Frontiers of Entrepreneurship Research 1995

Frontiers of Entrepreneurship Research
1995 Edition

1995 Abstracts

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    Benoît F. Leleux, Babson College
    Julian E. Lange, Babson College
    Veronique M. Matthys, Woodland Hill Associates


    The efficiency of the market in pricing stocks which have historically experienced extremely high growth rates is examined. In order to test market efficiency in these conditions, the returns to simple investment portfolio strategies based on public information are investigated. The portfolios consist of shares in the firms listed in the Inc.100 Ranking of the Fastest Growing Public Companies in America. The analyses conducted here indicate that significant abnormal returns are generated for these strategies in excess of what would normally be required to compensate for the level of risk incurred. Although the tests could potentially be affected by a form of survivorship bias, supplementary analyses indicate that this is unlikely to be the case here. The implications of this initial study are enormous, for both investors and issuers. If indeed the market is not able to properly price high-growth entities, a fact long theorized by growth and new venture specialists, then significant abnormal returns could be earned by simple trading rules.


    If capital markets were efficient, no advantages could be gained by issuers attempting to time their stock offerings since their share price would always reflect all available information. Despite the general acceptance of the efficiency paradigm in its various strengths (from weak to strong), investment bankers and issuers alike persist in spending great efforts and money in finding the right "window of opportunity" in the market, where temporary "mispricings" would optimize the proceeds of their planned financial operations. To reconcile these two positions, it is necessary to investigate the efficiency of the market in some critical, limit conditions, such as those prevailing when attempting to price stocks which have historically experienced extremely high growth rates.

    Standard valuation theories assume that the price of a share is simply the discounted value of all cash flows accruing to that stock in the future. Under an efficient market hypothesis, the resulting prices should at all times reflect rational expectations about the future, so that the realized returns on such stock holdings should on average be commensurate with their risk level. However, empirical tests in the literature have highlighted many instances in which the pricing of stocks appears to deviate from that which would be expected under efficient markets.

    A first line of research indicated the existence of abnormal returns for small firms, which, in subsequent investigations, were further shown to occur primarily in early January (what became known as the "January effect", although it should more adequately be referred to as the "Small Firm January Effect") [Banz (1981); Keim (1983); Reinganum (1983); Blume and Stambaugh (1983); Ritter and Chopra (1989)]. Other studies have identified additional anomalies including a "day-of-the-week effect": statistically significant differences in returns are observed on different days of the week, and in particular largely negative returns during the period from the close of the market on Friday to its close on Monday [French (1980); Keim and Stambaugh (1984)]; the "earnings report effect": abnormal returns seem to occur around quarterly earnings announcements which differ markedly from prior analysts' expectations [Rendleman, Jones, and Latane (1982)]; and an "overreaction/reversal effect": stocks having experienced high returns during one period tend to underperform in subsequent periods, and vice-versa for the underperformers [DeBondt and Thaler (1985); Clayman (1987); Chopra, Lakonishok, and Ritter (1992); Haugen (1995)].

    Supergrowth firms, which for the purpose of this study will be defined as the publicly traded firms having experienced the highest annual sales growth over the prior 5 year period, are especially likely to stretch the efficiency concept because: (1) future growth rates are difficult to estimate and very volatile; and (2) most of these firms belong to high beta cohorts where deviations from CAPM pricing have already been outlined in the empirical literature. Moreover, behavioral studies of decision-making under uncertainty indicate a tendency for estimates of future outcomes to be biased by observations of prior outcomes. Anchoring, for example, has been shown to occur where an initial best guess is adjusted upward or downward to predict a future outcome: the resulting estimated ranges tend to be too narrow and biased in the direction of the initial estimate. Issues of small sample size and the representativeness of prior observed experience can lead to further biases in estimates of future outcomes, as detailed by Kahneman, Slovic and Tversky (1982).

    The estimation of future growth rates of supergrowth firms can be hypothesized to be unduly affected by investors' observations of recent growth rates, and therefore lead to expectations of stock prices which are biased upward from their intrinsic values. Indeed, a statistically significant overreaction of this type has been observed by Chopra, Lakonishok and Ritter (1992). The over-reaction effect was observed to persist even after adjusting for firm size and risk and was shown to be more pronounced for smaller firms. The noted tendency for individuals to weight recent data more heavily in the making of judgments about the future might be expected to create further deviations from the conformance of observed returns to those predicted by the CAPM.

