initial public offerings . Journal of Finance, 46, 3-27 . Spiess, D ., & Affleck-Graves, J . (1995) . Underperformance in long-run stock returns following seasoned equity offerings . Journal of Financial Economics, 38, 243-267 . Spiess, D ., & Affleck-Graves, J . (1999). The long-run performance of stock returns following debt offerings . Journal of Financial Economics, 54, 45-73 .

TAKEOVER STUDIES : TAKE NOTE OF THE SIZE AND THE AGE OF FIRMS IN YOUR SAMPLE Vijay Gondhalekar

ABSTRACT This study investigates the reasons for the disagreement among takeover studies on whether the return to bidders (short-term and long-term) is zero or negative . It documents that the two-day announcement-period bidder returns are inversely related to the size and age of targets, but positively related to the age of bidders . Conversely, five-year buy-hold returns (adjusted for size, market-to-book equity, and price momentum) if measured from the date of announcement are positively related to the size of targets, and if measured from the date of resolution are negatively related to the age of bidders . These and other findings are taken to suggest that if takeover studies impose different restrictions on the size and/or age of firms in the sample (and the study documents that they do), they could end up with contradictory findings about bidder returns .

Innovations in Investments and Corporate Finance, Volume 7, pages 145-162 . Copyright © 2002 by Elsevier Science Ltd. All rights of reproduction in any form reserved . ISBN : 0-7623-0897-4 145

1 46

VIJAY GONDHALEKAR

1 . INTRODUCTION Takeover studies unanimously report that the average abnormal return, at the announcement of takeovers, to the shareholders of targets is positive . However, studies report contradictory findings about returns to bidder . Some studies claim that the average announcement-period return to bidders is zero (e .g . Jensen & Ruback, 1983, review article ; Bradley, Desai & Kim, 1988 ; Mitchell & Lehn, 1990 ; Lang, Stulz & Walkling, 1989, 1991) . Other studies report that the average is reliably negative (e .g . Dodd, 1980; Varaiya, 1986 ; Smith & Kim, 1994) . Even when it comes to post-merger long-term return to bidders, some studies claim that the average is zero (e .g . Franks, Harris & Titman, 1991 ; Loughran & Vijh, 1997), while others claim that the average is negative (e .g . Asquith, 1988 ; Agrawal, Jaffe & Mandelker, 1992) . As returns to bidders are an important signal about the net present value of the takeovers to them, it is important to understand why studies end up with contradictory findings about this signal. No study has yet addressed this issue, and so I take a first step in that direction . A detailed examination of important takeover studies reveals the following . They use standard well-tested methodologies for computing abnormal returns associated with takeovers . However, due to the data requirements for addressing the questions posed in them, different studies mix data from different sources (e .g. CRSP, MERC tenderbase, Mergerstat, Dow Jones News, Compustat, Spectrum, I/B/E/S, etc) . and thereby often impose different restrictions on the size or age of bidders and targets in their samples (actual examples are provided in the next section) . Firm size and firm age (i .e . the duration for which shares have been seasoned in the market) carry important information about them . Firm size has a significant relationship with the expected return on stocks (e .g . Banz, 1981 ; Fama & French, 1992, 1993), the governance of firms (e .g . Demsetz & Lehn, 1985), the growth in earnings (see Fama & French, 1995), and the information asymmetry about firms (e .g . Arbel & Strebel, 1982 ; Zegal, 1985) . Similarly, the age of firms has a significant relationship with the profitability and asset growth of firms (see Fama & French, 2001), and with the information asymmetry about firms (e .g . Barry & Brown 1984, 1986) . The size and age of firms track various important characteristics/attributes of firms . Hence, biasing these variables in a sample could have significant impact on the findings . Studies on corporate takeovers are particularly vulnerable to this problem because two sets of firms are involved (bidders and targets) and generally large targets tend to be acquired by large bidders, while mature (older) bidders tend to acquire growth (younger/smaller) firms . Consequently, biasing

Takeover Studies

147

the size or age of one set of firms can easily bias the other set in some form . If bidder returns are related to the size and/or age of bidders or targets, one reason that studies disagree about the findings on bidder returns could be that they impose different constraints on the size/age of firms in their sample .' I examine the above possibility based on a sample that covers takeovers of Nasdaq targets by NYSE/AMEX/Nasdaq bidders during the period 1973-1992 . Unlike a sample that covers takeovers of NYSE/AMEX targets (which is very common in extant studies), this sample allows a cleaner comparability of age, at least across the targets, and a wider variation in the size and age of bidders . Short-term returns are taken to be the market-adjusted returns for the window (day-1, 0) around the date of takeover announcement in the Wall Street Journal (day 0) . Long-term returns are taken as the five-year buy-hold returns adjusted for size, market-to-book equity ratio, and price momentum of the bidder . Statistical inference about long-term bidder returns is based on the pseudo-portfolio method introduced by Brock, Lakonishok and LeBaron (1992) . I find that the average short-term return to bidders is significantly negative (-0 .45%) and that to the targets is positive (15%) . The average long-term return to bidders is not different from zero computed from the date of announcement (0 .06%) or from the date of resolution (0 .07%) . Cross-sectional regressions indicate that short-term bidder returns exhibit an inverse relationship with target size and target age, but a positive relationship with bidder age . Furthermore, other findings indicate that the age of targets carry information separate from that in target size about bidder returns . Long-term bidder returns from the announcement of takeovers exhibit a positive relationship with target size, while long-term bidder returns from the date of resolution exhibit an inverse relationship with bidder age . When the sample is split on the basis of positive vs . negative short-term returns, the average target size, target age, and bidder size are significantly lower for the former group than for the latter . The two groups, however, do not exhibit significant differences in target returns, takeover premium, asset market-to-book ratios of bidders and targets, and the leverage ratios of bidders and targets . A similar analysis using long-term returns indicates no difference in any of the above variables (there is weak evidence of difference in target age) . In short, the evidence in this study suggests two things . One, different studies, due to their data requirements, impose different restrictions on the size and/or age of bidders and targets in their sample . Two, empirically, not only are the short-term and long-term bidder returns related to the size or age of targets and bidders, but also the balance between the cases of bidders with positive vs . negative short-term bidder returns can be titled by biasing the size/age of firms in the sample . Taken together, the evidence supports the view that if takeover

1 48

VIJAY GONDHALEKAR

studies impose different restrictions on the size and/or age of firms in their sample, they could end up with significantly different findings on short-term and long-term bidder returns . In addition to providing an empirically backed explanation for the disagreement among takeover studies about the returns to bidders, the study makes two other important contributions to the literature on takeovers . First, while most extant studies examine acquisitions of NYSE/AMEX targets, this study documents returns to bidders from the acquisitions of Nasdaq targets . Second, in computing long-term bidder returns, the study not only takes into account the size and market-to-book equity ratio of the bidders, but unlike any of the prior studies, it also takes into account their price momentum . This is important because Lyon, Barber and Tsai (1999) empirically show that the use of size and market-to-book equity ratio alone in computing long-term returns can result in misspecified test statistics . The remainder of the study is organized as follows . A sampling of takeover samples used in prior studies is provided in Section 2 . Details about the data and the methodology are in Section 3 . The empirical findings are in Section 4 . The summary and conclusions are in Section 5 .

2. A SAMPLING OF TAKEOVER SAMPLES USED IN PRIOR STUDIES This section provides sample details from some of the prior takeovers studies . Many studies do not provide details about the size of firms in their samples, and none have provided data on the age of the firms in their samples . Consequently, it is impossible to pinpoint the bias in the size and age of firms introduced by the sample selection process . However, the fact that a bias is introduced still comes through clearly . The next paragraph covers some of the studies on short-term bidder returns around the announcement of takeovers followed by a paragraph on two studies that cover long-term returns to bidders . Varaiya (1986) uses the Kidder Peabody & Co database on mergers and acquisitions for the period 1976-1983 . This database covers only those cases in which the purchase price is at least $50 million . Plus, Varaiya requires that the targets be covered by the I/B/E/S database (which at that time covered only those firms that were of interest to the institutional investors) . This further curtails smaller/younger targets from the sample . Bradley, Desai and Kim BDK (1988) cover successful tender offers by NYSE/AMEX bidders for NYSE/AMEX targets during 1963-1984 . Fama and French (2001) report that new lists on NYSE/AMEX plunged after Nasdaq came on line in 1972 and that new lists tend to be near the median size of firms on their respective

Takeover Studies

149

exchanges . It is therefore difficult to gauge the restrictions on the size and age of firms in takeover studies such as BDK that cover both pre and post 1972 periods . Mitchell and Lehn ML (1990) examine the acquisitions during the period 1982-1986 undertaken by firms listed on Value Line during the fourth quarter of 1981 . They exclude from their sample the bidders belonging to the following industries : financial services, electric utilities, and all the industries with less than 10 firms . This restriction eliminates bidders (and perhaps targets) in young and mature industries . Lang, Stulz and Wakling LST (1989, 1991) combine data on tender offers from two sources for their sample - The Rochester MERC database for the period 1968-1980 and The Austin Tenderbase for the period 1980-1986 . They require that bidders have data on Compustat and Value Line . It is difficult to gauge the specific restrictions imposed by LST on the size and age of firms in their sample, but the findings they report indicate that 94% of bidders and targets in their sample had existed for at least five years before the takeover announcements . In other words, the age of firms was biased in their sample . While, Varaiya (1986) reports that the average announcementperiod return to bidders is negative, all others report that the average is zero . Agrawal, Jaffee and Mandelker AJM (1992), in their study on long-term returns to bidders, use a sample that covers takeovers of NYSE/AMEX targets by NYSE bidders during the period 1955-1987 . AJM eliminate AMEX bidders from their sample thereby curtailing 6% of the sample (no reason for doing this is given) . Loughran and Vijh (1997), in their study on long-term bidder returns, use a sample that covers takeovers of NYSE/AMEX/Nasdaq targets by NYSE/AMEX/Nasdaq bidders during the period 1970-1989 . This sample is likely to have smaller and younger bidders/targets compared to those in the AJM sample . The two studies report contradictory findings about average long-term post-merger returns to bidders . In short, different studies, because of their data sources and requirements, knowingly or unknowingly, impose different constraints on the size and/or age of firms in their samples .

