No easy way out: dissecting firm heterogeneity to enhance default risk prediction

Frame of the research: Effective risk assessment is central to managerial decision-making in financial institutions, corporate finance, and strategic planning. Drawing from prior studies on default risk, this paper investigates how the predictive ability of financial indicators varies depending on firm characteristics. Purpose of the paper: This study seeks to explore how firm-level heterogeneity is associated with varying levels of predictive strength of financial indicators for default risk, examining how industry, technological levels, size, and age shape the extent to which such indicators are able to predict a firm’s likelihood of default. Methodology: The analysis relies on a sample of 121,809 Italian firms sourced from the AIDA database. Logistic regression and random forests are employed to assess the extent to which financial indicators - grouped into liquidity, efficiency, profitability, and growth - predict default risk across firm-specific contingencies. Findings: Results indicate that default risk is more strongly associated with: (a) liquidity indicators in service-oriented firms, (b) efficiency ratios in high-tech firms, (c) profitability measures in smaller firms, and (d) growth indicators in younger firms. These findings support the use of tailored prediction models rather than generalized approaches to default risk prediction. Research limits: The study mainly focuses on incorporated firms and relies primarily on quantitative financial indicators, potentially overlooking qualitative factors and unincorporated micro enterprises. Practical implications: The study points toward the refinement of risk assessment models through the incorporation of firm-level contingencies. This, in turn, has implications for managers, policymakers and institutions involved in SME financing or credit scoring. Originality of the paper: The paper contributes to research on default prediction by combining an integrative theoretical perspective with both statistical and machine learning techniques.