Building a model to warn of the risk of default for Vietnamese joint stock commercial banks

shows neural network model, decision tree- two models belonging to the branch of model using intelligent techniques to increase the classification performance.

+ Fifth: The thesis quantifies the level of difference and characteristics of each bank that affect the possibility of default. At the same time, it points out four banks with high potential risk of default that need to be comprehensively considered.

+ Sixth: From the experimental construction of the debt warning model in the thesis, the author also proposes a debt warning process for Vietnamese commercial banks.

+ Seventh: From the results achieved, the author proposes a number of solutions and recommendations to banks and management agencies to help banks limit the risk of bankruptcy.

Proposed future research directions

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To continue to build a more complete model of warning of debt risk for Vietnamese commercial banks, the author proposes some future research directions that can implement some main contents as follows:

+ Firstly , due to the limitation in accessing data sources used for the research, the author used bad debt indicators and performance classification as the basis for determining the risk of bank default. Other studies can search for classification criteria, test and compare research results according to these criteria.

+ Second, other studies can experiment with models such as survival analysis models, feature analysis models, genetic algorithms, etc. and compare model selection. Other studies can also find ways to combine multiple methods and models in the study to increase classification performance.

+ Third, further research is needed into model building and testing of predictive performance with samples outside the model.

+ Fourth , other studies, after calculating the probability of default and ranking banks, can calculate the bank's reclassification matrix or build a model to determine the factors affecting the bank's reclassification.

LIST OF PUBLISHED WORKS OF THE AUTHOR

1. Dang Huy Ngan, Phung Minh Duc (2013), 'Business bankruptcy warning models', Proceedings of the national conference: Training and application of Mathematics in socio-economics , National Economics University Publishing House, pp. 243-249.

2. Dang Huy Ngan (2015), 'Using a combination of factor analysis and logistic regression to classify Vietnamese joint stock commercial banks', Proceedings of the national scientific conference: Vietnam's financial and monetary security in the context of international integration , National Economics University Publishing House, pp. 102-114.

3. Dang Huy Ngan (2015), 'To reduce the risk of default of joint stock commercial banks', Economic and Forecast Magazine , No. 22, pp. 25-28.

4. Dang Huy Ngan (2016), 'Using neural networks to classify and forecast the risk of default of Vietnamese joint stock commercial banks', Journal of Economics and Forecasting , special issue T1/2016, pp. 6-9.

5. Dang Huy Ngan (2016), 'Building a model to warn of debt default risk for Vietnamese joint stock commercial banks', Journal of Economics and Development , Special issue T9/2016, pp. 82-90.

6. Dang Huy Ngan (2017), 'Applying Logit model with array data in the study of default warning of Vietnamese joint stock commercial banks', Proceedings of the 4th National Conference on Mathematics Applications , Information and Communication Publishing House, pp. 157-166.

LIST OF REFERENCES

1. Altman, Edward I. (1968), 'Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy', Journal of Finance , p.189-209.

2. Atlman, E.I., Hartzell J., Peck M. (1995), Emerging Markets Corporate Bonds: A Scoring System , Salomon Brothers Inc. New York.

3. Atlman, EI, Heine, ML, Zhang, L., &Yen, J. (2007), 'Corporate financial distress diagnosis in China', Salomon Center Working Paper , New York University.

4. Amri, PD and Kocher, BM (2012), 'The Political Economy of Financial Sector Supervision and Banking Crises: A cross-Country Analysis', European Law Journal , 18(1), p. 24-43.

5. Balcaen, S., and Ooghe, H. (2006), '35 years of studies on business failure: An overview of the classic statistical methodologies and their related problems', The British Accounting Review , 38(1), p. 63-93.

6. Barr, Richard, and Thomas Siems (1994), 'Predicting Bank Failure Using DEA to Quantify Management Quality' , Federal Reserve Bank of Dallas, Financial Industry Studies, Working Paper No. 1-94.

7. Banker, R.D., Charnes, A., and Cooper, W, W (1984), 'Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis', Management Science , 30(9), p.1079-1092.

8. Basel II (2008), International unification of measurement and capital standards, Culture-Information Publishing House, Hanoi.