    Even though stock prices at all times should reflect the present value of all future cash flows, it is clear that estimating the cash flows for supergrowth firms is no easy task. Moreover, many of these stocks may not be as closely followed by institutional investors as those from larger, more established companies. This lack of scrutiny may in many instances lead to a diminution of the quality of information available about the supergrowth stocks as a group and to a further lack of precision in the market's pricing of these assets [Arbel (1985)]. In addition, these stocks tend to exhibit high systematic risk, a factor which has been associated in previous papers with deviations from standard asset pricing models (high beta stocks tend in general to perform worse than expected under the risk/return model).

    The objective of the study is then, using simple investment portfolio strategies, to determine the extent to which abnormal returns could be earned by selectively investing in these supergrowth stock based solely on public information. More specifically, all trades are based on the Inc. magazine annual ranking of the 100 fastest growing public companies in America. This ranking is likely to identify a group of companies particularly difficult to price for the market because of their extreme historical rates of growth (which are unlikely to persist) and their high level of overall risk (most firms belong to emerging, high technology fields).

    The results tend to support the general hypothesis: opportunities for statistically significant abnormal returns seem to exist, even after correcting for risk and the possibility of a survivorship bias in the sample. An equally weighted investment in all firms listed in the Inc.100 rankings in 1990 and 1992 would have generated risk-adjusted excess returns of close to 30% in the 20 months following the ranking. The results are generally not consistent with market efficiency for these supergrowth firms, opening up the door to the possibility of strategic games, such as issue timing, by issuers and investors alike. These results also shed new light on the contradiction observed under the traditional efficiency paradigm between the theoretical absence of benefits to timing activities and the observed reliance of many issuers and investment bankers on such factors. If the market is indeed less efficient in "limit" conditions (high growth, high risks, etc.), then mispricings may occur and timing may be a valuable investment.

    The study is structured as follows. Section 2 lays down the research hypotheses and their conceptual justifications. Section 3 outlines the database created for the purpose of this research program. It is followed by an extensive presentation of the methodologies used to measure abnormal returns in the supergrowth portfolios. Section 5 provides some descriptive statistics of the sample, and introduces the extensive analyses of the results conducted in section 6. Conclusions and discussions ensue.


    The fundamental objective of the paper is to investigate the market's ability to properly price a group of firms characterized by very high historical growth rates. As outlined in the introduction, these firms are likely to stretch the market efficiency concept to the limit because of the intrinsic volatility of such firms and the difficulty of forecasting future growth and risk. Testing for pricing errors in supergrowth firms thus becomes a proxy for the larger question of market efficiency in critical (limit) conditions. Within that broad statement of objectives, a series of more specific hypotheses are being analyzed.

    Hypothesis 1: Supergrowth firms have higher-than-normal exposure to market risk, i.e. they exhibit statistically higher levels of systematic risk that the market as a whole.

    Hypothesis 1 serves important conceptual and methodological purposes. Conceptually, the evolution of market risk exposure for high growth firms is still largely uncharted territory, as outlined by Cotter (1991) and Loughran and Ritter (1994). The common understanding is that supergrowth firms, particularly those which are relatively young and have products with untested futures, have "high risk", as mentioned by Loughran, Ritter and Rydqvist (1994). A significant problem with this approach, highlighted in Leleux (1993), is that total risk is often mistaken for market-related risk (also referred to as systematic risk, or beta). In other words, these firms seem to exhibit large variances in their returns but the sensitivity of these returns to variations in market returns may not be "high". Still presented differently, most of the risk inherent in these young, high-growth firms may be of a non-systematic (idiosyncratic) nature and thus easily diversifiable in a portfolio. These risks may include production problems, marketing and logistics constraints, etc., most of which are unconnected to the performance of the market. No extra returns should then be expected to carry unsystematic risk, as underlined in the finance literature.

    The nature of the risk has important methodological implications for the choice of the returns adjustment procedure. If the systematic risk is close to 1.0 on average, a simple market adjustment is acceptable. On the other hand, if beta is statistically different from 1.0, an additional risk adjustment is required, using some form of risk/return model, usually the CAPM.

