Reassessing False Discoveries in Mutual Fund Performance: Skill, Luck, or Lack of Power? A Reply
37 Pages Posted: 21 Aug 2019 Last revised: 25 Mar 2020
Date Written: October 24, 2019
Andrikogiannopoulou and Papakonstantinou (AP; 2019) conduct an inquiry into the bias of the False Discovery Rate (FDR) estimators of Barras, Scaillet, and Wermers (BSW; 2010). In this Reply, we replicate their results, then further explore the bias issue by (i) using different parameter values, and (ii) updating the sample period. Over the original period (1975-2006), we show how reasonable adjustments to the parameter choices made by BSW and AP results in a sizeable reduction in the bias relative to AP. Over the updated period (1975-2018), we further show that the performance of the FDR improves dramatically across a large range of parameter values. Specifically, we find that the probability of misclassifying a fund with a true alpha of 2% per year is 32% (versus 65% in AP). Our results, in combination with those of AP, indicate that the use of the FDR in finance should be accompanied by a careful evaluation of the underlying data generating process, especially when the sample size is small.
Keywords: False Discovery Rate, Multiple Testing, Mutual Fund Performance
JEL Classification: C11, G12, G23
Suggested Citation: Suggested Citation