3. DATA AND METHODOLOGY I chose a sample consisting of Nasdaq targets for two reasons . One, as most takeovers studies use a sample consisting of NYSE/AMEX targets, not much evidence exists on the takeovers of Nasdaq targets . Two, using a sample consisting of NYSE/AMEX targets automatically eliminates NASDAQ targets and bidders, i .e . introduces a bias against smaller and younger bidders and targets from being in the sample . Conversely, a sample consisting of Nasdaq targets allows bidders to be NYSE, AMEX or Nasdaq firms and therefore does

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not bias the size and age of at least the bidders . An ideal takeover sample would include NYSE/AMEX as well as Nasdaq targets . However, since NYSE/AMEX firms can overwhelm Nasdaq firms in size, such a sample has the potential of causing the results to be driven primarily by the takeovers involving NYSE/AMEX targets . For these reasons, I stick to a sample consisting only of Nasdaq targets . The starting sample consists of all the Nasdaq firms delisted from CRSP tapes due to mergers during the period December 14, 1971 to December 31, 1992 . A total of 1884 firms satisfy this criterion . I could find relevant information from the Wall Street Journal for only 915 targets out of the total of 1884 . The requirement that the bidders be NYSE/AMEX/Nasdaq firms, so that relevant and sufficient stock price information about the bidders would be available for analysis, curtails the final sample size to 395 takeovers . The primary reason for losing a significant portion of the beginning sample (nearly 75%) is that the targets, being Nasdaq firms, are often very small and so do not merit coverage by the financial press due to low interest by traders . Such a loss of sample size is very common in takeovers studies, especially those involving Nasdaq firms (see some of the studies that provide sample details, e .g . Martin, 1996 ; Rau & Vermaelen, 1996) . Thus, even without any stringent data requirements, the takeover sample looses a significant portion of small/young targets . This problem gets compounded when studies, perhaps due to the questions posed in them, need data or information from multiple sources . For example, when this study requires accounting data on the bidders/targets to compute the market-to-book ratio of assets, the leverage ratio, etc ., the sample size goes down even further (the resulting sample sizes are reported in the tables) . Standard market adjusted methodology is used for computing short-term bidder abnormal returns (day -1, 0) around the date of the first Wall Street Journal announcement of the takeovers (day 0) . For computing long-term (five-year) bidder returns (from the date of announcement or date of resolution), the study uses the methodology in Lyon, Barber and Tsai (1999) . Each month, all NYSE firms are sorted into size (market value of equity) deciles, and the smallest decile is further portioned into quintiles giving 14 portfolios . AMEX/Nasdaq firms are then placed into these portfolios according to their size. Each size portfolio is partitioned into quintiles based on their market-tobook equity ratio, and each of the resulting portfolios is further partitioned into three groups based on the price performance of stocks during the previous year . This gives a total of 210 portfolios (14 x 5 x 3) each month . Each bidder, based on the month of takeover announcement (or resolution), is then matched with all the non-bidder firms in the same size, market-to-book equity, and price

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performance portfolio . Five-year buy-hold abnormal returns are calculated for a bidder by benchmarking the bidder's return against the five-year buy-hold equally weighted return on the firms from the matching portfolio . If a bidder is delisted prior to the five-year period, the returns stop at the time of delistment and the same is done with the benchmark return . If a matching firm gets delisted prior to the bidder, the CRSP value-weighted return is spliced into the calculations from the date after the delistment . The benefits from computing long-term returns in this fashion are enumerated in Lyon, Barber and Tsai (1999) . Statistical inference about the average long-term bidder return is based on the pseudo-portfolio method introduced by Brock, Lakonishok and LeBaron (1992) . A pseudo-portfolio is constructed by replacing each bidder with one firm drawn with replacement from the matching portfolio . Five-year buy-hold abnormal return for this pseudo-portfolio is calculated by using the matching portfolio approach described earlier . This gives one observation, and this procedure is repeated 1000 times to come up with an empirical distribution of abnormal returns . This empirical distribution is used to formulate a p-value for the null hypothesis that the mean of the five-year buy-hold abnormal returns of the bidders is equal to the mean of the five-year buy-hold abnormal returns for the 1000 pseudo-portfolios .

4. EMPIRICAL FINDINGS Table 1 presents summary statistics on the announcement period abnormal returns, the size of firms, and the age of firms in the sample . There are two things that jump out from the findings in Table 1 . First, the size and age of firms involved in takeovers have gone up over time . 2 Second, the returns to bidders have declined over time . Even Bradley, Desai and Kim (1988) report a similar trend about the size of firms and the returns to bidders . Consistent with studies such as Dodd (1980), Varaiya (1986), and Smith and Kim (1994), this study finds that for the entire sample period 1973-1992 the average bidder return is reliably negative (-0 .46%, t-statistic -2 .8) . Thus, the findings in table 1 suggest that on average, at the announcement of takeovers, the shareholders of bidders are unhappy about the takeovers . Furthermore, the size and age of firms involved in takeovers has gone up over time, and the shareholders of bidders have taken an increasingly dim view about the takeovers . The findings on the average five-year buy-old abnormal returns to bidders are as follows (not included in tables), the average from the date of announcement is 0 .06% (p-value 0 .30, based on the pseudo-portfolio method), and the average is 0 .07% (p-value 0 .25, based on the pseudo-portfolio method) .

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Table 1. Summary Statistics on the Abnormal Returns, Size, and Age of Bidders and Targets in the Acquisitions of Nasdaq Targets by NYSE/AMEX/Nasdaq Bidders During 1972-1993 . Mean [Median] (t-statistic) n = sample size Sub - Periods Variable Bidder Size, $ mill Target Size, $ mill Bidder Age, days listed Target Age, days listed Bidder CAR (-1,0), % Target CAR (-1, 0), %

1973-1979

1980-1989

594 [212] 2184 [667] n = 110 n = 207 32 [16] 81 [37] n = 110 n = 207 3136 [36301 3536 [3785] n = 107 n = 206 1203 [1303] 1832 [1570] n = 110 n = 207 0 .22 [0.23] -0 .47 [-0 .34] (1 .0) (-2 .0) n n=110 n=207 16 .50 [10.84] 18 .59 [8 .74] (7 .2) ° (10 .3)' n=102 n=195

Full Period 1990-1992

1973-1992

3220 [1054] 1949 [585] 78 395 177 [81] 87 [33] 78 395 3635 [3298] 3447 [3622] 78 391 2300 [16651 1751 [14281 78 395 -1 .45 [-0.70] -0 .46 [-0.26] (-3 .4)' (-2.8) n n=78 n=395 16 .29 [12.44] 15 .09 [10.05] (7 .1)' (14 .2)'' n=74 n=371

Note : The sample covers acquisitions of Nasdaq targets by NYSE/AMEX/Nasdaq bidders during

the period December 14, 1972 to December 31, 1992 . Abnormal returns to shareholders of the targets and the bidder, at the announcement of takeovers, are estimated as the market-adjusted returns for the window (day - 1, 0) surrounding the first Wall Street Journal announcement (day 0) of the takeover . Value-weighted Nasdaq index is taken as the market proxy for target firms, while value-weighted NYSE/AMEX or Nasdaq index is taken as the market proxy for bidders on the basis of their exchange . a, b, and c indicate significance at the 1%, 5% and 10% level, respectively .

Size = market value of equity ($ million) 40 trading days prior to the day of announcement . Age = number of trading days for which a firm is listed on NYSE/AMEX or NASDAQ .

Thus, consistent with the findings reported by Loughran and Vijh (1997), this study finds that long-term bidder returns are not different from zero . Table 2 examines the cross-sectional relationship between bidders returns at the announcement of takeovers and the size and age of targets and bidders . Column 1 reiterates the finding in Table 1 that announcement-period bidder returns have declined over the years . More specifically, it indicates that the bidder returns have significantly declined since the latter half of the 1980s . Studies by Fama and French (1992, 1993) suggest that the expected returns on company stocks have a significant relationship with the size and the marketto-book equity ratio of the companies . Therefore, I also control for the size and

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2.

Regression of the Announcement-period Bidder Returns on the Size and Age of Bidders and Targets .