    Hypothesis 2: Abnormal returns can be observed for supergrowth firms before, upon, or after the Inc.100 rankings publishing date.

    Hypothesis 2 is essentially testing the market efficiency concept. In an efficient capital market, stock prices correctly reflect all publicly available information, so that changes in stock prices around information announcements (such as the publication of the Inc.100 rankings) provide an unbiased assessment of the economic effect of the event on the target/acquiror company's shareholders [Schwert (1984); Brown and Warner (1980,1985)]. Furthermore, companies only earn a "normal" rate of return over the long-term, where normal is defined with respect to their respective risk category.

    Hypothesis 3: Cumulative abnormal returns post-ranking can be related to growth-related variables.

    Hypothesis 3 extends the results obtained while testing hypothesis 2 if significant abnormal returns are highlighted there. It is an attempt to determine the factors driving abnormal returns over the long-run, factors which could include prior growth rates, growth in employees, growth in net income, etc.


    This preliminary paper is intended only as a concept demonstrator to justify further investments in time and research funds. Accordingly, only a subsample of all Inc.100 ranked firms is utilized. The Inc.100 ranking of the fastest growing public companies in America was first published in Inc. magazine in May 1979 and has been a regular feature ever since, with the exception of 19911. The 1990 and 1992 rankings are used here because 1) the relatively recent period facilitates the collection of financial information; 2) sufficient time exists post-rankings to test for the existence of abnormal returns to various strategies consisting in buying (selling) shares in the Inc.100 rankings, and, 3) the two timeframes allowed for some informal tests of the survivorship bias possibly affecting early rankings2. Of the 100 firms in the 1992 ranking, the whereabouts of 95 could be traced back, of which 2 were reported as "delisted". Delisting can happen for a multitude of reasons, including merger, acquisition, going-private, bankruptcy, or liquidation. The first three categories possibly provide shareholders with reasonable returns upon delisting, the last two are more questionable. No information is available at this stage on delisting reasons, nor the performance of the stocks from ranking until delisting. For the 1990 ranking, the whereabouts of 3 companies are unknown, 11 firms were delisted with partial returns information available and another 14 were delisted with no information.

    For each firm in the sample, the following information was collected: rank in Inc.100 survey, name, industry code, sales growth in the five years prior to the ranking, revenues and net income in last financial report and five years prior to the that, number of employees in ranking year and five years prior to it, year the company went public and on which market, salary of CEO, whether the CEO founded the company, and the equity ownership of the CEO. Monthly return and systematic risk data was obtained from Compustat sources. Economic series, such as monthly risk-free rates and returns on market indices (Standard & Poors 500 and Dow Jones Industrial) were downloaded from Citibase.


    The traditional event study performance analysis is based on the measurement of some "abnormal" return for the shares investigated over a period of interest. In the case at hand, the objective is to determine to what extent a ranking in the Inc.100 list of the fastest growing public companies in America actually conveys information about future returns, i.e. is it possible to implement, on the basis of the rankings, investment strategies that will earn returns higher than expected by the standard risk/return models?

    This objective implies that the period of interest for the analysis is the post-ranking months, since trading (or arbitrage) strategies must be based on information released in the Inc.100 rankings. These rankings have historically been published essentially in May each year (with the exception of the 1991 lapse mentioned above). The month of publication of the ranking is then referred as Month 0 in the event study. All subsequent months are denoted by higher integers, i.e. the third month post-ranking is month 3.

    The definition of what constitutes "abnormal" returns presupposes 1) the knowledge of what a "normal" return should look like, and, 2) market efficiency, in the sense that risk information is indeed reflected in the prices. Accordingly, event studies are always joint tests of both market efficiency and the particular model being used to represent the return process. Two models are used here for the latter. First is a simple market model, assuming the expected return for all stock is the actual return realized on the market for the period. This implicitly assumes an average beta of 1.0, an assumption which will be tested later. Abnormal returns are then measured by:

