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Predicting the Profitability of Directional Changes Using Machine Learning: Evidence from European Countries

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  • Nicholas D. Belesis

    (Department of Business Administration, University of Piraeus, 18534 Piraeus, Greece)

  • Georgios A. Papanastasopoulos

    (Department of Business Administration, University of Piraeus, 18534 Piraeus, Greece)

  • Antonios M. Vasilatos

    (Department of Business Administration, University of Piraeus, 18534 Piraeus, Greece)

Abstract

In this paper, we follow the suggestions of past literature to further explore the prediction of the profitability direction by employing different machine learning algorithms, extending the research in the European setting and examining the effect of profits mean reversion for the prediction of profitability. We provide evidence that simple algorithms like LDA can outperform classification trees if the data used are preprocessed correctly. Moreover, we use nested cross-validation and show that sample predictions can be obtained without using the classic train–test split. Overall, our prediction results are in line with previous studies, and we also found that cash flow-based measures like Free Cash Flow and Operating Cash Flow can be predicted more accurately compared to accrual-based measures like return on assets or return on equity.

Suggested Citation

  • Nicholas D. Belesis & Georgios A. Papanastasopoulos & Antonios M. Vasilatos, 2023. "Predicting the Profitability of Directional Changes Using Machine Learning: Evidence from European Countries," JRFM, MDPI, vol. 16(12), pages 1-14, December.
    • Handle: RePEc:gam:jjrfmx:v:16:y:2023:i:12:p:520-:d:1302258
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    References listed on IDEAS

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