Dependant variable = Bidder CAR (- 1, 0) in percent . (1) Intercept Dummy (1975-79) Dummy (1980-84) Dummy (1985-89) Dummy (1990-92) Bidder Size

0 .967 (0.9) -0 .864 (-0 .8) -0.735 (-0.7) -2 .01 (-2.0) n -2.43 (-2,3) 1

Bidder P/E

(2) 1 .506 (1 .4) -0.693 (-0.7) -0 .547 (-0.5) -1 .704 (-1 .6) -2 .165 (-2 .0) 1 -0 .119 (-1 .2) 0 .003 (0 .8)

Bidder Age x 1000 Target Size Target Age x 1000 Sample Size Adjusted R2 F Statistic

(3) 1 .557 (1 .4) -0.856 (-0.8) -0.746 (-0.7) -1 .811

(-1 .7) ` 2.271 (-1 .9)1 -0 .203 (-1 .9) < 0 .002 (0 .5) 0 .175 (1 .8)°

391 0 .04 5 .0

391 0 .04 3 .6

391 0 .05 3 .6

(4) 2 .080 (1 .9) -0 .863 (-0 .8) -0 .489 (-0 .5) -1 .475 (-1 .4) -1 .813 (-1 .7) ` 0.02 (0.2) 0 .003 (0.7)

-0.427 (-2.8) °

391 0 .06 4 .3

(5)

(6)

(7)

1 .623 (1 .5) -0.384 (-0.4) -0.097 (-0.1) -1 .196 (-1 .1) -1 .531

2 .090 (1 .9) C

2 .108 (1 .9)C

-0 .604 (-0 .6) -0.154 (-0.1) -1 .119 (-1 .0) -1 .377

-0.745 (-0 .7) -0.337 (-0.3) -1 .233 (-1 .2) -1 .498

(-1 .2) -0 .117 (-1 .2) 0.003 (0 .9)

(-1 .3) -0.000 (-0.0) 0.003 (0.8)

(-1 .3) -0.08 (-0.6) 0.002 (0.5) 0.148

-0.333 (-2 .3) 1 391 0 .05 3 .9

-0.368 (-2.4) ° -0.254 (-1 .7)' 391 0 .06 4 .1

(1 .7)`C -0.351 (-2.2) -0.245 (-1 .7) 391 0.06 4 .0

Note: The sample covers takeovers of Nasdaq targets by NYSE/AMEX/Nasdaq bidders during the period December 14, 1972 - December 31, 1992 . Abnormal returns to the bidders, at the announcement of takeovers, are estimated as the market-adjusted returns for the two-day window (-1, 0) surrounding the first Wall Street Journal announcements (day 0) regarding the takeovers . Value-weighted NYSE/AMEX or Nasdaq index is used as the market proxy according to the exchange of the bidder. a, b, and c indicate significance at the 1%, 5% and 10% level, respectively . Intercept = represents the mean bidder CAR (-1, 0) for the period 1972-1974 . Dummy (period) = 1 if the takeover announcement (day 0) falls within that period, otherwise = 0 . Size = log(ME), where ME stands for the market value of equity 40 trading days prior to the takeover announcement (day 0). P/E ratio = price/earnings ratio on the fiscal year end prior to the date of takeover announcement . Age = the number of trading days for which a firm is listed on NYES/AMEX or Nasdaq .

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P/E ratio of the bidders in the other regressions of Table 2 (see columns 2 through 7) . 3 The findings indicate that announcement-period bidder returns are inversely related to both the size and the age of targets (see columns 4, 5, 6, and 7), but are positively related to the age of bidders (columns 3 and 7) . More specifically, the findings indicate that for a 1% increase in the size of targets the returns to bidders decrease by about 0 .4%, and for a 1000 day increase in the age of targets the returns to bidders decrease by about 0 .3% . Conversely, if the age of the bidder increases by 1000 day, the returns increase by about 0 .15% . Columns 5, 6, and 7 indicate that the differences in bidder returns across time observed in equation-1 vanish completely if we control for the variation in the size and age of bidders and targets (the age of target seems the be the one that has the most influence on the differences observed across time) . Thus, the findings in Table 2 suggest that if studies cover different time periods or even if they cover the same time period but impose different restrictions on the size and age of firms in their sample, they could end up with significantly different findings about the returns to bidders at the announcement of takeovers . Fama and French (2001) report that the size of the NYSE, AMEX, and Nasdaq newly listed firm tends to be near the median value for the respective exchange . In other words, newly listed firms are not necessarily small in size . Consequently, even though the announcement-period bidder returns are inversely related to both the size and the age of targets, the information carried by the size of targets about the bidder returns may be different from that carried by the age of targets . Table 3 examines this issue by running the following three regressions . First, residuals are gathered from the regression of the announcement-period bidder returns on the size of targets . The residuals represent bidder returns purified of the influence of target size . Second, residuals are gathered from the regression of target age on target size . These residuals represent target age purified of the influence of target size . Finally, the announcement-period bidder returns purified of the influence of target size (residuals from the first step) are regressed on the age of targets that have been purified of the influence of the size of targets . As the slope in this regression is significant (-0 .02, t-statistic -1 .9), it indicates that the age of targets carries information about the announcement-period return to bidders over and above that in the size of targets . This finding suggests that takeover studies should be careful in imposing any restrictions on the size and/or the age of the targets in their samples, else their findings on announcement-period bidder returns could disagree with those in other studies .' Next, the study presents evidence on the relationship between the long-term bidder returns and the size and age of bidders and targets . The findings

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Table 3. Regression of Bidder Returns Purified of the Influence of Target Size on the Listing-age of Targets Purified of the Influence of Target Size . STEP 1 Bidder CAR (-1, O)j

=

1 .75 (3 .6)

=

817 .90 (5 .0)

_

-1 .56 E-16 (0 .00)

R'= 0 .053 Sample Size = 395 STEP 2 Target Listing-Age s R'= 0.089 Sample Size = 395 STEP 3 0, R2 = 0.01 Sample Size = 395

+

0 .59 Size of Target] (-4 .7)

+

Oi

265 .38 Size of Targets (6 .2)

+

>

0.002 >;, (-1 .9)

+

crw

i

Note : The sample covers the acquisitions of NASDAQ targets by NYSE/AMEX/Nasdaq bidders during the period December 14, 1972 - December 31 . 1992 . The abnormal returns to the bidders, at the announcement of takeovers, are estimated as the market-adjusted returns for the window (-1, 0) surrounding the first Wall Street Journal announcement of the takeover (day 0). Valueweighted NYSE/AMEX or Nasdaq index is used as the market proxy according to the exchange of the bidder. In Step 1, bidder abnormal returns, at the announcement of takeovers, are regressed against the size of targets. The residuals in this regression provide the bidder abnormal returns purified of the influence of target size . In Step 2, the listing-age of targets is regressed against the size of targets . The residuals in this regression provide the listing-age of targets purified of the influence of target size . Finally, in Step 3, the residuals from Step I are regressed against the residuals from Step 2 . Size of Target = log(ME), ME is the market value of equity 40 trading days prior to takeover announcement (day 0) . Target Listing-Age = number of trading days target is listed on NASDAQ .

pertaining to long-term returns to bidders measured from the date of announcement are in Table 4, while the findings on long-term returns measured from the date of resolution are in Table 5 . The long-term bidder returns are estimated after taking into account the differences in the size of bidders, the market-to-book equity ratio of bidders, and the price momentum of bidders during the prior year . Perhaps because of this reason, the size as well as the P/E ratio of bidders do not enter any of the regression in Tables 4 and 5 with significance and so the regressions were ran without including these variables .

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VIJAY GONDHALEKAR

Table 4. Regressions of the Five-year Buy and Hold Returns to Bidders from the Date of Announcement on the Size and Age of Bidders and Targets . Dependant variable = Five-Year Bidder Abnormal Return (AR) from Announcement (1) Intercept Dummy (1985-89) Dummy (1990-92) Target Size

0.198 (1 .3) -0.132 (-0.6) -0.113 (-0.6)

Target Age x 1000 Bidder Age x 1000 Sample Size Adjusted R 2 F Statistic

187 0 .00 0 .2

(2)

(3)

(4)

(5)

(6)

-0 .430 (-1 .5) -0 .288 (-1 .4) -0 .293 (-1 .3) 0.189 (2.6) a

0 .168 (0 .9) -0 .138 (-0 .7) -0 .123 (-0 .5)

0 .136 (0 .6) -0 .137 (-0 .7) -0 .116 (-0.5)

-0 .470 (-1 .5) -0 .291 (-1 .4) -0 .294 (-1 .3) 0 .188

-0 .409 (-1 .4) -0 .287 (-1 .4) -0 .285 (-1 .2) 0 .198

187 0 .02 2 .4

(2 .6)-

0 .018 (0 .3) 0 .017 (0 .4)

0.012 (0.3)

187 0.00 0.2

187 0 .00 0.2

187 0 .02 1 .8

(2 .6)

(7) -0.444 (-1 .4) -0.289 (-1 .4) -0.286 (-1 .2) 0 .196 (2 .6) a

-0 .030 (-0 .5) 0 .010 (0 .3)

-0 .028 (-0 .4)

187 0 .02 1 .9

187 0 .01 1 .5

Note: The sample covers takeovers of NASDAQ targets by NYSE/AMEX/Nasdaq bidders during the period January 1, 1980 - December 31, 1992 . The abnormal returns to a bidder is computed as the difference between the five-year buy-hold return from the month of takeover announcement to the bidder and the same period return on a portfolio matched on the basis of the size, marketto-book ratio of assets, and the price momentum of the bidder . a, b, and c indicate significance at the 1%, 5% and 10% level, respectively . Five-Year AR = the difference between the five-year buy-and-hold return to a bidder from the date of takeover announcement and the EW return for the same period on a portfolio matched by the size, market-to-book ratio of assets, and the price momentum of the bidder (i .e . price performance during the year prior to the takeover announcement) . Intercept = represents the mean five-year buy-hold abnormal return from the date of announcement to bidders that initiated takeovers during the period 1980-1984 . Dummy (period) = 1 if the takeover resolution date falls within that period, otherwise = 0 . Size = log(ME), where ME stands for the market value of equity 40 trading days prior to the takeover announcement (day 0) . Age = the number of trading days for which a firm is listed on NYSE, AMEX or Nasdaq .