    (1) where ARi,t is the market-adjusted abnormal return on share i in post-event month t, Ri,t is the raw return on share i in month t, and Rm,t is the corresponding return on the market index. The choice of an adequate market benchmark potentially influences the reported results, so two indices are used here: the Standard & Poors 500 and the Dow Jones 30 Industrial. The second model explicitly incorporates the systematic risk of individual securities in the adjustment procedure, using a CAPM-type risk/return relationship. Abnormal returns are measured as follows:

    (2) where Ri,t is the raw return on share i in month t, and Rf,t is the corresponding return on the risk-free security (proxied here by the return on the U.S. Treasury 90-day bill), (I is share i's system-atic risk coefficient measured over the previous 60 months3, and MRP is the Market Risk Premium, or the price the market has historically been willing to pay per unit of beta risk carried by investors. A 70-year average is used here, amounting to 8.5% per year per unit of beta risk.

    For each method, an average abnormal return is then calculated for each event month following publication, using:

    (3) where nt is the number of shares in the cross-section in month t following publication. The long-run performance measure involves the cumulating of these abnormal returns over time for each individual firm, followed by cross-sectional averages [Dimson and Marsh (1986)]:

    (4) Although this has been the method of choice for the last 20 years, Conrad and Kaul (1993) high-lighted the potential bias in this procedure, which aggregates not only returns but also individual estimation errors. In general, estimation errors seem to cancel out, but the potential remains and the magnitude of the problem is unknown.


    As mentioned above, 200 firms entered the 1990 and 1992 Inc.100 rankings. Of the 1992 cohort, only 95 firms could be located using a combination of Bridge on-line, Compustat, Standard and Poors Stock Listings, Datastream, and Dial-Data Stock Lookup, with 2 of them reported as delisted. Another 5 firms are "unknown", possibly resulting from typos in the original Inc.100 listings. For the 1990 ranking, the whereabouts of 3 companies are unknown, 11 firms were delisted with partial returns information available and another 14 were delisted with no information.

    The distribution of the sample's sales growth in the five years leading to the ranking are depicted below in exhibit 1. Most firms experience average compounded annual growth rates over that 5 year span in excess of 100% (mean=135.2%), with the top ranked firms exceeding 400% (maximum=413%). These figures qualify the sample firms as supergrowth firms, justifying the title of the paper. Exhibit 2 focuses on the distribution of employment among these firms at the time of the rankings. The distribution is far more stretched at the employment level, with a mean of 759 employees, but a standard deviation of 1652 (minimum=10; maximum=14500). The same sample of firms had, on average, only 122 employees (SD=549) 5 years before the rankings.

    On the financial side, sample firms have an average revenue in ranking year of $86,408,420 (SD=114,563,524; Min=$17,000; Max=$776,029,000), up from $3,646,915 five years before. The Net Income figure averages $4,405,445 (SD=12,326,602; Min=$-46,590,000; Max=$81,766,000), up from an average of $-923,000 five years before the rankings.

    (Exhibit 1)(exhibit 2)

    The Chief Executive Officers of the companies listed control on average 13.04% (SD=13.4%; Min=0%; Max=75%) of the equity in their firms, with the distribution highlighted in exhibit 3. In most instances, stakes are significant, so most CEOs would adequately be referred to as owner-directors. They also draw an average salary of $237,156 (SD=$377,400; Min=$50,000; Max=$5,000,000).

    (Exhibit 3)


    Systematic Risk of Sample Firms

    Hypothesis 1 calls for the analysis of the systematic risk (beta) of the firms involved in the ranking. Beta is a measurement of the sensitivity of a company's stock price to the overall fluctuations in a stock market, proxied here by the Standard & Poor's 500 Index Price for Industrial Companies. For example, a beta of 1.5 indicates that a company's stock price tends to rise (fall) 1.5% with a 1% rise (fall) in the index price. Beta is calculated here for a 5-year (60-month) time period, ending in the ranking month. If less history is available, beta is calculated for as few as 24 months. Month-end closing prices, including dividends received, are used in the calculation. The resulting average betas for each event month around the ranking date are presented in exhibit 4. Betas is shown to hover in the 1.25 to 1.40 range, significantly larger than the market average of 1.00. These figures support hypothesis 1 and, by direct implication, the use of risk-adjusted returns in complement to the simpler market adjustment, which would be upward biased in this case.

