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

Nils-Gunnar Birkeland Abrahamsen, Emil Nylén-Forthun, Mats Møller, Petter Eilif de Lange and Morten Risstad ()
Additional contact information
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

JRFM, 2024, vol. 17, issue 10, 1-23

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.

Keywords: financial distress prediction; credit risk; machine learning; explainable AI; Nordics (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1911-8074/17/10/432/pdf (application/pdf)
https://www.mdpi.com/1911-8074/17/10/432/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jjrfmx:v:17:y:2024:i:10:p:432-:d:1487547

Access Statistics for this article

JRFM is currently edited by Ms. Chelthy Cheng

More articles in JRFM from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().