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Data Mining for Bankruptcy Prediction: An Experiment in Vietnam

Wybrane pełne teksty z tego czasopisma
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
In the history of the world economy, the bankruptcy of some large companies has caused global financial crises. The study aimed to postulate a model of bankruptcy prediction for listed companies on Vietnam's stock market. The research used six popular algorithms in data mining to predict bankruptcy risk with data collected from 4693 observations in the period 2009-2020. The research results showed that Logistic algorithms, Artificial Neural Network, Decision Tree have a high level of predicting bankruptcy with an accuracy of 98%. The study identified the three most important indicators: inventory turnover ratio, debt to equity ratio, and debt ratio that affect the corporate bankruptcy prediction. The study showed the threshold points of 10-indicators to avoid bankruptcy likelihood. These results recommended that the model could be applied in practice to reduce risks for businesses and investors in the Vietnamese market.
Rocznik
Tom
Strony
175--184
Opis fizyczny
Bibliogr. 35 poz., tab., wykr., il.
Twórcy
  • Faculty of Accounting and Auditing Hanoi University of Industry 298, Cau Dien street, Bac Tu Liem, Hanoi, Vietnam
  • Faculty of Accounting and Auditing Hanoi University of Industry 298, Cau Dien street, Bac Tu Liem, Hanoi, Vietnam
Bibliografia
  • 1. The World Bank. (2021). The World Bank in Vietnam: Overview [webpage]. Available: https://www.worldbank.org/en/country/vietnam/overview#1
  • 2. R. C. Merton, "On the pricing of corporate debt: The risk structure of interest rates," The Journal of Finance, vol. 29, no. 2, pp. 449-470, 1974.
  • 3. E. I. Altman, "Financial ratios, discriminant analysis and the prediction of corporate bankruptcy," The Journal of Finance, vol. 23, no. 4, pp. 589-609, 1968.
  • 4. V. T. T. Binh, N.-M. Tran, D. M. Thanh, and H.-H. Pham, "Firm size, business sector and quality of accounting information systems: Evidence from Vietnam," Accounting, vol. 6, no. 3, pp. 327-334, 2020.
  • 5. D. Liang, C.-C. Lu, C.-F. Tsai, and G.-A. Shih, "Financial ratios and corporate governance indicators in bankruptcy prediction: A comprehensive study," European Journal of Operational Research vol. 252, no. 2, pp. 561–572, 2016.
  • 6. F. Barboza, H. Kimura, and E. Altman, "Machine learning models and bankruptcy prediction," Expert Systems with Applications, vol. 83, pp. 405-417, 2017.
  • 7. C.-H. Chou, S.-C. Hsieh, and C.-J. Qiu, "Hybrid genetic algorithm and fuzzy clustering for bankruptcy prediction," Applied Soft Computing, vol. 56, pp. 298-316, 2017.
  • 8. F. Antunes, B. Ribeiro, and F. Pereira, "Probabilistic modeling and visualization for bankruptcy prediction," Applied Soft Computing, vol. 60, pp. 831-843, 2017.
  • 9. T. Le, M. Y. Lee, J. R. Park, and S. W. Baik, "Oversampling techniques for bankruptcy prediction: novel features from a transaction dataset," Symmetry, vol. 10, no. 4, p. 79, 2018.
  • 10. T. Le, H. Le Son, M. T. Vo, M. Y. Lee, and S. W. Baik, "A cluster-based boosting algorithm for bankruptcy prediction in a highly imbalanced dataset," Symmetry, vol. 10, no. 7, p. 250, 2018.
  • 11. D. Veganzones and E. Séverin, "An investigation of bankruptcy prediction in imbalanced datasets," Decision Support Systems, vol. 112, pp. 111-124, 2018.
  • 12. F. Mai, S. Tian, C. Lee, and L. Ma, "Deep learning models for bankruptcy prediction using textual disclosures," European journal of operational research, vol. 274, no. 2, pp. 743-758, 2019.
  • 13. H. Son, C. Hyun, D. Phan, and H. J. Hwang, "Data analytic approach for bankruptcy prediction," Expert Systems with Applications, vol. 138, p. 112816, 2019.
  • 14. Z. Chen, W. Chen, and Y. Shi, "Ensemble learning with label proportions for bankruptcy prediction," Expert Systems with Applications, vol. 146, p. 113155, 2020.