9. Beaver, W. (1966), 'Financial ratios as predictors of failure', Journal of Accounting Research 5: p. 71-111.

10. Birsen Eygi Erdogan (2016), 'Long-term Examination of Bank Crashes Using Panel Logistic Regression: Turkish Banks Failure Case', International Journal of Statistics and Probability ; Vol. 5, No. 3.

11. Berger, AN, De Young, R. (1997), 'Problem loans and cost efficiency in commercial Banks', Journal of Banking And Finance , (21)6, pp.849-870.

12. Bureau of Business Research (1930), A Test Analysis of Unsuccessful Industrial Companies , Bulletin No.31. Urbana: University of Illinois Press.

13. Blum, M. (1974), 'Failing company discrimination analysis', Journal of Accounting research, 2(1),p. 1-25.

14. Charnes, A., Cooper, W. and Rhodes, E. (1978), 'Measuring efficiency of decision making units', European journal of operations research , 6(3), p.429-444.

15. Crouhy, M., Galai, D. & Mark (2001), Risk Management , Mc Graw-Hill.

16. Coelli, T. (1996), “A guide to DEAP version 2.1: a data envelopment analysis program”, CEPA Working Paper 1996/08, A vailable at: http://www.une.edu.au/econometrics/ cepa.htm.

17. Stoxplus Joint Stock Company. http://www.stoxplus.com.

18. David W.Hosmer, Jr., Stanley Lemeshow, Rodney X.Studivant (2013), Applied Logistic Regression , Third Edition.

19. Demirguc-Kunt, Asli (1989), 'Deposit-Institution Failures: A Review of the Empirical Literature', Federal ReserveBank of Cleveland, Economic Review , Quarter 4.

20. Deakin, EB (1972), 'A discriminant analysis of predictors of business failure',

Journal of Accounting research , p. 167-179.

21. Dimitras, AI, Zanakis, SH and Zopounidis, C. (1996),'A survey of business failure with an emphasis on prediction methods and industrial applications', European Journal of Operational Research , Vol.90, p. 487-513.

22. Ding, Y., Song, X., & Yen, Y. (2008), 'Forecasting financial condition of Chinese listed companies based on support vector machine', Expert System with Applications , 34 (4), p. 3081-3089.

23. Distingguin, Isabelle, Rous, Philippe, and Tarazi, Amine (2005), 'Market Discipline and the Use of Stock Market Data to Predict Bank Financial Distress'. Working papers .

24. Duclaux, Soupmo Badjio (2009), 'A warning model for bank default in CEMAC countries', HEC Management School-University of LIEGE.

25. Eisenbeis, Robert, and Simon Kwan (1994), 'An Analysis of Inefficiencies in Banking: A Stochastic Cost Frontier Approach' , Workingpaper.

26. Farrell, MJ (1957), 'The measurement of productive efficiency', Journal of the Royal Statistical Society , 120, p. 253-281.

27. Farrell, R., Grosskopf, S. and Lovell, CA K (1985), The Measurement of Efficiency of Production , KluwerNijhoff, Boston.

28. Fitzpatrick, P. (1932), 'A Comparison of the Ratios of Successful Industrial Enterprises with Those of Failed Companies', The Accountants Purishing Company .

29. Frederic S. Mishkin (1999), Money, banking and financial markets , Science and Technology Publishing House.

30. Frydman, H., Altman, E. and Kao, D. (1985), 'Introducing recursive partitioning for financial classification: the case of financial distress', The Journal of Finance , Vol. XL No.1, pp. 269-91.

31. Gleb Lanine and Rudi Vander Vennet (2006), 'Failure prediction in the Russian bank sector with Logit and trait recognition models', Expert systems with application 30(3), p. 436-478.

32. Gropp, Reint, Vesala Jukka, and Vulpes Giuseppe (2005), 'Equity and bond market signs as leading indicators of bank fragility'. Journal of Money Credit and Banking, upcoming .

33. Hausman, JA (1978), 'Specification tests in econometrics', Econometrica , 46, 1251-1271.

34. Hosmer, David W. Jr. and Stanley Lemeshow (1989), Applied Logistic regression , John Wiley and Son, New York.

35. Hughes, Joseph P., and Choon-Geol Moon (1995), 'Measuring Bank Efficiency when Managers Trade Return for Reduced Risk' , Working paper.

