Hedge Fund Return Prediction and Fund Selection: A Machine-Learning Approach

Jiaqi Chen, Michael Tindall and Wenbo Wu

No 16-4, Occasional Papers from Federal Reserve Bank of Dallas

Abstract: A machine-learning approach is employed to forecast hedge fund returns and perform individual hedge fund selection within major hedge fund style categories. Hedge fund selection is treated as a cross-sectional supervised learning process based on direct forecasts of future returns. The inputs to the machine-learning models are observed hedge fund characteristics. Various learning processes including the lasso, random forest methods, gradient boosting methods, and deep neural networks are applied to predict fund performance. They all outperform the corresponding style index as well as a benchmark model, which forecasts hedge fund returns using macroeconomic variables. The best results are obtained from machine-learning processes that utilize model averaging, model shrinkage, and nonlinear interactions among the factors.

Keywords: hedge fund return prediction; gradient boosting; machine learning; deep neural networks; random forest; lasso; Hedge fund selection (search for similar items in EconPapers)
Pages: 37 pages
Date: 2016-11-01
New Economics Papers: this item is included in nep-big and nep-cmp
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Citations: View citations in EconPapers (2)

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