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Automated stock picking using random forests

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  • Breitung, Christian

Abstract

We derive a stock ranking by applying a technical features-based random forest model on an international dataset of liquid stocks. Rather than predicted return, our ranking is based on outperformance probability. By applying a decile split, we find that long–short portfolios achieve Sharpe ratios of up to 1.95 and a highly significant yearly six-factor alpha of up to 21.79%. Moreover, we show that outperformance probabilities serve as a superior measure of future returns in the context of portfolio optimization. Mean–variance portfolios using this measure are less volatile and more profitable than equally- or value-weighted portfolios. Our findings are robust to firm size, regional restrictions, and non-crisis periods and cannot be explained by limits to arbitrage.

Suggested Citation

  • Breitung, Christian, 2023. "Automated stock picking using random forests," Journal of Empirical Finance, Elsevier, vol. 72(C), pages 532-556.
    • Handle: RePEc:eee:empfin:v:72:y:2023:i:c:p:532-556
    DOI: 10.1016/j.jempfin.2023.05.001
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    References listed on IDEAS

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    Cited by:

    1. Vitor Azevedo & Georg Sebastian Kaiser & Sebastian Mueller, 2023. "Stock market anomalies and machine learning across the globe," Journal of Asset Management, Palgrave Macmillan, vol. 24(5), pages 419-441, September.

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