Machine Learning and Zombie Firms Classification
Source abstract
Abstract
We investigate whether the machine learning technique helps to identify zombie firms. We also analyze the differences in zombie indicators proposed by previous research.revious studies successfully classified firms as zombies by focusing on whether they receive subsidized credits. However, when the policy interest rate is low, it becomes more challenging to identify zombies, because low-interest payments by firms can be caused by lenders’ support to zombies and by low policy interest rates. According to our machine learning approach, we show that we can predict zombie firms from financial information that is publicly available even when the policy interest rate is low. We also find that the financial accounts important for predicting zombie firms differ for every zombie indicator, suggesting that these indicators reflect different aspects of firms’ status.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: COVID-19 Pandemic Impacts
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Gradulate School of Business Administration, Hitotsubashi University, 2-1 Naka, Kunitachi, Tokyo 186-8601, Japan
Funding
Promotion of Science
References (20)
Show source-provided references
1. Economics of the living dead10.1111/j.1468-5876.2006.00354.x ↗
2. Zombie lending and depressed restructuring in Japan10.1257/aer.98.5.1943 ↗
3. The heterogeneous effects of COVID-19 on labor markets: People’s movement and non-pharmaceutical interventions10.1016/j.jjie.2021.101170 ↗
4. Why did ‘zombie’firms recover in Japan?10.1111/j.1467-9701.2011.01368.x ↗
5. Resource orchestration in practice: CEO emphasis on SHRM, commitment-based HR systems, and firm performance10.1002/smj.2217 ↗
6. Unfinished business: Zombie firms among SME in Japan’s lost decades10.1016/j.japwor.2018.09.007 ↗
7. A panel study of zombie SMEs in Japan: Identification, borrowing and investment behavior10.1016/j.jjie.2015.12.001 ↗
8. Identifying the real effects of zombie lending10.1093/rcfs/cfaa010 ↗
9. Machine learning for zombie hunting: Predicting distress from firms’ accounts and missing values10.1093/icc/dtad049 ↗
10. Unnatural selection: Perverse incentives and the misallocation of credit in Japan10.1257/0002828054825691 ↗
11. The walking dead? Zombie firms and productivity performance in OECD countries10.1093/epolic/eiy012 ↗
12. Zombie Credit and (Dis-) Inflation: Evidence from Europe10.1111/jofi.13342 ↗
13. Banerjee, R., and Hofmann, B. (2018). The rise of zombie firms: Causes and consequences. BIS Q. Rev., 67–78.
14. Hong MG, H., Igan MD, O., and Lee, D. (2021). Zombies on the Brink: Evidence from Japan on the Reversal of Monetary Policy Effectiveness, International Monetary Fund.10.5089/9781513570037.001 ↗
15. Athey, S. (2018). The impact of machine learning on economics. The Economics of Artificial Intelligence: An Agenda, University of Chicago Press.10.7208/chicago/9780226613475.003.0021 ↗
16. Targeting with machine learning: An application to a tax rebate program in Italy10.1016/j.jebo.2018.09.010 ↗
17. Machine learning in the service of policy targeting: The case of public credit guarantees10.1016/j.jebo.2022.04.004 ↗
18. Machine learning models and bankruptcy prediction10.1016/j.eswa.2017.04.006 ↗
19. Mining corporate annual reports for intelligent detection of financial statement fraud—A comparative study of machine learning methods10.1016/j.knosys.2017.05.001 ↗
20. Measuring corporate culture using machine learning10.1093/rfs/hhaa079 ↗