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

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. This paper was accepted by David Simchi‐Levi, finance.

FinTech as a Game Changer: Overview of Research Frontiers

“…However, very few studies examine the newly developed deep learning algorithms in finance. Neural network algorithms are used and have shown good performance in predicting returns under certain circumstances (Easley et al 2020, Gu et al 2020, Wu et al 2020 with the basic feed forward algorithm. Advanced algorithms like CNN, RNN, and GAN are seldom seen in the literature.…”

“…By finding and evaluating ex ante optimal trading rules, they find technical trading rules cannot bring excess returns when factoring in transaction costs. Wu et al (2020) leverage machine learning to predict the cross-sectional returns of different hedge funds. It is hard for investors to compare and select hedge funds because of the confidentiality of their investment strategies.…”

Prediction and Key Characteristics of All-Cause Mortality in Maintenance Hemodialysis Patients

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Which firms opt for corporate social responsibility assurance? A machine learning prediction

“…On this background, the literature of late is increasingly filled with and turning to research approaches applying machine learning techniques to finance, accounting and management issues, with results suggesting the superiority of machine learning techniques in making predictions (Alanis, 2022;Gu et al, 2020;Wu et al, 2021).…”

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