36. Isabelle Distinguin and Amine Tarazi (2008), 'The use of accounting and stock market data to predict bank rating changes: The case of south east Asia', University de Limoges , France.

37. Jo, H., & Han, I. (1996), 'Integration of case-based forecasting neural network and discriminant analysis for bankruptcy prediction', Expert Systems with applications , 11(4), 415-422.

38. Kolari, J., Glennon, D., Shin, H., and Caputo, M. (2002), 'Predicting Large US Commercial Bank Failures', Journal of Economics and Business , 54(321), p. 361-387.

39. Le Dan (2004), Applying statistical methods to analyze the performance of Vietnamese commercial banks , PhD thesis in economics, National Economics University, Hanoi.

40. Le Khuong Ninh (2015), 'Causes of bankruptcy of commercial banks: Theory and lessons learned from practice', Banking Magazine , No. 20, pp. 47-49.

41. Le Thi Hong Hanh (2013), 'Resolving bad debt - a key issue in restructuring the banking system', Information and Documentation Center Magazine , No. 01, 2013.

42. Lin, H., & Sun, J. (2009), 'Predicting financial failure using multiple case-based reasoning combined with support vector machine', Expert Systems with Applications , 36(6), p.10085-10096.

43. Marais ML, Patel J. Wolfson M. (1984), 'The experimental design of classification models: an application of recursive partitioning and bootstrapping to commercial bank loan classification', Journal of Accounting , Vol. 22, pp. 87-113.

44. Martens, D., Bruynseels, L., Baesens, B., Willekens, M., & Vanthienen, J. (2008), 'Predicting going concern opinion with data mining', Decision Support Systems , 45(4), p.765-777.

45. Martin, D. (1977), 'Early warning of bank failures: A logit regression approach',

Journal of Banking and Finance 1: p. 249-276.

46. Merwin, C. (1942), 'Financing Small Corporations: In Five Manufacturing Industries, 1926-36', National Bureau of Economic Research .

47. Metron, R. (1974), “On the pricing of corporate debt: the risk structure of interest rates”, Journal of Finance , Vol.29 No.2, pp.449-70.

48. State Bank (2008), Decision 06/2008/QD-NHNN on promulgating Regulations on classification of joint stock commercial banks, issued on March 12, 2008.

49. State Bank (2013), Circular No. 02/2013/TT-NHNN on classification of assets, provisioning levels, methods of setting up risk provisions and use of provisions to handle risks in the operations of credit institutions and foreign bank branches, issued on January 21, 2013.

50. State Bank (2013), Circular No. 19/2013/TT-NHNN on purchasing, selling and handling bad debts of Asset Management Companies of Vietnamese credit institutions, issued on September 6, 2013.

51. State Bank (2014), Circular No. 14/2014/TT-NHNN on amending and supplementing a number of articles of the regulations on debt classification, provisioning and use of reserves to handle credit risks in banking activities of credit institutions issued under Decision No. 493/2005/QD-NHNN, issued on May 20, 2014.

52. State Bank (2014), Circular No. 36/2014/TT-NHNN on regulations on limits and safety ratios in the operations of credit institutions and foreign bank branches, issued on November 20, 2014.

53. State Bank of Vietnam (2005), Decision No. 493/QD-NHNN on debt classification, provisioning and use of reserves to handle credit risks in banking activities of credit institutions , issued on April 22, 2005.

54. State Bank of Vietnam (2009 to 2015), Annual Report, Hanoi.

55. Vietnam Commercial Bank (2009 to 2015), Annual Report .

56. Nguyen Quang Dong (2009), Credit rating of Vietnamese banks and financial institutions using discriminant analysis method , ministerial-level scientific topic, National Economics University.

57. Nguyen Cao Van, Tran Thai Ninh, Ngo Van Thu (2012), Textbook of Probability Theory and Mathematical Statistics , National Economic Publishing House, Hanoi.

58. Nguyen Minh Phong (2012), Bad debt - causes and solutions

59. Nguyen Phi Lan (2015), Building and applying structural models in inspection and supervision of credit institutions , