A Multi-Stage Financial Distress Early Warning System: Analyzing Corporate Insolvency with Random Forest
Author
Abstract
Suggested Citation
Download full text from publisher
References listed on IDEAS
- Liran Einav & Jonathan Levin, 2014.
"The Data Revolution and Economic Analysis,"
Innovation Policy and the Economy, University of Chicago Press, vol. 14(1), pages 1-24.
- Liran Einav & Jonathan Levin, 2013. "The Data Revolution and Economic Analysis," NBER Chapters, in: Innovation Policy and the Economy, Volume 14, pages 1-24, National Bureau of Economic Research, Inc.
- Liran Einav & Jonathan D. Levin, 2013. "The Data Revolution and Economic Analysis," NBER Working Papers 19035, National Bureau of Economic Research, Inc.
- Liran Einav & Johnathan Levin, 2013. "The Data Revolution and Economic Analysis," Discussion Papers 12-017, Stanford Institute for Economic Policy Research.
- Drehmann, Mathias & Juselius, Mikael, 2014.
"Evaluating early warning indicators of banking crises: Satisfying policy requirements,"
International Journal of Forecasting, Elsevier, vol. 30(3), pages 759-780.
- Mathias Drehmann, 2013. "Evaluating early warning indicators of banking crises: Satisfying policy requirements," BIS Working Papers 421, Bank for International Settlements.
- Katsuyuki Tanaka & Takuji Kinkyo & Shigeyuki Hamori, 2018.
"Financial Hazard Map: Financial Vulnerability Predicted by a Random Forests Classification Model,"
Sustainability, MDPI, vol. 10(5), pages 1-18, May.
- Katsuyuki Tanaka & Takuji Kinkyo & Shigeyuki Hamori, 2017. "Financial Hazard Map: Financial Vulnerability Predicted by a Random Forests Classification Model," Discussion Papers 1720, Graduate School of Economics, Kobe University.
- Jiaming Liu & Chengzhang Li & Peng Ouyang & Jiajia Liu & Chong Wu, 2023. "Interpreting the prediction results of the tree‐based gradient boosting models for financial distress prediction with an explainable machine learning approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(5), pages 1112-1137, August.
- Climent, Francisco & Momparler, Alexandre & Carmona, Pedro, 2019. "Anticipating bank distress in the Eurozone: An Extreme Gradient Boosting approach," Journal of Business Research, Elsevier, vol. 101(C), pages 885-896.
- Hal R. Varian, 2014. "Big Data: New Tricks for Econometrics," Journal of Economic Perspectives, American Economic Association, vol. 28(2), pages 3-28, Spring.
- Pindado, Julio & Rodrigues, Luis & de la Torre, Chabela, 2008. "Estimating financial distress likelihood," Journal of Business Research, Elsevier, vol. 61(9), pages 995-1003, September.
- William F. Messier, Jr. & James V. Hansen, 1988. "Inducing Rules for Expert System Development: An Example Using Default and Bankruptcy Data," Management Science, INFORMS, vol. 34(12), pages 1403-1415, December.
- Jayasekera, Ranadeva, 2018. "Prediction of company failure: Past, present and promising directions for the future," International Review of Financial Analysis, Elsevier, vol. 55(C), pages 196-208.
- Almaskati, Nawaf & Bird, Ron & Yeung, Danny & Lu, Yue, 2021. "A horse race of models and estimation methods for predicting bankruptcy," Advances in accounting, Elsevier, vol. 52(C).
- Altman, Edward I. & Marco, Giancarlo & Varetto, Franco, 1994. "Corporate distress diagnosis: Comparisons using linear discriminant analysis and neural networks (the Italian experience)," Journal of Banking & Finance, Elsevier, vol. 18(3), pages 505-529, May.
- Miglani, Seema & Ahmed, Kamran & Henry, Darren, 2015. "Voluntary corporate governance structure and financial distress: Evidence from Australia," Journal of Contemporary Accounting and Economics, Elsevier, vol. 11(1), pages 18-30.
- Ohlson, Ja, 1980. "Financial Ratios And The Probabilistic Prediction Of Bankruptcy," Journal of Accounting Research, Wiley Blackwell, vol. 18(1), pages 109-131.
- Turetsky, Howard F & McEwen, Ruth Ann, 2001. "An Empirical Investigation of Firm Longevity: A Model of the Ex Ante Predictors of Financial Distress," Review of Quantitative Finance and Accounting, Springer, vol. 16(4), pages 323-343, June.
- Marek Vochozka & Jaromir Vrbka & Petr Suler, 2020. "Bankruptcy or Success? The Effective Prediction of a Company’s Financial Development Using LSTM," Sustainability, MDPI, vol. 12(18), pages 1-17, September.
- Stewart Jones & David Johnstone & Roy Wilson, 2017. "Predicting Corporate Bankruptcy: An Evaluation of Alternative Statistical Frameworks," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 44(1-2), pages 3-34, January.
- Bi-Huei Tsai, 2013. "An Early Warning System of Financial Distress Using Multinomial Logit Models and a Bootstrapping Approach," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 49(S2), pages 43-69, March.
- Tanaka, Katsuyuki & Kinkyo, Takuji & Hamori, Shigeyuki, 2016. "Random forests-based early warning system for bank failures," Economics Letters, Elsevier, vol. 148(C), pages 118-121.
- Tian, Shaonan & Yu, Yan & Guo, Hui, 2015. "Variable selection and corporate bankruptcy forecasts," Journal of Banking & Finance, Elsevier, vol. 52(C), pages 89-100.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Antulov-Fantulin, Nino & Lagravinese, Raffaele & Resce, Giuliano, 2021. "Predicting bankruptcy of local government: A machine learning approach," Journal of Economic Behavior & Organization, Elsevier, vol. 183(C), pages 681-699.
- Alberto Tron & Maurizio Dallocchio & Salvatore Ferri & Federico Colantoni, 2023. "Corporate governance and financial distress: lessons learned from an unconventional approach," Journal of Management & Governance, Springer;Accademia Italiana di Economia Aziendale (AIDEA), vol. 27(2), pages 425-456, June.
- Hoang Hiep Nguyen & Jean-Laurent Viviani & Sami Ben Jabeur, 2023. "Bankruptcy prediction using machine learning and Shapley additive explanations," Post-Print hal-04223161, HAL.
- Beynon, Malcolm J. & Peel, Michael J., 2001. "Variable precision rough set theory and data discretisation: an application to corporate failure prediction," Omega, Elsevier, vol. 29(6), pages 561-576, December.
- Katsuyuki Tanaka & Takuji Kinkyo & Shigeyuki Hamori, 2018.
"Financial Hazard Map: Financial Vulnerability Predicted by a Random Forests Classification Model,"
Sustainability, MDPI, vol. 10(5), pages 1-18, May.
- Katsuyuki Tanaka & Takuji Kinkyo & Shigeyuki Hamori, 2017. "Financial Hazard Map: Financial Vulnerability Predicted by a Random Forests Classification Model," Discussion Papers 1720, Graduate School of Economics, Kobe University.
- Pablo de Llano Monelos & Manuel RodrÃguez López & Carlos Piñeiro Sánchez, 2013. "Bankruptcy Prediction Models in Galician companies. Application of Parametric Methodologies and Artificial Intelligence," International Journal of Economics & Business Administration (IJEBA), International Journal of Economics & Business Administration (IJEBA), vol. 0(1), pages 117-136.
- Beutel, Johannes & List, Sophia & von Schweinitz, Gregor, 2019. "Does machine learning help us predict banking crises?," Journal of Financial Stability, Elsevier, vol. 45(C).
- Casado Yusta, Silvia & Nœ–ez Letamendía, Laura & Pacheco Bonrostro, Joaqu’n Antonio, 2018. "Predicting Corporate Failure: The GRASP-LOGIT Model || Predicci—n de la quiebra empresarial: el modelo GRASP-LOGIT," Revista de Métodos Cuantitativos para la Economía y la Empresa = Journal of Quantitative Methods for Economics and Business Administration, Universidad Pablo de Olavide, Department of Quantitative Methods for Economics and Business Administration, vol. 26(1), pages 294-314, Diciembre.
- Christian Lohmann & Steffen Möllenhoff & Thorsten Ohliger, 2023. "Nonlinear relationships in bankruptcy prediction and their effect on the profitability of bankruptcy prediction models," Journal of Business Economics, Springer, vol. 93(9), pages 1661-1690, November.
- Mohamed Salah Elzalabany, 2025. "Market Responses to Financial Distress: A Comparative Study of the U.S. and Chinese Markets," International Journal of Science and Business, IJSAB International, vol. 45(1), pages 14-29.
- Takuo Higashide & Katsuyuki Tanaka & Takuji Kinkyo & Shigeyuki Hamori, 2021. "New Dataset for Forecasting Realized Volatility: Is the Tokyo Stock Exchange Co-Location Dataset Helpful for Expansion of the Heterogeneous Autoregressive Model in the Japanese Stock Market?," JRFM, MDPI, vol. 14(5), pages 1-18, May.
- Lanbiao Liu & Chen Chen & Bo Wang, 2022. "Predicting financial crises with machine learning methods," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(5), pages 871-910, August.
- Ali Namaki & Reza Eyvazloo & Shahin Ramtinnia, 2023. "A systematic review of early warning systems in finance," Papers 2310.00490, arXiv.org.
- Sami Ben Jabeur & Youssef Fahmi, 2014. "Les modèles de prévision de la défaillance des entreprises françaises : une approche comparative," Working Papers 2014-317, Department of Research, Ipag Business School.
- Stewart Jones, 2017. "Corporate bankruptcy prediction: a high dimensional analysis," Review of Accounting Studies, Springer, vol. 22(3), pages 1366-1422, September.
- fernández, María t. Tascón & gutiérrez, Francisco J. Castaño, 2012. "Variables y Modelos Para La Identificación y Predicción Del Fracaso Empresarial: Revisión de La Investigación Empírica Reciente," Revista de Contabilidad - Spanish Accounting Review, Elsevier, vol. 15(1), pages 7-58.
- Zhichao Luo & Pingyu Hsu & Ni Xu, 2020. "SME Default Prediction Framework with the Effective Use of External Public Credit Data," Sustainability, MDPI, vol. 12(18), pages 1-18, September.
- Alessandro Bitetto & Stefano Filomeni & Michele Modina, 2021. "Understanding corporate default using Random Forest: The role of accounting and market information," DEM Working Papers Series 205, University of Pavia, Department of Economics and Management.
- Xavier Brédart & Eric Séverin & David Veganzones, 2021. "Human resources and corporate failure prediction modeling: Evidence from Belgium," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(7), pages 1325-1341, November.
- Dimitras, A. I. & Slowinski, R. & Susmaga, R. & Zopounidis, C., 1999. "Business failure prediction using rough sets," European Journal of Operational Research, Elsevier, vol. 114(2), pages 263-280, April.
More about this item
Keywords
random forest; data science; company insolvency and bankruptcy; financial distress; financial vulnerability; economic activity;All these keywords.
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jjrfmx:v:18:y:2025:i:4:p:195-:d:1628059. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.