Evaluating Hedge Funds with Pooled Benchmarks

Abstract

The evaluation of hedge fund performance is challenging given the flexible nature of hedge funds' strategies and their lack of operational transparency. As a result inference about skill is inevitably contaminated by the error in the benchmark model. To address this concern, we propose a model pooling approach to develop a fund-specific benchmark obtained by pooling a set of diverse attribution models. We illustrate the advantages of a pooled benchmark over alternative approaches, including the Fung and Hsieh (2004) model and stepwise regression methods, in the contexts of a real-time investment strategy, hedge fund replication, and fund failure prediction.

Keywords: Hedge funds, Model pooling, Model combination, Performance evaluation, Predictive distributions, Log predictive score

JEL Classification: C11, C52, C53, G12