A Cross-Sectional Machine Learning Approach for Hedge Fund Return Prediction and Selection

Wenbo Wu (), Jiaqi Chen (), Zhibin (Ben) Yang () and Michael L. Tindall ()
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Wenbo Wu: Department of Management Science and Statistics, The University of Texas at San Antonio, San Antonio, Texas 78249
Jiaqi Chen: Twin Tree Capital Management, Dallas, Texas 75225
Zhibin (Ben) Yang: Department of Operations and Business Analytics, University of Oregon, Eugene, Oregon 97403
Michael L. Tindall: Supervisory Risk and Surveillance, Federal Reserve Bank of Dallas, Dallas, Texas 75201

Management Science, 2021, vol. 67, issue 7, 4577-4601

Abstract: We apply four machine learning methods to cross-sectional return prediction for hedge fund selection. We equip the forecast model with a set of idiosyncratic features, which are derived from historical returns of a hedge fund and capture a variety of fund-specific information. Evaluating the out-of-sample performance, we find that our forecast method significantly outperforms the four styled Hedge Fund Research indices in almost all situations. Among the four machine learning methods, we find that deep neural network appears to be overall most effective. Investigating the source of methodological advantage of our method using a case study, we find that cross-sectional forecast outperforms forecast based on time series regression in most cases. Advanced modeling capabilities of machine learning further enhance these advantages. We find that the return-based features lead to higher returns than the benchmark of a set of macroderivative features, and our forecast method yields best performance when the two sets of features are combined.

Keywords: hedge fund; portfolio; return prediction; forecast; cross-sectional; machine learning; lasso; random forest; gradient boosting; deep neural network (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (16)

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