As both Table 4 and Table 5 use long-term bidder returns as the dependant variable, the findings are discussed together. The long-term bidder returns (from date of announcement) are positively related to the size of targets (Table 4, see columns 2, 5, 6, and 7), and the long-term returns (from the date of

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Table 5.

Regressions of the Five-year Post-merger Buy and Hold Returns to Bidders on the Size and Age of Bidders and Targets .

Dependant variable = Five-Year Bidder Abnormal Return (AR) from date of Resolution

Intercept Dummy (1985-89) Dummy (1990-92) Target Size

(1)

(2)

(3)

(4)

(5)

(6)

0 .067 (0 .5) 0 .048 (0 .2) -0 .203 (-0 .9)

0 .258 (0 .9) 0 .094 (0 .5) -0 .149 (-0 .7) -0 .057 (-0 .8)

-0 .083 (-0 .5) 0.027 (0.1) -0 .247 (-1 .1)

0 .136 (0 .6) -0.137 (-0.7) -0 .116 (-0 .5)

0 .507 (1 .6) 0 .110 (0 .5) -0.130 (-0 .6) -0 .051 (-0 .7)

-0 .077 (-2 .1) e

-0 .075 (-2 .0) 1

0 .164 (0 .6) 0 .089 (0 .4) -0 .178 (-0 .8) -0 .083 (-1 .1) 0 .106 (1 .6)

191 1 .4 1 .9

191 1 .1 1 .6

Target Age x 1000 Bidder Age x 1000 Sample Size Adjusted R 2 F Statistic

191 0 .00 0 .7

191 0 .00 0 .7

0 .088 (1 .3)

191 0 .01 1 .1

191 0.3 1 .1

(7) 0 .407 (1 .3) 0 .1059 (0 .5) -0 .156 (-0 .7) -0 .073 (-1 .0) 0 .088 (1 .3) -0 .068 (-1 .8) 191 1 .5 1 .6

Note : The sample covers takeovers of NASDAQ targets by NYSE/AMEX/Nasdaq bidders during the period January 1, 1980 - December 31, 1992 . The abnormal returns to a bidder is computed as the difference between the five-year buy-hold return from the month of takeover announcement to the bidder and the same period return on a portfolio matched on the basis of the size, market-to-book ratio of assets, and the price momentum of the bidder . a, b, and c indicate significance at the 1%, 5% and 10% level, respectively . Five-Year AR = the difference between the five-year buy-and-hold return to a bidder from the date of takeover announcement and the EW return for the same period on a portfolio matched by the size, market-to-book ratio of assets, and the price momentum of the bidder (i .e . price performance during the year prior to the takeover announcement) . Intercept = represents the mean five-year buy-hold post-merger abnormal return to bidders that initiated takeovers during the period 1980-1984 . Dummy (period) = 1 if the takeover resolution date falls within that period, otherwise = 0 . Size = log(ME), where ME stands for the market value of equity 40 trading days prior to the takeover announcement (day 0) . Age = the number of trading days for which a firm is listed on NYSE, AMEX or Nasdaq .

resolution) are negatively related to age of bidders (Table 5, see columns 4, 5, and 7) . These findings are notable for two reasons . One, even long-term bidder returns exhibit significant relationships with the size of targets or the age of bidders, and so the findings suggest that if takeover studies impose different

1 58

WAY GONDHALEKAR

restrictions on these variables in their sample due to data requirement, etc ., they could end up with contradictory findings about long-term bidder returns . Two, as you will recall from Table 2, short-term bidder returns are negatively related to the size of targets and positively related to the age of bidders . In other words, at the announcement of takeovers, the market responds less favorably towards bidders that are younger (compared to those that are older) and to those that acquire larger targets (compared to those that acquire smaller targets), but then makes an about face over the long haul . This finding adds to the growing evidence on similar market behavior in other events documented by other studies . Though such findings raise questions about market efficiency, at least one researcher has taken strong objections to interpreting the findings as indicative of market inefficiency (see Fama, 1998) . In Table 6, the full sample is split on the basis of positive vs . negative announcement-period bidder abnormal returns . The split-samples are then used for testing the difference of means (by using a parametric and a nonparametric test) for the size, the listing-age and some of the other variables of bidders/targets suggested by prior literature as having influence over the abnormal returns to bidders at the announcement of takeovers . There are three notable findings in this table . One, the mean abnormal return to bidders is 2% for the group consisting of cases where the bidders experience positive abnormal returns (sample size 176) and the mean abnormal return to the bidders is -2 .5% for the group consisting of bidders that experience negative abnormal returns at the announcement of takeovers (sample size 219), and the difference between the two means is statistically significant . Two, the means are also different for the size of bidders and targets and for the age of targets . More specifically, the mean size and the age of targets, when the announcement period bidder returns are positive, are $63 .6 million and 1582 trading days respectively, and these numbers are $97 .5 million and 1887 trading days respectively when the bidder returns are negative . In other words, the average target tends to be smaller and younger when the announcement period bidder returns are positive than when they are negative . Three, the means for the split samples are not different for the following variables : the abnormal returns to targets, the premium paid in cash takeovers (which is used as proxy for the probability of success of bids at the announcement of takeovers), the market-to-book ratio of assets of bidders and targets and the leverage ratio of bidders and targets . Though the mean bidder size for the split samples is significantly different, since bidder size has not been significant in any of the cross-sectional regressions (see Table 2), the difference arises perhaps because of the fact that larger targets tend to be acquired by larger bidders . In any case, the findings of Table 6, like the

159

Takeover Studies

Table 6. Differences in Bidder and Target Attributes if Takeovers are Split on the Basis of Positive vs . Negative Abnormal return to Bidders at the Announcement of Takeovers . Variable

Bidder CAR (-1, 0) < 0 Mean (Sample Size)

Bidder CAR(-1, 0),% Target CAR(-1, 0),% Premium Paid,% Bidder Size, $m Bidder Listing-Age Bidder MB Ratio Bidder Debt/Assets Target Size, $m Target Listing-Age Target MB Ratio Target Debt/Assets

-2 .5 14 .6 68 .9 2321 .1 3422 8 .59 0 .54 97 .5 1887 1 .72 0 .48

(219) (219) (128) (219) (219) (125) (125) (219) (219) (133) (133)

Bidder CAR (-1, 0) > 0 Mean (Sample Size) 2.0 13 .5 63 .2 1455 .1 3447 2 .40 0 .54 63.6 1582 1 .93 0 .46

Parametric T-Test

(176) (176) (114) (176) (176) (110) (110) (176) (176) (115) (115)

17 .95 0 .57 0 .74 2.01" 0 .12 1 .01 0 .10 2 .63' 2 .55' 0 .61 0 .66

MannWhitney Z-Statistic 17 .08' 0 .50 0 .32 2 .48' 0.44 0.72 0 .19 2 .78' 2 .03' 0 .04 0.50

Note : The sample covers takeovers of Nasdaq targets by NYSE/AMEX/Nasdaq bidders during the period December 14, 1972 - December 31, 1992. Abnormal returns to bidding firm shareholders, at the announcement of takeovers, are estimated as the market-adjusted returns for the two-day window (-1, 0) surrounding the first Wall Street Journal announcements (day 0) regarding the takeovers . Value-weighted NYSE/AMEX or Nasdaq index is used as the market proxy according to the exchange of the bidder . The differences in means values of various attributes of bidders and targets are examined on the basis of the parametric t-test (assuming unequal variances) and the non-parametric Mann-Whitney test . a, b, and c represent significance levels of 1%, 5%, and 10% respectively . Size = market value of equity, 40 trading days prior to the date of first Wall Street Journal announcement of the takeover (day 0) . Listing-Age = number of trading days a firm has been listed on the NYSE, AMEX or Nasdaq . Premium Paid = percentage difference between the price of the target 40 trading days prior to the takeover announcement and the final price paid by the bidder (cash offers only) . MB Ratio = market-to-book ratio of assets ; (MV of Equity + BV of Debt)/(BV of Assets) . Debt/Assets = BV of Debt/(MV of Equity+BV of Debt) .

findings in Tables 2 and 3, suggest that biasing the size and/or age of targets is likely to have a significant impact on the findings about the abnormal returns to bidders at the announcement of takeovers . Analysis similar to that in Table 6, but using long-term bidder returns instead of short-term bidders returns proved to be futile since none of the variable turned out to be significantly different across the two groups (there is weak evidence that the mean target age is different in the two groups) .

