Return Smoothing and its Implications for Performance Analysis of Hedge Funds

Abstract

Return smoothing and performance persistence are both sources of autocorrelation in hedge fund returns. The practice of pre-processing the data in order to remove smoothing before conducting performance analysis also affects the predictability of hedge fund returns. This paper develops a Bayesian framework for the performance evaluation of hedge funds that simultaneously accounts for smoothing, time-varying performance and factor loadings, and the short-lived nature of reported returns. Simulation evidence reveals that “unsmoothing” predictable, persistent hedge fund returns reduces the ability to detect performance persistence in the second step of the analysis. Empirically, smoothing generates severe biases in standard estimates of abnormal performance, factor loadings, and idiosyncratic volatility. In particular, for funds with high systematic risk, a standard deviation increase in smoothing implies an upward bias in α in excess of 2% annually and a downward bias in equity market beta of more than 20%. For funds with low systematic risk exposure, the smoothing bias is most apparent in estimates of idiosyncratic volatility.