Mutual Fund Performance with Learning Across Funds

This paper is based on the premise that knowledge about the alphas of one set of funds will influence an investor's beliefs about other funds. This will be true insofar as an investor's expectation about the performance of a fund is partly a belief about the abilities of mutual fund managers as a group and, more generally, a belief about the degree to which financial markets are efficient. We develop a simple framework for incorporating this prior dependence' and find that it can have a substantial impact on the cross-section of posterior beliefs about fund performance as well as asset allocation. Under independence, the maximum posterior mean alpha increases without bound as the number of funds increases and 'extremely large' estimates are randomly observed. This is true even when fund managers have no skill. In contrast, with prior dependence, investors aggregate information across funds to form a general belief about the potential for abnormal performance. Each fund's alpha estimate is shrunk toward the aggregate estimate, mitigating extreme views. An additional implication is that restricting the estimation to surviving funds, a common practice in this literature, imparts an upward bias to the average fund alpha.

[1]  Ľuboš Pástor,et al.  Investing in Equity Mutual Funds , 2001 .

[2]  C. S. Jones.  Nonlinear Mean Reversion in the Short-Term Interest Rate , 2003 .

[3]  P. Swamy.  Statistical Inference in Random Coefficient Regression Models Using Panel Data , 1971 .

[4]  P. A. V. B. Swamy,et al.  Statistical Inference in Random Coefficient Regression Models , 1971 .

[5]  Glenn Ellison,et al.  Are Some Mutual Funds Managers Better than Others? Cross-Sectional Patterns in Behavior and Performance , 1996 .

[6]  Fischer Black,et al.  How to Use Security Analysis to Improve Portfolio Selection , 1973 .

[7]  Jessica A. Wachter,et al.  Should Investors Avoid All Actively Managed Mutual Funds? A Study in Bayesian Performance Evaluation , 2001 .

[8]  J. Geweke,et al.  Measuring the pricing error of the arbitrage pricing theory , 1996 .

[9]  Glenn Ellison,et al.  Are some mutual fund managers better than others , 1999 .

[10]  R. Stambaugh,et al.  Analyzing Investments Whose Histories Differ in Length , 1997 .

[11]  L. Tierney.  Markov Chains for Exploring Posterior Distributions , 1994 .

[12]  J. Berger.  Statistical Decision Theory and Bayesian Analysis , 1988 .

[13]  Klaas Baks,et al.  On the performance of mutual fund managers , 2002 .

[14]  M. C. Jensen.  The Performance of Mutual Funds in the Period 1945-1964 , 1967 .

[15]  R. C. Merton,et al.  On Estimating the Expected Return on the Market: An Exploratory Investigation , 1980 .

[16]  Sheridan Titman,et al.  On Persistence in Mutual Fund Performance , 1997 .

[17]  Jay Shanken,et al.  A BAYESIAN APPROACH TO TESTING PORTFOLIO EFFICIENCY , 1987 .

[18]  Richard J. Zeckhauser,et al.  Hot Hands in Mutual Funds: Short‐Run Persistence of Relative Performance, 1974–1988 , 1993 .

[19]  Narasimhan Jegadeesh,et al.  Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency , 1993 .

[20]  Ľuboš Pástor,et al.  Credit Suisse Asset Management , 2000 .

[21]  Oldrich A Vasicek,et al.  A NOTE ON USING CROSS‐SECTIONAL INFORMATION IN BAYESIAN ESTIMATION OF SECURITY BETAS , 1973 .

[22]  A. Mackinlay,et al.  Multifactor Models Do Not Explain Deviations from the CAPM , 1994 .

[23]  Ľuboš Pástor,et al.  Costs of Equity Capital and Model Mispricing , 1998 .

[24]  Leonard J. Savage,et al.  The foundations of statistical inference : a discussion , 1962 .

[25]  Philippe Jorion.  Bayes-Stein Estimation for Portfolio Analysis , 1986, Journal of Financial and Quantitative Analysis.

[26]  G. Casella,et al.  Explaining the Gibbs Sampler , 1992 .

[27]  R. Stambaugh,et al.  On the Predictability of Stock Returns: An Asset-Allocation Perspective , 1995 .

[28]  E. Fama,et al.  Common risk factors in the returns on stocks and bonds , 1993 .