Evaluating Hedge Funds with Pooled Benchmarks
提出一种模型池化方法，通过组合多个归因模型构建基金专属基准，以降低基准误差对技能推断的污染，并在实时投资策略、基金复制和失败预测中展示优势。
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. This paper was accepted by Wei Jiang, finance.