Is It Possible to Detect the Insolvency of a Company?

Katsuyuki Tanaka, Takuo Higashide, Takuji Kinkyo and Shigeyuki Hamori
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Takuo Higashide: au Asset Management Corporation, JAPAN
Takuji Kinkyo: Graduate School of Economics, Kobe University, JAPAN

No DP2024-34, Discussion Paper Series from Research Institute for Economics & Business Administration, Kobe University

Abstract: As corporate sector stability is critical for economic stability and development, machine learning has become a popular tool for constructing an early warning system (EWS) to detect a company's financial vulnerabilities more accurately. Although most of the EWS literature focuses on constructing bankruptcy prediction models, bankruptcy is not the only indicator of a company's financial fragility. This study uses random forest modelling to systematically investigate the possibility of detecting 1) the financial signs of a company falling into a financially fragile condition of insolvency, and 2) whether insolvent companies fall into bankruptcy. We also analyse how the financial conditions of insolvent companies differ from those of active and bankrupt companies. Our empirical study shows that highly accurate insolvency models can be built to detect status changes from active to insolvent and from insolvent to bankrupt. Our analysis also shows that the financial criteria for the status change from active to insolvent and are quite different from those of a change from insolvent to bankrupt. The criteria of the former are due to structural and operational ratios, whereas those for the latter are due to further financial distress in operational and profitability ratios.

Keywords: Random forest; Data science; Company insolvency and bankruptcy; Financial distress; Financial vulnerability; Economic activity (search for similar items in EconPapers)
JEL-codes: C0 G0 (search for similar items in EconPapers)
Pages: 16 pages
Date: 2024-10
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