A Technique to Predict Bankruptcy Using Ultimate Ownership Network as Key Indicators
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25530%2F25%3A39922780" target="_blank" >RIV/00216275:25530/25:39922780 - isvavai.cz</a>
Result on the web
<a href="https://ijtech.eng.ui.ac.id/article/view/7516" target="_blank" >https://ijtech.eng.ui.ac.id/article/view/7516</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.14716/ijtech.v16i1.7516" target="_blank" >10.14716/ijtech.v16i1.7516</a>
Alternative languages
Result language
angličtina
Original language name
A Technique to Predict Bankruptcy Using Ultimate Ownership Network as Key Indicators
Original language description
Predicting bankruptcy is crucial to avert company failures, which could lead to a systemic collapse of the economy. This study examines the network of executives, directors, and shareholders to identify conglomerates, which are often characterized by a lack of explicit connections between these individuals or institutions. Understanding these networks is crucial for mitigating the risk of bankruptcy and its potential systemic effects. We proposed a technique that uses non-financial factors that could serve as predictors of bankruptcy, as well as the link among the ultimate owners. A regression analysis is employed to evaluate the network's effect on bankruptcy prediction. The findings indicate a significant impact of the directors' degree of centrality and the direct bankruptcy rate of director and executive networks on the likelihood of bankruptcy. Additionally, the predictions for one and two years ahead are significantly influenced by the strength or weighted degree of centrality and betweenness centrality of directors. Notably, the influence of executive and shareholder indirect bankruptcy rates becomes increasingly prominent in predicting distress. These results offer a novel perspective on incorporating network variables into bankruptcy prediction models, with an accuracy of 86% using random forest and XGBoost models. The findings indicate that bankruptcy prediction techniques can employ network variables, as alternative data to financial indicators.
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
10200 - Computer and information sciences
Result continuities
Project
—
Continuities
N - Vyzkumna aktivita podporovana z neverejnych 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
International Journal of Technology
ISSN
2086-9614
e-ISSN
2087-2100
Volume of the periodical
16
Issue of the periodical within the volume
1
Country of publishing house
ID - INDONESIA
Number of pages
14
Pages from-to
275-288
UT code for WoS article
001417352700019
EID of the result in the Scopus database
2-s2.0-85217475994