{"title":"财务比率分数阶多项式变换的破产预测","authors":"Zenon Taoushianis","doi":"10.1016/j.ejor.2025.07.036","DOIUrl":null,"url":null,"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.","PeriodicalId":55161,"journal":{"name":"European Journal of Operational Research","volume":"14 1","pages":""},"PeriodicalIF":6.0000,"publicationDate":"2025-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Bankruptcy prediction with fractional polynomial transformation of financial ratios\",\"authors\":\"Zenon Taoushianis\",\"doi\":\"10.1016/j.ejor.2025.07.036\",\"DOIUrl\":null,\"url\":null,\"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.\",\"PeriodicalId\":55161,\"journal\":{\"name\":\"European Journal of Operational Research\",\"volume\":\"14 1\",\"pages\":\"\"},\"PeriodicalIF\":6.0000,\"publicationDate\":\"2025-07-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"European Journal of Operational Research\",\"FirstCategoryId\":\"91\",\"ListUrlMain\":\"https://doi.org/10.1016/j.ejor.2025.07.036\",\"RegionNum\":2,\"RegionCategory\":\"管理学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"OPERATIONS RESEARCH & MANAGEMENT SCIENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"European Journal of Operational Research","FirstCategoryId":"91","ListUrlMain":"https://doi.org/10.1016/j.ejor.2025.07.036","RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"OPERATIONS RESEARCH & MANAGEMENT SCIENCE","Score":null,"Total":0}
Bankruptcy prediction with fractional polynomial transformation of financial ratios
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.
期刊介绍:
The European Journal of Operational Research (EJOR) publishes high quality, original papers that contribute to the methodology of operational research (OR) and to the practice of decision making.