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.
Forecasting stock returns is extremely challenging in general, and this task becomes even more difficult given the turbulent nature of the Chinese stock market. We address the stock selection process as a statistical learning problem and build crosssectional forecast models to select individual stocks in the Shanghai Composite Index. Decile portfolios are formed according to rankings of the forecasted future cumulative returns. The equity market's neutral portfolio-formed by buying the top decile portfolio and selling short the bottom decile portfolio-exhibits superior performance to, and a low correlation with, the Shanghai Composite Index. To make our strategy more useful to practitioners, we evaluate the proposed stock selection strategy's performance by allowing only long positions, and by investing only in Ashare stocks to incorporate the restrictions in the Chinese stock market. The longonly strategies still generate robust and superior performance compared to the Shanghai Composite Index. A close examination of the coefficients of the features provides more insights into the changes in market dynamics from period to period.
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