  • 15. E. I. Altman, "Predicting financial distress of companies: revisiting the Z-score and ZETA® models," in Handbook of research methods and applications in empirical finance: Edward Elgar Publishing, 2013, pp. 428–456.
  • 16. Z. H. U. Konglai and L. I. Jingjing, "Studies of discriminant analysis and logistic regression model application in credit risk for China’s listed companies," Management Science and Engineering, vol. 4, no. 4, pp. 24-32, 2011.
  • 17. J. A. Ohlson, "Financial ratios and the probabilistic prediction of bankruptcy," Journal of accounting research, vol. 18, no. 1, pp. 109-131, 1980.
  • 18. S. Meeampol, P. Lerskullawat, A. Wongsorntham, P. Srinammuang, V. Rodpetch, and R. Noonoi, "Applying emerging market Z-score model to predict bankruptcy: A case study of listed companies in the stock exchange of Thailand (Set)," in Management, Knowledge And Learning International Conference, 2014, pp. 25-27.
  • 19. M. N. Kumar and V. S. H. Rao, "A new methodology for estimating internal credit risk and bankruptcy prediction under Basel II Regime," Computational Economics, vol. 46, no. 1, pp. 83-102, 2015.
  • 20. P. R. Kumar and V. Ravi, "Bankruptcy prediction in banks and firms via statistical and intelligent techniques–A review," European journal of operational research, vol. 180, no. 1, pp. 1-28, 2007.
  • 21. C. Serrano-Cinca, "Self organizing neural networks for financial diagnosis," Decision support systems, vol. 17, no. 3, pp. 227-238, 1996.
  • 22. D. Fletcher and E. Goss, "Forecasting with neural networks: an application using bankruptcy data," Information & Management, vol. 24, no. 3, pp. 159-167, 1993.
  • 23. A. Fan and M. Palaniswami, "Selecting bankruptcy predictors using a support vector machine approach," in Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium, 2000, vol. 6, pp. 354-359: IEEE.
  • 24. A. W. Ghazali, N. A. Shafie, and Z. M. Sanusi, "Earnings management: An analysis of opportunistic behaviour, monitoring mechanism and financial distress," Procedia Economics and Finance, vol. 28, pp. 190-201, 2015.
  • 25. J. Berkson, "Application of the logistic function to bio-assay," Journal of the American statistical association, vol. 39, no. 227, pp. 357-365, 1944.
  • 26. E. I. Altman, G. Marco, and F. Varetto, "Corporate distress diagnosis: Comparisons using linear discriminant analysis and neural networks (the Italian experience)," Journal of Banking & Finance, vol. 18, no. 3, pp. 505-529, 1994.
  • 27. A. M. Flitman, "Towards analysing student failures: neural networks compared with regression analysis and multiple discriminant analysis," Computers & Operations Research, vol. 24, no. 4, pp. 367-377, 1997.
  • 28. B. P. Carlin and T. A. Louis, Bayes and empirical Bayes methods for data analysis. Chapman & Hall/CRC, 2000.
  • 29. R. J. Hyndman and G. Athanasopoulos, Forecasting: Principles and Practice (2nd ed). Melbourne, Australia: OTexts, 2018.
  • 30. S. Suthaharan, "Support vector machine," in Machine learning models and algorithms for big data classification: Springer, 2016, pp. 207-235.
  • 31. W. A. Belson, "Matching and prediction on the principle of biological classification," Journal of the Royal Statistical Society: Series C (Applied Statistics), vol. 8, no. 2, pp. 65-75, 1959.
  • 32. J. R. Quinlan, "Improved use of continuous attributes in C4. 5," Journal of artificial intelligence research, vol. 4, pp. 77-90, 1996.
  • 33. J. R. Quinlan, "Induction of decision trees," Machine learning, vol. 1, no. 1, pp. 81-106, 1986.
  • 34. L. Breiman, "Bagging predictors," Machine learning, vol. 24, no. 2, pp. 123-140, 1996.
  • 35. R. Schapire and Y. Freund, "A decision-theoretic generalization of on-line learning and an application to boosting," in Second European Conference on Computational Learning Theory, 1995, pp. 23-37.
Uwagi
1. Preface
2. Session: International Conference on Research in Management and Technovation
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-06a84dff-7dfc-4683-a9cf-a979ce447360
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