2024
DOI: 10.3390/jrfm17100432
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Financial Distress Prediction in the Nordics: Early Warnings from Machine Learning Models

Nils-Gunnar Birkeland Abrahamsen,
Emil Nylén-Forthun,
Mats Møller
et al.

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 mode… Show more

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