Can Weight-Based Measures Distinguish between Informed and Uninformed Fund Managers?

Abstract

This paper studies weight-based mutual fund performance measures in a panel predictive regressions framework, where future stock returns are regressed on a fund's portfolio weights. Existing performance measures suffer biases related to benchmark misspecifications and are statistically inefficient. To address these issues, we introduce bias-adjusted and weighted least squares (WLS) measures. Simulations show that new methods can effectively control bias and improve power, compared with existing measures. We also apply the existing and newly introduced measures to empirical examples. Using bias-adjusted measures and efficient measures can lead to different conclusions about managers' abilities.