Issue: 2026/Vol.36/No.2, Pages 59-84
SELECTION OF OVER TIME STABILITY RATIOS USING MACHINE LEARNING TECHNIQUES
Sebastian Klaudiusz Tomczak
, Aleksander Denisiuk
Cite as: S. K. Tomczak, A. Denisiuk. Selection of over time stability ratios using machine learning techniques. Operations Research and Decisions 2026: 36(2), 59-84. DOI 10.37190/ord215251
Abstract
According to the data provided by Coface platform, there are almost 3.8 million registered companies in the Visegrad Group (V4), with a significantly increased number of bankruptcies over the last years. Therefore, the main aim of this paper is to identify stable key indicators that determine the financial condition of these companies, which is of crucial importance for stakeholders and investors. To address this topic, we rely on the original dataset consisting of 145,638 company-years from the V4 countries, covering six main sectors during the period of 2018-2021. We calculate 78 financial and non-financial ratios, and we build a robust framework for the identification of the most important ones. Our framework relies on explainable machine learning techniques followed by cross-country and cross-sectional comparisons of the indicators. The results reveal that most of the non-financial indicators included in the analysis are important in assessing the financial condition of companies.
Keywords: random forest, GINI index, Shapley values, financial distress, COVID-19
Received: 8 September 2025 Accepted: 7 December 2025
Published online: 7 December 2025