Can mutual fund “stars” really pick stocks? New evidence from a wild bootstrap analysis

Abstract

This paper identifies the issue of “duplicate observations” in existing methods for analyzing mutual fund performance and proposes a solution using a novel wild bootstrap-based approach. Our proposed method preserves various characteristics of mutual fund databases, including entry/exit points for each fund (i.e., missing data) and cross-sectional information. We show that our proposed bootstrap tests have a near-optimal size and exhibit greater power compared to widely used standard bootstrap methods for evaluating mutual fund performance. Additionally, we present a new approach to picking the top-performing mutual funds. Our empirical results indicate that a measurable fraction of funds outperform the market.