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Financial Distress Prediction in the Nordics: Early Warnings from Machine Learning Models

Author

Listed:
  • Nils-Gunnar Birkeland Abrahamsen

    (Department of Industrial Economics and Technology Management, Norwegian University of Science and Technology, 7491 Trondheim, Norway)

  • Emil Nylén-Forthun

    (Department of Industrial Economics and Technology Management, Norwegian University of Science and Technology, 7491 Trondheim, Norway)

  • Mats Møller

    (Department of Industrial Economics and Technology Management, Norwegian University of Science and Technology, 7491 Trondheim, Norway)

  • Petter Eilif de Lange

    (Department of International Business, Norwegian University of Science and Technology, 6001 Ålesund, Norway)

  • Morten Risstad

    (Department of Industrial Economics and Technology Management, Norwegian University of Science and Technology, 7491 Trondheim, Norway)

Abstract

This paper proposes an explicable early warning machine learning model for predicting financial distress, which generalizes across listed Nordic corporations. We develop a novel dataset, covering the period from Q1 2001 to Q2 2022, in which we combine idiosyncratic quarterly financial statement data, information from financial markets, and indicators of macroeconomic trends. The preferred LightGBM model, whose features are selected by applying explainable artificial intelligence, outperforms the benchmark models by a notable margin across evaluation metrics. We find that features related to liquidity, solvency, and size are highly important indicators of financial health and thus crucial variables for forecasting financial distress. Furthermore, we show that explicitly accounting for seasonality, in combination with entity, market, and macro information, improves model performance.

Suggested Citation

  • Nils-Gunnar Birkeland Abrahamsen & Emil Nylén-Forthun & Mats Møller & Petter Eilif de Lange & Morten Risstad, 2024. "Financial Distress Prediction in the Nordics: Early Warnings from Machine Learning Models," JRFM, MDPI, vol. 17(10), pages 1-23, September.
    • Handle: RePEc:gam:jjrfmx:v:17:y:2024:i:10:p:432-:d:1487547
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    References listed on IDEAS

    as
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