160

VUAY GONDHALEKAR

5. SUMMARY AND CONCLUSIONS Extant studies disagree on whether the average abnormal bidder return

(short-term and long-term) in pursuing takeovers is zero or negative . A detailed examination of prior studies reveals that they use standard

methodologies for computing abnormal returns, but because of meshing up data from a myriad of different sources, they impose different restrictions on the size and age of firms (bidders/targets) in their sample . The study, therefore, examines the relationship between bidder returns and the size and age of bidders and targets .

Based on a sample that covers takeovers of Nasdaq targets by NYSE/AMEX/

Nasdaq bidders during 1973-1992, this study finds that the average short-term

(day -1, 0) bidder abnormal return around takeover announcement is negative . The long-term (five-year buy-hold) average abnormal return from the date of announcement or from the date of resolution (after making appropriate

adjustment for the size, market-to-book equity, and price momentum of bidders) is zero . More importantly, the study finds that, cross-sectionally, the bidder returns (short-term and long-term) are related to the size and/or age of bidders

or targets . Furthermore, the study finds that the average size and/or age of firms are significantly different in takeovers that result in positive short-term bidder

returns than in those that result in negative short-term bidder returns . Therefore, the findings suggest that if takeover studies knowingly or unknowingly impose

different restrictions on the size and/or age of firms in the sample (and they do), then they may end up with contradictory findings about takeovers . Though the findings of this study pertain only to takeovers, they have much broader

implications . They draw attention to the fact that if we impose different

restrictions on the trees in the sample, we may end up with contradictory findings about the forest .

NOTES 1 . Though takeovers studies report that bidder returns are related to the relative size of firms (see the review of variables that affect bidder returns in Lang, Stulz & Walking, 1991), there is hardly any evidence on the relationship of bidder returns with the size of bidders or targets and none whatsoever on the relationship with the age of firms . 2 . Though for the sake of comparison with earlier studies, the size of firms is reported in nominal terms, the study also finds that even in rela terms (constant 1992 dollars) the size of bidders and targets has increased over the years (results not in the table, but available from the author) . 3 . The data on Price/Earnings ratio (gathered from the Standard and Poor's Stock Guides) was available for the most recent quarter prior to the takeover announcement

16 1

Takeover Studies

and so was used instead of the market-to-book equity ratio which was available consistently only for the fiscal year end prior to the takeover announcement . Also, using P/E ratio instead of the market-to-book equity ratio was helpful in retaining the size of the sample. In any case, the qualitative findings and the inferences drawn do not chage even when the market-to-book equity ratio is used instead on the P/E ratio in all the regressions of Table 2 . 4. The evidence and implication of Table 3 may seem obvious, but it is easy to get overlooked . For example, Lang, Stulz and Walking LST (1989) investigate the relationship of announcement-period bidder returns and the Tobin's Q ratio of the bidders and targets . Somehow their data requirements are such that over 94% of targets in their sample have existed for at least five years . In other words, their data requirement has introduced a bias in the age of targets in their sample . It would not be surprising if some other study that does not bias the age of targets as in LST end up with findings that do not match those in LST) .

ACKNOWLEDGMENTS I gratefully acknowledge helpful comments and suggestions from the seminar participants at the University of Pittsburgh, PA, especially from Anil Makhija and James Kenkle . I thank Tomas Jandik and Jonathan Clarke for computing assistance and Robert Barnett for editorial suggestions . The usual disclaimer applies .

REFERENCES Asquith, P . (1983). Merger bids, uncertainty and stockholder returns . Journal of Financial Economics, 11, 51-83 . Agrawal, A ., Jaffee, J ., & Mandelker, G . (1992) . The post-merger performance of acquiring firms : a re-examination of an anomaly . Journal of Finance, 47(4), 1605-1621 . Arbel, A ., & Strebel, P . (1982) . The neglected and small firm effects . The Financial Review, 17, 201-218 . Banz, R . (1981) . The relationship between return and market value of common stocks . Journal of Financial Economics, 9, 3-18. Barber, B ., & Lyon, J . (1997) . Detecting long-run abnormal stock returns : the empirical power and specification of test statistics . Journal of Financial Economics, 43, 341-372. Barry, C ., & Brown, S . (1984) . Differential information and the small firm effect . Journal of Financial Economics, 13, 283-294 . Barry, C., & Brown, S . (1986). Limited information as a source of risk. The Journal of Portfolio Management, (Winter), 66-72. Bradley, M ., Desai, A ., & Kim, E . (1988) . Synergistic gains from corporate acquisitions and their division between the stockholders of target and acquiring firms . Journal of Financial Economics, 21, 3-40. Brock, W., Lakonishok, J., & LeBaron, B . (1992) . Simple technical trading rules and the stochastic properties of stock returns . Journal of Finance, 47, 1731-1764. Demsetz, H., & Lehn, K. (1985) . The structure of corporate ownership : causes and consequences . Journal of Political Economy, 93(6), 1155-1177 .

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Dodd, P. (1980), Merger proposals, management discretion and stockholder wealth . Journal of Financial Economics, 8, 105-138. Fama, E. (1998). Market efficiency, long-term returns and behavioral finance. Journal of Financial Economics, 49, 283-306. Fama, E ., & French, K . (1992) . The cross section of expected stock returns . Journal of Finance, 47, 427-465 . Fama, E ., & French, K . (1993) . Common risk factors in the returns on stocks and bonds . Journal • Financial Economics, 33, 3-56. Fama, E ., & French, K. (1995) . Size and book-to-market factors in earnings and returns . Journal • Finance, 50, 131-156 . Fama, E ., & French, K. (2001) . Newly listed firms : fundamentals, survival rates, and returns . University of Chicago Working Paper No . 530 . Franks, J ., Harris, R ., & Titman, S . (1991) . The post-merger share price performance of acquiring firms . Journal of Financial Economics, 29, 81-96 . Jensen, M ., & Ruback, R. (1983) . The market for corporate control : The scientific evidence . Journal • Financial Economics, 11, 5-50. Kothari, S ., & Warner, J . (1997). Measuring long-horizon security price performance . Journal of Financial Economics, 43, 301-340. Lang, L ., Stulz, R ., & Walkling, R. (1989) . Managerial performance, Tobin's Q and the gains from successful tender offers . Journal of Financial Economics, 24, 137-154. Lang, L ., Stulz, R ., & Walkling, R . (1991) . A test of free cash flow hypothesis : The case of bidder returns . Journal of Financial Economics, 29, 315-336 . Loughran, T ., & Vijh, A . (1997) . Do long-term shareholders benefit from corporate acquisitions? Journal of Finance, 52(5), 1765-1790 . Lyon, J., Barber, B ., & Tsai, C . (1998) . Improved methods for tests of long-run abnormal stock returns . Journal of Finance, 54, 165-202. Martin, K . (1996) . The method of payment in corporate acquisitions, investment opportunities, and managerial ownership . Journal of Finance, 51, 1227-1246 . Mitchell, M ., & Lehn, K. (1990) . Do bad bidders become good targets? Journal of Political Economy, 98, 372-398. Rau, R ., & Vermaelen, T. (1996) . Glamour, value and the post-acquisition performance of acquiring firms . Working paper, INSEAD . Smith, R., & Kim, J. (1994) . The combined effect of free cash flow and financial slack on bidder and target stock. Journal of Business, 281-310. Varaiya, N . (1987) . An empirical investigation of the bidding firms' gains from corporate takeovers. Research in Finance, 149-178 . Zegal, D . (1984) . Firm size and the information content of financial statements . Journal of Financial and Quantitative Analysis, 19, 299-310 .

DIVIDEND SMOOTHING AND

THE CROSS-SECTIONAL DETERMINANTS OF CORPORATE PAYOUT POLICY Gurmeet S . Bhabra, Jinho Jeong and John G. Powell

ABSTRACT This study examines the underlying factors which influence and cross-sectionally explain differences in the degree of dividend smoothing of firms . Differences in corporate dividend smoothing are documented by estimating the sensitivity of corporations' dividend payout ratios to changes in earnings . Theoretical determinants of dividend smoothing are investigated by cross-sectionally regressing the degree of dividend smoothing of firms against firm characteristics . The results show that riskier firms and smaller firms are more likely to smooth dividends . The empirical relationship between dividend smoothing and firm characteristics is much more significant for high growth firms, and varies considerably amongst sub-groups of the data that differ with respect to firm risk .

