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Mutual Funds: New Perspectives

Paper Session

Friday, Jan. 3, 2020 10:15 AM - 12:15 PM (PDT)

Manchester Grand Hyatt, Seaport F
Hosted By: American Finance Association
  • Chair: Marcin Kacperczyk, Imperial College London

Managerial Structure and Performance Induced Trading

Anastassia Fedyk
University of California-Berkeley
Saurin Patel
Western University
Sergei Sarkissian
McGill University


We propose a new channel through which teamwork improves mutual fund activity: by offsetting individual overconfidence, teams mitigate excessive performance-induced trading. The predictions of our theoretical model are confirmed in the data. Team-managed funds trade less after good performance than single-managed funds, and the magnitude of this differential increases with team size. Moreover, changes from single- to team-management correspond to lower performance-induced trading. Our results cannot be explained by alternative explanations, including manager experience, gender, and fund flows. Overconfident trading by single-managed funds results in lower next-period returns compared to team-managed funds. Our findings indicate that team-management reduces uninformed overconfident trading.

Thousands of Alpha Tests

Stefano Giglio
Yale University
Yuan Liao
Rutgers University
Dacheng Xiu
University of Chicago


Data snooping is a major concern in empirical asset pricing. By exploiting the “blessings of dimensionality” we develop a new framework to rigorously perform multiple hypothesis testing in linear asset pricing models, while limiting the occurrence of false positive results typically associated with data-snooping. We first develop alpha test statistics that are asymptotically valid, allow for weak dependence in the cross-section, and are robust to the possibility of omitted factors. We then combine them in a multiple-testing procedure that ensures that the rate of false discoveries is ex-ante bounded below a prespecified 5% level. We also show that this method can detect all positive alphas with reasonable strength. Our procedure is designed for high-dimensional settings and works even when the number of tests is large relative to the sample size, as in many finance applications. We illustrate the empirical relevance of our methodology in the context of hedge fund performance (alpha) evaluation. We find that our procedure is able to select – among more than 3,000 available funds – a subset of funds that displays superior in-sample and out-of-sample performance compared to the funds selected by standard methods.

The Allocation of Talent across Mutual Fund Strategies

Andrea Buffa
Boston University
Apoorva Javadekar
Indian School of Business


We propose a theory of self-selection by mutual fund managers into stock “picking” and market “timing.” With adverse selection, investors learn more easily about the skill of picking funds than of timing funds, since picking investments are less correlated than timing investments. The equilibrium allocation of talent across strategies is such that high-skill managers always pick, while low-skill managers time with positive probability. We empirically confirm the prediction that picking funds outperform timing funds, even though picking does not outperform timing as a strategy. Consistent with the investors’ learning in our model, picking funds exhibit higher flow-performance sensitivity than timing funds, and low-skill managers have the incentive to rely more on timing strategies when their reputation, or aggregate volatility, increase.
Lu Zheng
University of California-Irvine
Rossen Valkanov
University of California-San Diego
Stijn Van Nieuwerburgh
Columbia University
JEL Classifications
  • G1 - General Financial Markets