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his support with the CDA/Spectrum database. We especially thank Russ Wermers for providing us with the characteristic-adjusted stock returns reported in DGTW (1997). We acknowledge the financial support from Mitsui Life Center in acquiring the CDA/Spectrum data. All errors are our own responsibility. On the Industry Concentration of Actively Managed Equity Mutual Funds The value of active fund management recently has become a central debate among researchers and practitioners. Mutual fund managers can deviate from the passive market portfolio by concentrating their holdings in specific industries. We investigate whether mutual fund managers are motivated to hold concentrated portfolios because they have investment skills that are linked to specific industries or whether they are motivated by agency problems that induce them to hold poorly diversified portfolios. Using U.S. mutual fund data from 1984-1999, we study the relationship between the industry concentration of mutual funds and their performance. Our analysis indicates that mutual funds differ substantially in their industry concentration, and that concentrated funds tend to follow distinct investment styles. Managers of more concentrated funds

Estimates of standard performance measures can be improved by using returns on assets not used to dene those measures. Alpha, the intercept in a regression of a fund's return on passive benchmark returns, can be estimated more precisely by using information in returns on non-benchmark passive assets, whether or not one believes those assets are priced by the benchmarks. A fund's Sharpe ratio can be estimated more precisely by using returns on other assets as well as the fund. New estimates of these performance measures for a large universe of equity mutual funds exhibit substantial differences from the usual estimates.

and SGF 2006 for their helpful comments. The first and second authors acknowledge

We use daily returns to compare the performance predictability of Bayesian estimates of mutual fund performance with standard frequentist measures. When the returns on passive nonbenchmark assets are correlated with fund holdings, incorporating histories of these returns produces a performance measure that predicts future performance better than standard measures do. Bayesian alphas based on the Capital Asset Pricing Model (CAPM) are particularly useful for predicting future standard CAPM alphas. Over our sample period, priors consistent with moderate to diffuse beliefs in managerial skill dominate more skeptical prior beliefs, a result that is consistent with investor cash flows.

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We show theoretically that the responsiveness of a fund manager’s portfolio allocations to changes in public information decreases in the manager’s skill. We go on to estimate this sensitivity (RPI) astheR 2 of the regression of changes in a manager’s portfolio holdings on changes in public information using a panel of U.S. equity funds. Consistent with RPI containing information related to managerial skills, we find a strong inverse relationship between RPI and various existing measures of performance, and between RPI and fund flows. We also document that both fund- and manager-specific attributes affect RPI. THE CONCEPT OF SOPHISTICATED INVESTORS permeates the economic literature in several areas, including market microstructure, tests of the efficient market hypothesis, and the performance evaluation of financial institutions. Sandroni (2000, p.1303) succinctly describes these investors as those who “are consistently better in predicting prices. ” Whether such investors exist and whether they outperform others has been the subject of debate for at least a few decades,

In the asset pricing literature, time-variation in market expected excess return captured by financial ratios like dividend yield is typically viewed as a reflection of either changing risk, related to the business cycle, or irrational mispricing. Extending the work on asset allocation and dividend yield by Kandel and Stambaugh (1996) to accommodate variation in risk as well as expected return, we develop Bayesian methods to examine the interaction between the data and an investor’s initial beliefs about the sources of return predictability. Although results vary with the subperiod examined, different views on the relative importance of these factors can have important implications for asset allocation between a stock index and a riskless asset. In general, however, the simple risk/return model of Merton (1980) explains very little of the yield-related return predictability observed.