Innovations in Investments and Corporate Finance, Volume 7, pages 163-183 . Copyright © 2002 by Elsevier Science Ltd. All rights of reproduction in any form reserved . ISBN : 0-7623-0897-4 163

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GURMEET S . BHABRA, JINHO JEONG AND JOHN G. POWELL

INTRODUCTION Dividend smoothing theory explains why many firms smooth dividends relative to earnings, especially when earnings fall, but the diversity of dividend policies chosen by firms also tends to indicate that the dividend smoothing decision is considerably more important for some firms than for others . This paper empirically establishes the diversity of dividend smoothing behaviour chosen by dividend-paying firms and examines hypotheses developed from the dividend smoothing theory and information asymmetry literatures to explain measured differences in the extent to which firms smooth their dividends . The paper's results strongly support theoretical hypothesis which imply that small firms and firms with high earnings variability have a strong informational incentive to smooth dividends . The dividend smoothing literature has its roots in Lintner's (1956) finding that corporate managers prefer to pay stable dividends . Subsequent studies have shown the presence of dividend smoothing at the individual firm level and at

the aggregate, economy-wide level (Fama & Babiak, 1968 ; Laub, 1972 ; Lee, Djarraya & Wu, 1987 ; Marsh & Merton, 1987 ; Garrett & Priestley, 2000) . Dividend signalling models, starting with Bhatacharya (1979), explain why firms choose to smooth dividends and are reluctant to cut them . Stable dividends are hypothesized to signal management's view that underlying permanent earnings are less volatile than temporary earnings, whereas a dividend cut signals that a corresponding earnings decline is permanent, so firms with superior anticipated earnings profiles signal this information by paying and maintaining a higher, stable dividend level (Aivazian, Booth & Cleary, 2000) . John and William's (1985) signalling explanation of dividend smoothing extended the theoretical development of the dividend smoothing hypothesis by showing that, in equilibrium, the optimal dividend policy for firms with greater information asymmetry is a higher degree of dividend smoothing relative to the firm's stock price . Other theoretical models of dividend smoothing include Kumar's (1988) firm risk model of dividends (1988), John and Nachman's (1987) financial slack explanation of smoothed dividends, and Rozeff's (1982) firm growth explanation of dividend smoothing choices . A number of firm-specific factors play an important role in dividend signalling and agency cost explanations of dividend smoothing, thus implying that the absence of these factors in some firms and their strong presence in others could empirically explain cross-sectional differences in firms' dividend smoothing behaviour . Recent empirical studies of aggregate dividend behaviour and large dividend changes tend to support dividend smoothing theory, but have not focused on this paper's goal of using theoretical hypothesis concerning firm-

Dividend Smoothing and Cross-sectional Determinants

165

specific factors to cross-sectionally explain differences in firms' dividend smoothing behaviour (Sarig, 1999 ; Aivazian et al ., 2000 ; Garrett & Priestley, 2000 ; Klock & Maxwell, 2000) . The degree of dividend smoothing of firms is estimated in this paper using a parameter which measures the sensitivity of corporations' dividend payout ratios to changes in earnings . This measure is closely related to Lintner's smoothing parameter, since firms that choose to smooth dividends relative to earnings will have dividend payout ratios that move in the opposite direction of changes in earnings . The Lintner dividend smoothing parameter and the sensitivity of firms' payout ratios to changes in earnings are both estimated in the paper, and are highly correlated . The results of the paper focus on the sensitivity measure because of the greater empirical significance with which it captures the diversity of firms' dividend smoothing choices, thus facilitating the cross-sectional examination of theoretical explanations of the degree of dividend smoothing amongst firms . The paper's results indicate that company risk, size, and debt structure play important roles in the empirical explanation of the cross-section of dividend smoothing behaviour, as theoretical models of dividends would suggest . Factors that explain cross-sectional differences in dividend smoothing appear to be more relevant for companies with strong growth opportunities, a finding that is important because it helps to indicate the conditions under which financial managers have to be especially attentive to dividend policy and dividend smoothing choices . The results also indicate that the power with which firm-specific variables explain dividend smoothing behaviour varies considerably amongst sub-groups of the data that differ with respect to the information environment, including firm risk sub-groups . This finding is important because it emphasizes the theoretical importance of risk in dividend policy choice as well as the theoretical implication that the information environment is the primary driver of the value of information signals . The following section introduces measures of dividend smoothing, including the Lintner (1956) dividend adjustment factor as well as the sensitivity of corporations' dividend payout ratios to changes in earnings . Theoretical hypothesis concerning firm-specific factors that influence dividend smoothing choices are then introduced in order to explain differences in firms' dividend smoothing choices . The third section provides estimates of firms' dividend smoothing measures as well as results for the cross-sectional explanation of differences in firms' measured dividend smoothing behaviour . A brief concluding section highlights factors that are found to have an important influence on dividend smoothing choices .

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GURMEET S . BHABRA, JINHO JEONG AND JOHN G . POWELL

DIVIDEND SMOOTHING Measures of Dividend Smoothing Lintner (1956) found that corporate managers prefer to pay stable dividends, and explained firms' decisions to smooth dividends as an outcome of a policy of slowly adjusting the ratio of dividends to earnings towards a long run target payout ratio . Lintner modelled the change in corporation i's time t dividend per share, ADO, as being proportionate to a speed of adjustment factor c, times the amount by which last period's dividend is exceeded by the current desired payout (the desired payout ratio r, times earnings per share EPS; ) : AD; ,, = a ; + c ; (r; EPS;,-D;,,_i) + u ;,

(1)

where a, is a constant and a;, is a normally distributed random error term . Lintner (1956) tested a rearranged version of Eq . (1) on a sample of 26 firms using annual data for the time period 1918 to 1941, and found that the model explained 85% of the variation in dividends . He found that the speed of adjustment averaged 30% per year, and the target payout ratio averaged 50% of earnings . Lintner's model provides a measure of the extent to which dividends are smoothed relative to changes in earnings . Appropriate substitution in Eq . (1) reveals that the measure of the degree of smoothing of dividends relative to a change in earnings is given by the speed of adjustment parameter (c) times the target payout ratio (r) : 2 + a ; (1-c)+ u0-c,u ;,,_, OD;, = c ; r, (EPS;,-c ; EPS;,_)-c; (1-c;)D it

(2)

The speed of adjustment and target payout ratio parameters provide a dividend smoothing measure because, taken together, they provide an indication of the level of the change in dividends that occurs in the short run in response to a change in the level of earnings . A speed of adjustment parameter c ; close to 1 indicates no proportionate smoothing of dividends relative to percentage changes in earnings, whereas very low speed of adjustment parameter values or target payout ratio values indicate that dividends move independently of earnings . Both parameters therefore have to have intermediate estimated values before dividend smoothing is implied, with lower (multiplicative) values indicating a higher degree of dividend smoothing . Firms that choose to smooth dividends relative to earnings will have dividend payout ratios that rise when earnings fall, and decline when earnings

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recover, so a parameter that measures the sensitivity of the payout ratio to changes in earnings provides an alternative measure of dividend smoothing that is never-the-less closely related to Lintner's smoothing measure . The following degree of dividend smoothing parameter Y measures how a change in dividends caused by a change in earnings affects the existing payout ratio at time t, thus measuring the sensitivity of the payout ratio to changes in earnings : aD . _ * D . a EPS,, y ( EPS ., , )

(3)

Parameter Y measures dividend smoothing behaviour because it captures the extent to which the payout ratio is affected by changes in earnings . A value of Y < 1 indicates that the payout ratio falls when earnings rise, so percentage changes in dividends are proportionately smaller than percentage changes in earnings, thus indicating that dividends are being smoothed relative to changes in earnings, with lower estimated values indicating a greater degree of dividend smoothing . Estimation of the dividend smoothing parameter Y from Eq . (3) can be greatly simplified by assuming a specific theoretical relationship between dividends and earnings such as the following isoelastic relationship between dividends and earnings per share : D, ., = EPS ti,

(4)

This flexible isoleastic functional form is consistent with the dividend smoothing theory emphasis on a progressively stronger smoothing response when larger earnings fluctuations occur, especially in reaction to temporary earnings falls, thus potentially enhancing the measured empirical relationship between dividends and earnings (see e .g . Aivazian, Booth & Cleary, 2000 ; Kumar, 1988 ; John & Williams, 1985) . Multiplying the right hand side of Eq . (4) by a proportionality parameter b i ., taking the natural log of both sides, and adding an error term u,, yields a time series regression equation that can be used to estimate a dividend smoothing measure Y, for each company : In

D.,=Inb .+y,InEPS 0

u

(5)

The Lintner target payout ratio and speed of adjustment parameters as well as the sensitivity of the payout ratio to changes in earnings (Y) are estimated for

168


a large sample of United States firms in this study, thus documenting the extent to which firms differ in their dividend smoothing behaviour . Firm Characteristics and Dividend Smoothing Theory suggests a number of factors that are potentially relevant to empirical explanations of measured differences in dividend smoothing behaviour . Risk and Information Dividend signalling theory indicates that, in the presence of asymmetric information, a firm's dividend policy can help to credibly convey information held by insiders concerning the firm's future prospects . The John and Williams (1985) model suggests that a firm's information environment will be related to the extent to which dividends are smoothed relative to earnings . The John and Nachman (1987) model further implies that large fluctuations in a firm's quality attribute (true value) are more likely to lead to a higher level of dividend smoothing .' Kumar (1988) predicts that firms in risky industries are also more likely to smooth dividends in order to develop a reputation for having low systematic risk . Results from earnings variability studies further emphasize the relationship between risk and the incentive to smooth dividends, since high earnings variability has been found to be associated with lower than expected future profitability and future stock returns (Ronen & Sadan, 1981 ; Chaney & Lewis, 1995 ; Billings, 1999) . High risk companies with higher standard deviation of EPS would therefore have an even greater incentive to smooth dividends.' Risk considerations could also influence the extent to which financial leverage influences dividend smoothing behaviour . Leverage is likely to become an important determinant of dividend smoothing behaviour as higher leverage levels increase the riskiness of cash flows . The negative effect of financial leverage on dividend payments per se is already well-documented . Higgins (1972) and McCabe (1979) suggest that long-term debt has a negative effect on the amount of dividends paid . Rozeff (1982) finds that firms with higher financial leverage pay lower dividends in order to avoid the cost of raising external capital . There are at least two possible ways in which firm size can affect the degree of dividend smoothing chosen by firms, the first of which is also related to risk considerations. Size might affect dividend smoothing through its relationship with the volatility of stock returns since the stock prices of small firms will be more unstable due to small firms being less diversified than large firms . In fact, Fama and French (1992, 2000) show that size might be a proxy for risk . In