    (Exhibit 4)

    Monthly Abnormal Returns

    The next step in the analysis consists in analyzing the monthly abnormal returns around the respective ranking dates, using the various adjustments mentioned in the methodology section. These results are reproduced in graphic form in exhibit 5 below. A large proportion of the monthly returns appear to be on the positive side following the rankings but inferences are not particularly easy from this type of statistics. Instead, cumulative returns should be used over the post-ranking period.

    (Exhibit 5) (exhibit 6)

    To keep the sample consistent, individual stocks are tracked for only 20 months post-ranking. This is due to natural limitations in the samples selected: firms ranked in May 1992 only have 30 months of historical data, but only 20 are currently available on research tapes. On the other hand, firms listed in 1990 have close to 48 months of returns information. Accordingly, the shorter event window is used here at this stage in the research. Longer windows are of course desirable and will be investigated in later steps. The results of the cumulation over the 20-month post-ranking period are presented in exhibit 6 below for no adjustment (raw returns), a market adjustment (S&P500), and a risk adjustment (beta).

    Two features are particularly striking in the long-term performance of the shares. First of all, the Inc.100 ranking is followed by a significant "dip", or a period of abnormally negative returns. This, in and of itself, would tend to support the notion that a share appearing in the Inc.100 rankings may be "overbought" by investors chasing the next "hot" company, resulting in prices that are not sustainable over time. The market correction results in the negative abnormal returns observed through month 5.

    Second, following these negative returns, firms tend to experience statistically significant positive abnormal returns. These returns support an alternative interpretation: that the market is actually underestimating the future growth potential of the firms listed in the rankings or overestimating their risk and, accordingly, underpricing them. Strategies consisting in purchasing stocks listed in the Inc.100 rankings in the month of the ranking and holding them over 20 months generate raw returns of approximately 80% over the period, or 30% in excess of what would have been expected given the level of risk assumed in the strategy. The t-tests are reproduced in exhibit 7.

    (Exhibit 7)

    These cumulative abnormal returns indicate the apparent inability of the market to properly price supergrowth stocks, leaving ample opportunities for arbitrage profits, either short-selling the list over a short horizon (about 5 months) or buying the stocks and holding them over the long term (up to 20 months).

    A possible shortcoming of the method used above in the possibility of a survivorship bias. Since a number of firms are left out of the analysis, either because they cannot be identified or because they are reported as "delisted", there is always the possibility that these firms are actually bankrupt and/or liquidated, with consequent losses to their investors. As mentioned above, only 95 firms in the 1992 cohort could be located, with 2 of them reported as delisted. The remaining 5 firms are "unknown", possibly resulting from typos in the original listings. For the 1990 ranking, the whereabouts of 3 companies are unknown, 11 firms were delisted with partial returns information available and another 14 were delisted with no information.

    A survivorship bias is likely to affect results if all firms declared as "delisted" are actually total losses to their investors. But delisting can happen for a host of reasons, including mergers, acquisitions, going-private transactions, as well as liquidation. Bankruptcy is only one of the options. At this stage, no evidence has been gathered on the exact causes of disappearance. Since the 1990 cohort is likely to be more seriously affected than the 1992 group by a survivorship bias if one exists, the sample is split accordingly and the cumulative abnormal returns are recalculated. The results are presented in exhibit 8 below.

    (Exhibit 8)

    The same general pattern of cumulative abnormal returns is observable for the overall sample and each subsample by ranking year. The 1990 cohort, characterized by a larger percentage of variously "delisted" firms, does exhibit larger abnormal returns over the long term, possibly as a result of the survivorship upward bias, but the 1992 cohort, which is less affected by defections, still exhibits the significant positive abnormal returns. These results tend to support the contention that survivorship bias, if present at all, is probably not a significant influence on the results.

    A final series of tests are conducted to determine the extent to which two other portfolio strategies could improve the returns performance over the long term. Strategy 1, referred to a "front-loading", consists in putting higher portfolio weights on these firms that ranked higher in the survey. This corresponds practically to investing $100 in the firm ranked first, $99 in the firm ranked second, $98 in the firm ranked third and so forth. Conversely, strategy 2, referred to as "back-loading" takes the exact opposite position, investing $100 in the firm ranked last, $99 in the firm ranked 99th and up to $1 in the firm ranked first. The results of these tests indicate that changing the investment weights does not seem to affect the overall configuration of returns over time: there still appears to be positive abnormal returns to these various strategies.

