An ensemble model minimising misjudgment cost: Empirical evidence from Chinese listed companies
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25410%2F25%3A39923431" target="_blank" >RIV/00216275:25410/25:39923431 - isvavai.cz</a>
Result on the web
<a href="https://onlinelibrary.wiley.com/doi/full/10.1002/ijfe.3097" target="_blank" >https://onlinelibrary.wiley.com/doi/full/10.1002/ijfe.3097</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1002/ijfe.3097" target="_blank" >10.1002/ijfe.3097</a>
Alternative languages
Result language
angličtina
Original language name
An ensemble model minimising misjudgment cost: Empirical evidence from Chinese listed companies
Original language description
Predicting corporate financial distress is critical for bank lending and corporate bond investment decisions. Incorrect identification of default status can mislead lenders and investors, leading to substantial losses. This paper proposes an ensemble model that minimises the overall cost of misjudgment by considering the imbalanced ratio weighted loss of the unbalanced ratio of Type I and Type II errors in the objective function. Unlike existing static financial distress prediction models, the proposed model integrates panel data by using time-shifting to account for credit risk dynamics. To validate the prediction model, data were collected for Chinese listed companies, considering geographic area, ownership structure and firm size. We demonstrate that by weighting predictions from different classification models, the overall misjudgment cost can be minimised. This study identifies earnings per share and the product price index as the most relevant indicators affecting the financial performance of Chinese-listed companies. Overall, the results indicate that the proposed model has a predictive capacity of up to 5 years, with 98.7% for 1-year forecasting horizons and 96.8% for 5-year-ahead forecasting horizons. Furthermore, the proposed model outperforms existing distress prediction models in overall prediction performance by correctly identifying defaulting companies while avoiding misjudging good companies.
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
50206 - Finance
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
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
International Journal of Finance & Economics
ISSN
1076-9307
e-ISSN
1099-1158
Volume of the periodical
30
Issue of the periodical within the volume
4
Country of publishing house
US - UNITED STATES
Number of pages
26
Pages from-to
3875-3900
UT code for WoS article
001596318100039
EID of the result in the Scopus database
2-s2.0-85216445957