Evaluating Hedge Funds with Pooled Benchmarks

• 成果类型: Article
• 署名作者: O'Doherty, Michael S.; Savin, N. E.; Tiwari, Ashish
• 署名单位: University of Missouri System; University of Missouri Columbia; University of Iowa; University of Iowa
• 刊物名称: MANAGEMENT SCIENCE
• ISSN/ISSBN: 0025-1909
• DOI: 10.1287/mnsc.2014.2056
• 发表日期: 2016
• 页码: 69-89
• 关键词: HEDGE FUNDS; Performance evaluation; model pooling; model combination; hedge fund replication; log score

摘要

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. The weights assigned to the individual models in the pool are based on the log score criterion, an information-theoretic measure of the conditional performance of a model. We illustrate the advantages of a pooled benchmark over alternative approaches, including the Fung and Hsieh [Fung W, Hsieh DA (2004) Hedge fund benchmarks: A risk-based approach. Financial Analysts J. 60: 65-80] model, stepwise regression methods, and style-adjusted methods in the contexts of a real-time investment strategy, hedge fund replication, and fund failure prediction.