    Regression Analyses

    The empirical analyses performed above pertained to determining to what extent simple strategies based on public information, in this case the Inc.100 ranking of the fastest growing public companies in America, could be used to generate returns in excess of what a normal risk/return relationship would require. Such deviations are interpreted as supporting the inefficiency of the market, i.e. its inability to properly price stocks characterized by high systematic risks (beta) and extremely large historical growth rates.

    A final step involves the investigation of the factors that may explain the abnormal returns observed, in a classic regression methodology. Possible explanatory factors for the cumulative abnormal returns over the 20 months post-ranking include the growth in sales, revenues, and net income over the prior 5 years, market capitalization, the owner's salary or equity ownership in the firm, and whether the current CEO is also the company founder.

    None of the regressions performed, with either single explanatory factors or combinations thereof, indicate significant relationships. This may in part due to the low number of explanatory factors at this point. Further developments will add a number of accounting variables to the regressions. At this time, analyses do not indicate the existence of significant relationships between possible explanatory factors and the long-term performance of the investment strategies outlined above, so hypothesis 3 is not supported.


    The initial objective of the study is to determine the extent to which the market is able to properly price stocks characterized by very high historical growth rates, what is referred to here as "supergrowth" stock in deference to the more commonly known "growth" stocks. A number of arguments can be made to support possible market inefficiencies in these limit conditions. The finance literature focuses on the importance of growth rates and systematic risk in pricing shares, both factors which are likely to be difficult to evaluate for firms having experienced explosive growth in the last five years. The psychology literature, and in particular, its subset studying human inferences and its biases, highlights the tendency for individuals to diverge from pure rationality, for example by attributing larger probabilities than deserved to events relatively close in time to the present. In other words, humans may not be perfect Bayesian updaters, letting a number of biases taint their inferential processes.

    In order to test market efficiency in these conditions, the returns to simple investment portfolio strategies based on public information are investigated. The portfolios consist of shares in the firms listed in the Inc.100 Ranking of the Fastest Growing Public Companies in America. These portfolios are assembled when the rankings are published and held for various periods of time. The analyses conducted here indicate that significant abnormal returns are generated for these strategies in excess of what would normally be required to compensate for the level of risk incurred. Although the tests could potentially be affected by a form of survivorship bias, supplementary analyses indicate that this is unlikely to be the case here. Furthermore, refinement to the strategies, involving mixing portfolio weights, do not seem to affect the overall results. Cross-sectional regressions were not able to single out significant explanatory factors for the long-term performance of these investment strategies.

    The implications of this initial study are enormous, for both investors and issuers. If indeed the market is not able to properly price high-growth entities, a fact long theorized by growth and new venture specialists, then significant abnormal returns could be earned by simple trading rules. From a company standpoint, such inefficiencies essentially indicate that "windows of opportunity" indeed exist in the market for issuing new shares, a view again long-supported by investment bankers and issuers alike. In other words, periods of overpricing and underpricing of shares exist, justifying the recourse to, respectively, new issuances or stock repurchases.

    The existence of "pockets" of inefficiency in the market in its high-growth segments puts a serious cap on the generally accepted concept of efficiency as a whole. If indeed the market is efficient under "reasonable" conditions, deviations from that norm (such as those resulting from explosive growth, bankruptcies, liquidations, major catastrophes, etc.) seem to quickly stretch the ability of the market to analyze and incorporate the new information into the prices. These delayed responses or mispricings open up the door to strategic behavior by issuers and investors alike, something most financial actors have long supported but could not be accommodated by the classical market efficiency paradigm.

    This preliminary study is currently being extended to the full sample of Inc.100 rankings, going back to 1979. The reasons for delistings are also being researched to be able to strongly reject the possibility of a survivorship bias in the reported results. Finally, more refined analyses of the risk-adjustment process are being conducted, as well as a conceptually more robust development of possible explanatory factors for the long-term performance of the simple supergrowth portfolio strategies.


    Arbel, Avner. (1985) "Generic Stocks: An Old Product in a New Package." Journal of Portfolio Management, pp. 4-13.