169

addition, there is likely to be fewer market participants with the knowledge required for arbitraging the price fluctuations of small firms . It is also possible that small firms are likely to have limited outlets for public information, so their investors could react more strongly to announcements of new events . These arguments suggest that the market for small stocks may be subject to high volatility . This high volatility caused by the size effect may lead to a greater tendency for smaller firms to choose to smooth dividends . Size can also be a proxy for the information environment . Atiase (1985) reports that less information is available for smaller firms . Freeman (1987) and Kross and Schroeder (1988) report that the market prices of large firms reflect expected earnings more than those of small firms . Richardson (1984) suggests that, since institutions have low holdings in smaller firms because of liquidity problems and other constraints, analysts have less motivation to follow small firms due to reduced commissions from institutional trades . It is therefore possible that smaller firms would have a greater proclivity to use dividends as a signal of value . In fact, Eddy and Seifert (1988) report that the information content of a dividend change is greater for smaller firms than for large firms . Ghosh and Woolridge (1988) find that the market reaction to dividend changes is a function of the degree of information asymmetry (see also Dewenter & Warther, 1998) . All of these considerations imply that size is likely to reduce signalling needs and the degree of dividend smoothing . Growth and Financial Slack

Signalling theory implies that firms with growth opportunities that are unknown to the market are more likely to pay dividends to convey this information to the market . At the same time, these firms will also have a greater need to retain a higher proportion of earnings to support their valuable investment projects . This combination of requirements would be expected to lead to declining dividend payout ratios and smoothed dividends relative to earnings for firms with high growth rates . A further implication of this combination of requirements is that high growth firms are likely to be more sensitive to the tradeoff between dividend signalling needs and dividend signalling costs, whereas low growth firms will be much less sensitive to the tradeoff because they will not want to use costly signals . Financial slack can also be considered as a potentially important factor in the decision to smooth dividends . The presence of financial slack will, in theory, reduce external financing requirements and thus solve the "underinvestment" problem, thereby reducing the signalling needs of firms and the incentive to smooth dividends (see : Myers & Majluf, 1984 ; John & Williams, 1985) .

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Cross-Sectional Regression Model Theoretical explanations of differences in firms' dividend smoothing measures

are examined in this study using the following cross-sectional regression model :

-y, = R0 + 3 I Size .

+ f3 2Tobinq

, + 133SalesGrowth1 r + 134Slack + r

P5Leverage, , ,

+ 136EV. r + u .

(6)

where firm size (Size) is proxied by the natural logarithm of total assets, Tobin's

q (Tobinq) is a market measure of firm growth opportunities that is computed as in Chung and Pruitt (1994), sales growth (Sales Growth) is the accounting

growth rate in sales over at least a ten year period, financial slack (Slack) is the ratio of cash plus marketable securities to total assets, financial leverage (Leverage) is the ratio of long-term debt to total assets, and earnings variability

(EV) is measured using the standard deviation of earnings per share over at least a ten year period . Theory implies the following coefficient signs : I3 > 0, 132 133, 13 5 & 13 6 < 0 .

& 134

RESULTS Equations (1) and (5) were fitted to a sample of United States firms over the twenty-year period 1980 to 1998 in order to estimate measures of the degree

of dividend smoothing of firms . Results are reported for the elasticity of changes

in dividend with respect to changes in earnings, as estimated using Eq . (5), but results for the Lintner smoothing measure are also discussed . The following sample selection criteria were used :

(i)

Firms had to have at least 10 years of earnings and dividend data during

the period 1980-1998, as reported in the Compustat database . From a total of 4311 firms, 1720 met this screening criteria .

(ii) When estimating Eq .

(5),

all firms with non-positive EPS or zero

dividends were eliminated from the sample due to the natural logarithmic

transformation of EPS and dividends . This further reduced the sample to 1600 firms .

(iii) A further screen excluded observations where the F-statistic from the

estimation procedure was less than 4 to ensure that EPS significantly

explained the variation in DPS (see also Choi & Prasad, 1995) . When

estimating the smoothing measure Y using Eq . (5), this screen reduced the sample to 708 firms .


171

(iv) A final screen included only smoothing firms (firms with y < 1) in the sample used to estimate cross-sectional regression Eq . (6) . This further reduced the maximum sample size for the cross-sectional regressions to 494 firms . The sample size was slightly reduced further in some of the regressions due to missing values of the explanatory variables, as indicated in the tables . The exclusion of firms with negative earnings and zero dividends (due to the logarithmic transformation of the variables D and EPS) and ys that are not significantly different from zero has the advantage of eliminating "spurious dividend smoothing ." 4 This spurious dividend smoothing arises naturally rather than being the result of conscious management of dividend policy . Dividend smoothing implies a deliberate effort on the part of managers to adjust dividend payments in response to variations in the earnings stream . Table 1 provides a frequency distribution of smoothing and non-smoothing firms . There are 708 firms with y significantly different from zero . This number is comprised of 494 firms (70%) with y < 1 that smoothed dividends with respect to earnings and 214 firms (30%) that were non-smoothing firms . This indicates that more than two out of every three firms in the final sample appear to maintain dividend smoothing as part of their dividend policy . Distribution within the group of smoothing firms indicates that the -y distribution is skewed to the left . Only 304 firms had significant Lintner smoothing measures, with only 160 of these being significant dividend smoothing firms . It is important to note, however, that the correlations between F statistics for the Lintner smoothing Table 1 .

Frequency Distribution of Smoothing and Non-smoothing Firms . No . of firms

Percent of total

-y < 1

494

69 .8

ti >l

214

31 .2

Total

708

100

Smoothing factor

Level of ti 25th 50th 75th 25th 50th 75th

percentile percentile percentile percentile percentile percentile

0 .418 0 .636 0 .845 1 .070 1 .176 1 .380

Note : Frequency distribution of the dividend smoothing factor, -y, is depicted for the subset of firms where changes in earnings per share explained a significant proportion of the variation in changes of dividends per share . Equation (5) (as developed in the text) was estimated to obtain the smoothing factors .

172


measure and the elasticity smoothing measure regressions is 0 .9877, thus indicating that both measures of dividend smoothing are very closely related . Table 2 contains a summary of the financial characteristics of dividend smoothing (y < 1) and non-smoothing (y > 1) firms . The median smoothing measure of smoothing firms is y = 0 .636 and for non-smoothing firms it is y = 1 .176 . Smoothing firms do not differ in size compared to non-smoothing firms but they have significantly higher median leverage (0 .184 versus 0 .151) . Dividend smoothing firms also have significantly lower financial slack compared to non-smoothing firms (median slack of 0 .254 versus 0 .276) . The median volatility of earnings per share of dividend smoothing firms is significantly higher than that of non-smoothing firms . Smoothing firms also tend to be low-growth firms, as indicated by a significantly lower Tobin's q (0 .839 versus 1 .162) and a significantly lower growth rate of sales (0 .089 versus 0 .135) . The univariate comparisons of smoothing versus non-smoothing firms suggests the existence of systematic relationships between various firm-specific variables and the degree of dividend smoothing . A closer and more rigorous examination of such relationships is conducted using multi-variate regression analysis . Table 3 contains correlation coefficients between the variables used in the multi-variate regressions . It is clear that several of the variables are highly correlated . The correlation between leverage and Tobin's q is negative and significant, as suggested by Myers (1977) and Smith and Watts (1992) . Financial slack is negatively correlated to both size and leverage, a finding that is consistent with the pecking order hypothesis of Myers and Majluf (1984) . Since sales growth is a popular surrogate for growth opportunities, it is not surprising to obtain a strongly positive correlation between sales growth and Tobin's q . The potential for multi-collinearity suggests the estimation of variations of Eq . (6) that include uncorrelated variables in addition to estimation of the complete model . Table 4 depicts results for the multivariate regression for the complete sample of dividend smoothing firms . Model 1 contains all the variables without regard to multi-collinearity, while the remaining models include only uncorrelated variables . Results in Model 1 are consistent with the univariate comparisons of smoothing and non-smoothing firms ; firm size and both the growth proxies are positively related to y (i .e . inversely related to the degree of dividend smoothing) while leverage and variability of EPS are negatively related to -y (positively related to the degree of dividend smoothing) . Results from the estimation of variations of Model 1 that drop correlated variables from the regression are also consistent with the findings for the full model .

