Hybrid Machine Learning–Econometric Framework for Financial Distress Scoring: Evidence from German Manufacturing Firms

Abstract

Nowadays, the European economy faces significant global challenges that threaten the continuity of economic growth, especially in the German manufacturing sector, which is under strain from financial turmoil, resulting in numerous layoffs and firm closures. In this respect, FinTech significantly contributes to addressing these issues by providing data-driven analytical tools that improve the assessment and monitoring of firms’ financial position. However, in the literature, we have not found any paper that uses machine learning (ML) algorithms to assess the financial distress of German manufacturing firms, highlighting methodological and sectoral gaps that need to be bridged. Therefore, this study aims to develop an econometric and ML-based financial distress scoring model for German manufacturing firms by estimating contemporaneous Altman Z-scores that provide better insights into the financial distress determinants, enabling better financial management. The econometric findings revealed that the regression model has an adjusted R-squared value of 86%, confirming that the selected firm-specific and macroeconomic factors play a substantial role in explaining financial distress. The findings recommend that German manufacturing businesses retain more earnings rather than distributing them as dividends, while reducing their debt in capital structures to enhance financial stability. Moreover, the ML results found that Gradient Boosting and Random Forest have the highest accuracy scores among the ML methods, suggesting that these models provide strong capability for assessing financial distress and supporting more effective financial risk management, allowing firms to effectively respond to the threats of a dynamic environment and thereby better support the growth of the German and European economies. Keywords financial distress, machine learning, Altman Z-score, German manufacturing