Shangkun Deng , Yongqi Li , Yingke Zhu , Bingsen Wang , Hong Ning , Siyu Yi , Tatsuro Shimada
{"title":"中国上市公司财务困境预警与风险路径分析:一种可解释的机器学习方法","authors":"Shangkun Deng , Yongqi Li , Yingke Zhu , Bingsen Wang , Hong Ning , Siyu Yi , Tatsuro Shimada","doi":"10.1016/j.econmod.2025.107288","DOIUrl":null,"url":null,"abstract":"<div><div>Financial distress is typically not a sudden occurrence, but rather the outcome of accumulated operational inefficiencies and external pressures. In China’s capital market, existing financial distress warning models offer limited interpretability, making it challenging for regulators to obtain a reliable basis for risk identification. To address this limitation, we propose an interpretable machine learning framework that integrates extreme gradient boosting with non-dominated sorting genetic algorithm II for multiobjective optimization, and Shapley additive explanations with interpretive structural modeling to reveal both the marginal effects and the risk formation pathways of financial indicators. Using empirical data from A-share listed firms between 2010 and 2024, the optimized model demonstrates a 3.32 % improvement in warning accuracy and a 2.15 % gain in efficiency compared with benchmark models. Furthermore, the findings show that the predictive influence of profitability diminishes as the lead time before financial distress increases. Overall, this study presents an interpretable model that enables regulators and policymakers to identify financial risks at earlier stages and implement targeted interventions in the market environment.</div></div>","PeriodicalId":48419,"journal":{"name":"Economic Modelling","volume":"152 ","pages":"Article 107288"},"PeriodicalIF":4.7000,"publicationDate":"2025-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Financial distress warning and risk path analysis for Chinese listed companies: An interpretable machine learning approach\",\"authors\":\"Shangkun Deng , Yongqi Li , Yingke Zhu , Bingsen Wang , Hong Ning , Siyu Yi , Tatsuro Shimada\",\"doi\":\"10.1016/j.econmod.2025.107288\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Financial distress is typically not a sudden occurrence, but rather the outcome of accumulated operational inefficiencies and external pressures. In China’s capital market, existing financial distress warning models offer limited interpretability, making it challenging for regulators to obtain a reliable basis for risk identification. To address this limitation, we propose an interpretable machine learning framework that integrates extreme gradient boosting with non-dominated sorting genetic algorithm II for multiobjective optimization, and Shapley additive explanations with interpretive structural modeling to reveal both the marginal effects and the risk formation pathways of financial indicators. Using empirical data from A-share listed firms between 2010 and 2024, the optimized model demonstrates a 3.32 % improvement in warning accuracy and a 2.15 % gain in efficiency compared with benchmark models. Furthermore, the findings show that the predictive influence of profitability diminishes as the lead time before financial distress increases. Overall, this study presents an interpretable model that enables regulators and policymakers to identify financial risks at earlier stages and implement targeted interventions in the market environment.</div></div>\",\"PeriodicalId\":48419,\"journal\":{\"name\":\"Economic Modelling\",\"volume\":\"152 \",\"pages\":\"Article 107288\"},\"PeriodicalIF\":4.7000,\"publicationDate\":\"2025-08-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Economic Modelling\",\"FirstCategoryId\":\"96\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0264999325002834\",\"RegionNum\":2,\"RegionCategory\":\"经济学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ECONOMICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Economic Modelling","FirstCategoryId":"96","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0264999325002834","RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ECONOMICS","Score":null,"Total":0}
Financial distress warning and risk path analysis for Chinese listed companies: An interpretable machine learning approach
Financial distress is typically not a sudden occurrence, but rather the outcome of accumulated operational inefficiencies and external pressures. In China’s capital market, existing financial distress warning models offer limited interpretability, making it challenging for regulators to obtain a reliable basis for risk identification. To address this limitation, we propose an interpretable machine learning framework that integrates extreme gradient boosting with non-dominated sorting genetic algorithm II for multiobjective optimization, and Shapley additive explanations with interpretive structural modeling to reveal both the marginal effects and the risk formation pathways of financial indicators. Using empirical data from A-share listed firms between 2010 and 2024, the optimized model demonstrates a 3.32 % improvement in warning accuracy and a 2.15 % gain in efficiency compared with benchmark models. Furthermore, the findings show that the predictive influence of profitability diminishes as the lead time before financial distress increases. Overall, this study presents an interpretable model that enables regulators and policymakers to identify financial risks at earlier stages and implement targeted interventions in the market environment.
期刊介绍:
Economic Modelling fills a major gap in the economics literature, providing a single source of both theoretical and applied papers on economic modelling. The journal prime objective is to provide an international review of the state-of-the-art in economic modelling. Economic Modelling publishes the complete versions of many large-scale models of industrially advanced economies which have been developed for policy analysis. Examples are the Bank of England Model and the US Federal Reserve Board Model which had hitherto been unpublished. As individual models are revised and updated, the journal publishes subsequent papers dealing with these revisions, so keeping its readers as up to date as possible.