0 .587 1 .012 6 .306 0 .192 0 .26 0 .107 0.7

Smoothing factor Tobin's q Size Leverage Slack Sales growth Standard deviation of EPS

0.636 0.839 6 .274 0.184 0 .254 0 .089 0 .461

median 1 .282 1 .328 6 .122 0 .161 0 .285 0 .105 0 .678

mean 1 .176 1 .162 5 .907 0.151 0 .276 0 .135 0 .407

median

median < 0 .001 < 0 .001 0 .184 < 0 .001 0 .056 0 .001 0.001

mean < 0 .001 < 0 .001 0 .249 < 0 .001 0 .04 < 0 .001 0 .861

P-value of difference

Note : Financial characteristics of firms that smooth dividend payments (firms with -y < 1) are compared with firms that do not smooth dividend payments (firms with y > 1) . Computation of the smoothing factor -y is described in Table 1 . Tobin's q is computed as (market value of common shares + liquidating value of preferred shares + book value of long-term debt plus short-term assets net of short-term liabilities) / book value of total assets . Size is the natural logarithm of total assets, leverage is long-term debt divided by total assets, slack is cash plus marketable securities over total assets, sales growth rate is the average growth rate of sales over a minimum of ten years prior to 1998 and a maximum of twenty years and standard deviation of eps is the standard deviation of earnings per share over a minimum of ten years prior to 1998 and a maximum of twenty years . Difference in mean is computed using the paired t-test under the assumption of equal variance and the difference in medians is computed by the Mann-Whitney test .

mean

Non-smoothing firms (N=214)

Financial Summary of Smoothing and Non-smoothing Firms .

Smoothing firms (N=494)

Table 2.

Variable

w

R. .

b

174


Table 3 . Tobin's q Tobin's q Size Leverage Slack Sales growth St . dev . of EPS

1 -0.065 (0.085) -0.271 (< 0.001) 0.076 (0.045) 0 .253 (< 0 .001) -0 .065 (0 .085)

Pair-wise Correlation Between Variables . Size

Leverage

Slack

Sales growth

St . dev . of EPS

1 0 .250 (< 0 .001) -0.334 (< 0.001) -0.088 (0.020) 0.093 (0.0133)

1 -0.608 (< 0.001) 0.018 (0.634) 0.008 (0.837)

1 0 .162 (< 0 .001) 0 .003 (0 .935)

1 -0.112 (0.003)

1

Note : This table contains the Pearson correlation between variables . Tobin's q is computed as (market value of common shares + liquidating value of preferred shares + book value of long-term debt plus short-term assets net of short-term liabilities) / book value of total assets . Size is the natural logarithm of total assets, leverage is long-term debt divided by total assets, slack is cash plus marketable securities over total assets, sales growth rate is the average growth rate of sales over a minimum of ten years prior to 1998 and a maximum of twenty years and st . dev. of EPS is the standard deviation of earnings per share over a minimum of ten years prior to 1998 and a maximum of twenty years . P-values are contained in parentheses .

The positive sign on the size variable is consistent with theoretical predictions and indicates that dividend smoothing decreases with size . Smaller firms have higher degrees of information asymmetry regarding future prospects (Eddy & Seifert, 1988), so signalling theory suggests they have an increased incentive to smooth dividends since the information revealed with dividend changes is significantly higher for smaller firms (Ghosh & Woolridge, 1988 ; Dewenter & Warther, 1998) . The negative association between y and variability of EPS is also consistent with signalling theory, as might be expected given that the effect of variability in EPS on the information environment is well-documented. Lower earnings variability tends to indicate greater than expected future profitability (Ronen & Sadan, 1981 ; Chaney & Lewis, 1995) and higher future stock returns (Billings, 1999) . A positive sign on the growth proxies coefficients suggests that firms with high growth prospects smooth dividends less, contrary to theoretical predictions . The results also indicate the effect of leverage on y is as predicted by theory . Leverage could also be proxying for growth opportunities (Smith & Watts, 1992) .


Table 4.

Multivariate Regressions of Smoothing Factor on Firm-Specific Variables .

Variable Intercept Tobin's q Size Leverage Slack Sales growth Std. dev . of EPS Adjusted R2 F-statistic Prob > F-stat

175

1 0.481 (< 0 .000) 0 .072 (0 .005) 0 .017 (0 .008) -0 .330 (0 .046) -0 .161 (0 .233) 0 .521 (0 .017) -0 .038 (0 .006) 0 .074 7 .53 < 0 .001

2 0 .593 (< 0 .001)

-0 .225 (0 .069) 0.618 (0.002) -0.042 (0.002) 0.042 8 .19 < 0 .001

3 0 .514 (< 0 .001) 0 .105 (< 0 .001)

-0 .036 (0 .718) -0 .034 (0 .012) 0 .057 10.99 < 0.001

4 0 .593 ( F

Std . dev . of EPS

Sales growth

Slack

Leverage

Size

Tobin's q

0 .363 (0 .049) 0 .041 (0 .388) 0 .013 (0 .795) 0 .039 (0 .112) -0 .110 (0 .666) 1 .060 (0 .014) 0 .148 (0.714) 0.04 2 .22 0.044

Intercept

2

Low volatility of EPS (N = 177)

Regressions of Smoothing Factor on Firm Specific Variables For High and Low Volatility or Earnings Sub-groups.

1

Table 6.

Variable

00 00


179

explanatory power of the two growth proxies might be explained by the informativeness of earnings volatility for future profitability (Billings, 1999 ; Ronen & Sadan, 1981 ; Chaney & Lewis, 1995) . Low earnings volatility is associated with increased future profitability, so high growth firms in the low earnings volatility group do not have to use dividends to signal this growth because it is already indicated. High earnings variability and high growth provide contradictory indications, however, so firms with these characteristics will be pulled in opposite directions regarding the desire to use dividends as a signal . The results indicate that for firms with high Tobin's q, on the other hand, the market has already somehow gotten the message that the firm is expected to have strong growth, whether or not earnings variability is low or high . The growth coefficient results suggest that the analysis should be repeated for sub-sets of firms based on their growth prospects . At least two additional reasons motivate this approach . Growth firms can have a greater need to convey private information to the market regarding their growth potential, but they also need to conserve funds to finance their investment projects, thus complicating their dividend policy choices . Secondly, growth firms are usually smaller, younger firms (see Table 3) so there is likely to be more information asymmetry regarding their true value . Given these differences in the information environment, the relative effect of the various firm-specific variables that explain smoothing behaviour could be quite different. Table 7 contains the results of estimating Eq . (6) for the high growth (Tobin's q > 1) and low growth (Tobin's q < 1) sub-samples . Chow tests (not reported) again indicate highly significant differences in the cross-sectional relationship between dividend smoothing measures and explanatory variables for the two sub-samples . Sales growth is significant only in the low growth sub-sample while the magnitude of the Tobin's q coefficient drops significantly from the low growth to the high growth sub-sample . Regressions in the low growth sub-sample explain only a small fraction of the variation in -y . The story is radically different for the high growth sub-sample . The explanatory power of the same set of variables is now ten times higher, with size, leverage and volatility of earnings per share entering the regressions very significantly ; the magnitude of both the leverage and earnings variability coefficients is 10 to 15 times higher . The results suggest that while high growth firms need to conserve cash to fund future growth opportunities when earnings rise (rather than increasing dividends proportionately with earnings), that necessity is enhanced in the presence of high leverage or higher variability in earnings per share . The positive association between y and size can be due to the likelihood that smaller firms are riskier (Fama & French, 1992) and have much lower amounts of public

1

2 3

4

Low growth firms (N=327) 5

6

2

3 4

5

High growth firms (N= 167) 6

Note: Regressions of smoothing factor y on firm-specific variables for high growth firms (proxied by Tobin's q > 1) and low growth firms (proxied by Tobin's q < 1) . Computation of the smoothing factor is described in Table 1 . Tobin's q is computed as (market value of common shares + liquidating value of preferred shares + book value of long-term debt plus short-term assets net of short-term liabilities) / book value of total assets . Size is the natural logarithm of total assets, leverage is long-term debt divided by total assets, slack is cash plus marketable securities over total assets, sales growth is the average growth rate of sales over a minimum of ten years prior to 1998 and a maximum of twenty years and std .dev . of EPS is the standard deviation of earnings per share over a minimum of ten years prior to 1998 and a maximum of twenty years . P-values are contained in parentheses .

0.692 0.896 0 .714 0 .689 0 .896 0 .650 (< 0.001) (< 0.001) (< 0 .001) (< 0 .001) (< 0 .001) (< 0 .001) 0.076 0 .085 (0.021) (0 .008) 0.028 0 .033 0 .027 (0.024) (0 .007) (0 .027) -0.549 -0.411 -0 .512 -0.411 -0 .556 (0 .014) (0.031) (0 .008) (0.031) (0.028) -0 .321 -0 .252 (0 .089) (0.113) -0 .251 -0.325 -0 .175 -0.325 0.152 (0 .388) (0.243) (0 .529) (0.243) (0.616) -0 .249 -0.243 -0.247 -0 .255 -0.243 -0.271 (< 0 .001) (< 0 .001) (< 0.001) (< 0 .001) (< 0.001) (< 0.001) 0 .261 0 .203 0.214 0 .233 0.203 0.204 10 .78 15 .06 16.08 13 .58 15 .06 15 .16 < 0 .001 < 0 .001 < 0.001 < 0 .001 < 0.001 < 0.001

1

Regressions of Smoothing Factor on Firm Specific Variables for High and Low Growth Sub-groups .

0 .283 0 .470 0 .381 0.040 0.470 0 .395 (0 .025) (

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