Eliminating Biases in Evaluating Mutual Fund Performance from a Survivorship Free Sample

Abstract

Poor performing mutual funds are less likely to be observed in the data sets that are typically available. This so-called survivor problem can induce a substantial bias in measures of the performance of the funds and the persistence of this performance. Many studies have recently argued that survivorship bias can be avoided by analyzing a sample that contains returns on each fund up to the period of disappearance using standard techniques. Such data sets are usually referred to as 'survivorship free'. In this paper we show that the use of standard methods of analysis on a 'survivorship free' data-set typically still suffers from a bias and we show how one can easily correct for this using weights based on probit regressions. Using a sample with quarterly returns on U.S. based equity funds, we first of all model how survival probabilities depend upon historical returns, the age of the fund and upon aggregate economy-wide shocks. Subsequently we employ a Monte Carlo study to analyze the size and shape of the survivorship bias in various performance measures that arise when a 'survivorship free database' is used with standard techniques. In particular, we show that survivorship bias induces a spurious U-shape pattern in performance persistence. Finally, we show how a weighting procedure based upon probit regressions can be used to correct for the bias. In this way, we obtain bias-corrected estimates of abnormal performance relative to a one-factor and the Carhart [1997] four-factor model, as well as its persistence. Our results are in accordance with the persistence pattern found by Carhart [1997], and do not support the existence of a hot hand phenomenon in mutual fund performance.