A Technique to Predict Bankruptcy Using Ultimate Ownership Network as Key Indicators

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

A Technique to Predict Bankruptcy Using Ultimate Ownership Network as Key Indicators

Popis výsledku v původním jazyce

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&apos;s effect on bankruptcy prediction. The findings indicate a significant impact of the directors&apos; 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.

Název v anglickém jazyce

A Technique to Predict Bankruptcy Using Ultimate Ownership Network as Key Indicators

Popis výsledku anglicky

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&apos;s effect on bankruptcy prediction. The findings indicate a significant impact of the directors&apos; 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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10200 - Computer and information sciences

Projekt

—

Návaznosti

N - Vyzkumna aktivita podporovana z neverejnych zdroju

Rok uplatnění

2025

Kód důvěrnosti údajů

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

Název periodika

International Journal of Technology

ISSN

2086-9614

e-ISSN

2087-2100

Svazek periodika

16

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

ID - Indonéská republika

Počet stran výsledku

14

Strana od-do

275-288

Kód UT WoS článku

001417352700019

EID výsledku v databázi Scopus

2-s2.0-85217475994