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Headline-Driven Classification and Local Interpretation for Market Outperformance and Low-Risk Stock Prediction

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  • Daniil Karzanov

    (École polytechnique fédérale de Lausanne (EPFL))

Abstract

Financial news is one of the most influential sources for making long-term investment decisions. The goal of this paper is to learn whether it is possible, based on texts from news headlines, to select stocks with low forecasted volatility which outrun the market represented by the Standard and Poor’s 500 Index. We solve several binary classification problems using a range of machine learning and deep learning principles. The best classifiers are interpreted with a model-agnostic technique, LIME, to extract key words having the greatest impact on the probabilities of market outrun and low volatility.

Suggested Citation

  • Daniil Karzanov, 2024. "Headline-Driven Classification and Local Interpretation for Market Outperformance and Low-Risk Stock Prediction," Computational Economics, Springer;Society for Computational Economics, vol. 64(2), pages 769-788, August.
    • Handle: RePEc:kap:compec:v:64:y:2024:i:2:d:10.1007_s10614-023-10449-5
    DOI: 10.1007/s10614-023-10449-5
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    References listed on IDEAS

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    1. Burton G. Malkiel, 2003. "The Efficient Market Hypothesis and Its Critics," Journal of Economic Perspectives, American Economic Association, vol. 17(1), pages 59-82, Winter.
    2. Burton G. Malkiel, 2003. "The Efficient Market Hypothesis and Its Critics," Working Papers 111, Princeton University, Department of Economics, Center for Economic Policy Studies..
    3. Burton G. Malkiel, 2003. "The Efficient Market Hypothesis and Its Critics," Working Papers 111, Princeton University, Department of Economics, Center for Economic Policy Studies..
    4. repec:pri:cepsud:91malkiel is not listed on IDEAS
    5. Black, Fischer & Scholes, Myron S, 1973. "The Pricing of Options and Corporate Liabilities," Journal of Political Economy, University of Chicago Press, vol. 81(3), pages 637-654, May-June.
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