    Banz, R.W. (1981) "The Relationship Between Return and Market Value of Common Stock." Journal of Financial Economics, pp. 3-18.

    Blume, Marshall E., and Robert F. Stambaugh. (1983) "Biases in Computed Returns: An Application to the Size Effect." Journal of Financial Economics, pp. 387-404.

    Brown, Stephen and Jerold Warner. (1980) "Measuring Security Price Performance." Journal of Financial Economics 8, 3, pp. 205-258.

    Brown, Stephen and Jerold Warner. (1985) "Using Daily Stock Returns: The Case of Event Studies." Journal of Financial Economics 14, pp. 3-31.

    Chopra, Navin, Josef Lakonishok, and Jay R. Ritter. (1992) "Measuring Abnormal Performance: Do Stocks Overreact?" Journal of Financial Economics 31, pp. 235-268.

    Clayman, Michelle. (1987) "In Search of Excellence: The Investor's Viewpoint." Financial Analysts's Journal (May-June, 1987).

    Conrad, Jennifer and Gautam Kaul. (1993) "Long-Term Market Overreaction or Biases in Computed Returns." The Journal of Finance 48, 1, pp. 39-63.

    Cotter, James F. (1992) "The Long-Run Efficiency of IPO Pricing." University of North Carolina at Chapel Hill.

    De Bondt, Werner F.M. and Richard M. Thaler. (1985) "Does the Stock Market Overreact?" Journal of Finance 40 , pp. 793-805.

    Dimson, Elroy and Paul Marsh. (1986) "Event Study Methodologies and the Size Effect: The Case of UK Press Recommendations." Journal of Financial Economics 17, pp. 113-143.

    French, Kenneth. (1980) "Stock Returns and the Weekend Effect." Journal of Financial Economics 8 (March, 1980), pp. 55-69.

    Haugen, Robert. (1995) "The Race Between Value and Growth," The New Finance: The Case Against Efficient Markets. New York: Prentice-Hall, pp. 55-71.

    Kahneman, Daniel, Paul Slovic, and Amos Tversky. (1982) Judgment Under Uncertainty: Heuristics and Biases. Cambridge: Cambridge University Press.

    Keim, Donald B. (1983) "Size Related Anomalies and Stock Return Seasonality: Further Empirical Evidence." Journal of Financial Economics 12, pp. 13-32.

    Keim, Donald B. and Robert F. Stambaugh. (1984) "A Further Investigation of the Weekend Effect in Stock Returns." Journal of Finance 39, pp. 819-835.

    Leleux, Benoit F. and Daniel F. Muzyka. (1993) "IPO Performance in the United Kingdom: A Dynamic Beta Reappraisal." INSEAD working paper.

    Loughran, Tim, Jay R. Ritter and Kristian Rydqvist. (1994) "Initial Public Offerings: International Insights." Pacific-Basin Finance Journal 2, 2, pp. 165-199.

    Loughran, Tim and Jay R. Ritter. (1992) "The Long-Run Performance of IPOs: II." University of Illinois working paper.

    Reinganum, Marc R. (1983) "The Anomalous Stock Market Behavior of Small Firms in January: Empirical Tests for Tax-Loss Selling Effects." Journal of Financial Economics 12, pp. 89-104.z

    Rendleman, R.J., C.P. Jones, and H.A. Latane. (1982) "Empirical Anomalies Based on Unexpected Earnings and the Importance of Risk Adjustment." Journal of Finanical Economics, 10, pp. 269-287.

    Ritter, Jay R. and Navin Chopra. (1989) "Portfolio Rebalancing and the Turn-of-the-Year Effect." Journal of Finance 44, pp. 149-166.

    Schwert, William G. (1984) "Using Financial Data to Measure the Effects of Regulation." Journal of Law and Economics 24, pp. 121-158.

    1 The lapse was acknowledged by Inc. magazine's research manager for both the Inc.500 and the Inc.100 annual surveys.

    2 The possible existence of a survivorship bias is discussed at length in the methodology section.

    3 Or a minimum of 24 months if data are not available for the whole 60 months.

    4 Space limitations do not permit the inclusion of these data. The results, graphed by cohort year, are available upon request from the authors.

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