Financial Distress Warning and Risk Path Analysis for Chinese Listed Companies: An Interpretable Machine Learning Approach

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Financial Distress Warning and Risk Path Analysis for Chinese Listed Companies: An Interpretable Machine Learning Approach

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Financial Distress Warning and Risk Path Analysis for Chinese Listed Companies: An Interpretable Machine Learning Approach

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

50200 - Economics and Business

Projekt

—

Návaznosti

V - Vyzkumna aktivita podporovana z jinych verejnych zdroju

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

ECONOMIC MODELLING

ISSN

0264-9993

e-ISSN

1873-6122

Svazek periodika

152

Číslo periodika v rámci svazku

November

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

15

Strana od-do

1-15

Kód UT WoS článku

001565684500001

EID výsledku v databázi Scopus

2-s2.0-105014535097