EFFECT OF THE COMPANY RELATIONSHIP NETWORK ON DEFAULT PREDICTION: EVIDENCE FROM CHINESE LISTED COMPANIES

Guotai Chi (), Ying Zhou, Long Shen (), Jian Xiong () and Hongjia Yan ()
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Guotai Chi: School of Economics and Management, Dalian University of Technology, No. 2 Linggong Road, Ganjingzi District, Dalian City 116024, P. R. China
Ying Zhou: School of Economics and Management, Dalian University of Technology, No. 2 Linggong Road, Ganjingzi District, Dalian City 116024, P. R. China
Long Shen: School of Economics and Management, Dalian University of Technology, No. 2 Linggong Road, Ganjingzi District, Dalian City 116024, P. R. China
Jian Xiong: School of Economics and Management, Dalian University of Technology, No. 2 Linggong Road, Ganjingzi District, Dalian City 116024, P. R. China
Hongjia Yan: School of Economics and Management, Dalian University of Technology, No. 2 Linggong Road, Ganjingzi District, Dalian City 116024, P. R. China

International Journal of Theoretical and Applied Finance (IJTAF), 2022, vol. 25, issue 06, 1-22

Abstract: The default risk of listed companies not only threatens the interests of enterprises and internal staff but also leads the investors to face significant financial losses. Thus, this study attempts to establish an effective default prediction system for better corporate governance. In present times, it is not uncommon for a senior manager to serve in two or more companies. Our contribution has threefold. First, we construct an indicator system of default prediction for Chinese listed companies by considering the company relationship score. Then, we reversely infer the optimal ratios of the default and nondefault companies’ degrees of influence on their related companies with the maximum area under the curve (AUC). Third, the empirical results show that the default prediction accuracy is improved by using our indicator system that includes the company relationship score.

Keywords: Relationship network; indicator systems; default prediction; SVM; big data (search for similar items in EconPapers)
JEL-codes: C13 C44 G32 G33 (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (1)

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DOI: 10.1142/S021902492250025X

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