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Artificial Neural Networks and Gradient-Boosting Decision Trees in Time Series Forecasting of Earnings per Share in Poland

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  • Wojciech Kuryłek

Abstract

Precise forecasting of earnings for publicly traded companies holds significant importance in achieving investment success. It is highly important in countries where the coverage of these companies by financial analysts’ forecasts is relatively small, like Poland. This paper examines the prediction errors of modern machine learning and deep learning techniques within univariate time series settings. These methods are applied to earnings per share (EPS) data for companies listed on the Polish stock market during the period spanning from the 2008–2009 financial crisis to the 2020 pandemic shock. The seasonal random walk (SRW) model achieved the lowest error.

Suggested Citation

  • Wojciech Kuryłek, 2026. "Artificial Neural Networks and Gradient-Boosting Decision Trees in Time Series Forecasting of Earnings per Share in Poland," Eastern European Economics, Taylor & Francis Journals, vol. 64(2), pages 206-227, March.
    • Handle: RePEc:mes:eaeuec:v:64:y:2026:i:2:p:206-227
    DOI: 10.1080/00128775.2024.2429137
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