Financial Distress Warning and Risk Path Analysis for Chinese Listed Companies: An Interpretable Machine Learning Approach
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14560%2F25%3A00141872" target="_blank" >RIV/00216224:14560/25:00141872 - isvavai.cz</a>
Result on the web
<a href="https://www.sciencedirect.com/science/article/pii/S0264999325002834" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0264999325002834</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.econmod.2025.107288" target="_blank" >10.1016/j.econmod.2025.107288</a>
Alternative languages
Result language
angličtina
Original language name
Financial Distress Warning and Risk Path Analysis for Chinese Listed Companies: An Interpretable Machine Learning Approach
Original language description
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.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
50200 - Economics and Business
Result continuities
Project
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Continuities
V - Vyzkumna aktivita podporovana z jinych verejnych zdroju
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
ECONOMIC MODELLING
ISSN
0264-9993
e-ISSN
1873-6122
Volume of the periodical
152
Issue of the periodical within the volume
November
Country of publishing house
NL - THE KINGDOM OF THE NETHERLANDS
Number of pages
15
Pages from-to
1-15
UT code for WoS article
001565684500001
EID of the result in the Scopus database
2-s2.0-105014535097