Academic journal article Academy of Accounting and Financial Studies Journal

Portfolio Decisions and the Small Firm Effect

Academic journal article Academy of Accounting and Financial Studies Journal

Portfolio Decisions and the Small Firm Effect

Article excerpt

ABSTRACT

Many empirical studies have found excess returns on the stocks of small firms. This "small firm effect" may cause individual investors to choose to diversify into smaller firms when they make asset allocation decisions. This paper questions whether investors should consider the small firm effect. Monte Carlo simulations cast doubt on the small firm effect. We urge investors to exercise caution when buying small stocks.

The views expressed in this paper are those of the authors and do not necessarily represent the views or policies of the International Monetary Fund. This paper describes research by the authors and is published to elicit further debate. The authors are indebted to Stephen Smith, Peter Dadalt, Jacqueline Garner and an anonymous referee for several useful comments and suggestions.

INTRODUCTION

Many empirical studies have observed excess returns on the stocks of small firms. For example, Ibbotson and Sinquefeld (1999) report that common stocks earned an average annual return of 13.2 percent from 1926 to 1998 while small capitalization stocks earned an average annual return of 17.4 percent. This implies an excess annual return of 4.2 percent before adjusting for risk. Investors often include small stocks in their portfolios because they expect to receive higher returns. The higher returns will enable them to reach their goals or to reach their goals sooner. These investors are usually aware of the increased level of volatility associated with the returns of small stocks but they expect to be generously rewarded for the additional risk. According to this assumption, they will receive more return per unit of risk than if they had invested in the stocks of larger firms. We question whether this assumption is valid and whether the small firm effect (SFE) truly exists in the markets or whether it is an artifact of the datasets that are studied. By investing in small stocks, investors may be accepting even more risk than they believe they are.

Reinganum (1981/1999) finds excess risk-adjusted returns on small firms. Aharony and Falk (1992) find that small banks' returns second-order stochastically dominate returns for large banks. Roll (1981) offers an explanation for the phenomenon. He conjectures that the SFE may be attributable to improper estimation of systematic risk due to non-synchronous trading. According to this hypothesis, infrequent trading of smaller firms' stocks leads to autocorrelation in the returns for portfolios of small stocks. This, in turn, leads to an underestimation in their variances and systematic risk. Reinganum (1982), however, tests Roll's proposition and concludes that the SFE continues to be significant even after correcting for non-synchronous trading. Isberg and Thies (1992) attribute the SFE to the higher direct and indirect transaction costs involved in investing in small firms and the difficulty associated with measuring these. Wei and Stansell (1991) find that benchmark error (measurement error on the market) may explain the SFE. Knez and Ready (1997) find that the risk premium on size completely disappears when they use a regression technique that trims the one percent most extreme data observations each month. They believe that the differences in how various small firms grow determine the higher returns on small firms.

None of the above studies consider survival as a possible problem. Aharony and Falk (1992) test for the presence of survivorship bias arising out of the non-inclusion of failed firms. They do so by testing for differences in results between the entire sample period and a sub sample when no firms fail. However, the bias due to survival is present in the first period even though no firms fail in the sub sample period. On the other hand, survival, as we define it, refers to the fact that "default" states are not observed for the firm. Hence, the time series data on the stock does not comprise a representative sample for drawing inferences. …

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