Bankruptcy prediction with fractional polynomial transformation of financial ratios

Zenon Taoushianis

European Journal of Operational Research, 2025, vol. 327, issue 2, 690-702

Abstract: We show that simple nonlinear transformations of financial ratios, within a multivariate fractional polynomial approach, yield substantial improvements in bankruptcy prediction. The approach selects optimal power functions balancing parsimony and complexity. Focusing on a dataset comprising of non-financial firms, we develop a parsimonious nonlinear logit model with minimal parameter specification and clear interpretability, outperforming linear logit models. The model improves the in-sample fit, while out-of-sample it significantly reduces costly misclassification errors and improves discriminatory power. Similar insights are obtained when applying fractional polynomials on a secondary dataset consisting of banking firms. Interestingly, the fractional polynomial model compares favourably with other nonlinear models. By simulating a competitive loan market, we demonstrate that the bank using the fractional polynomial model builds a higher-quality loan portfolio, resulting in superior risk-adjusted profitability compared to banks employing alternative models.

Keywords: Risk analysis; Fractional polynomials; Financial ratios; Bankruptcy prediction (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:327:y:2025:i:2:p:690-702

DOI: 10.1016/j.ejor.2025.07.036

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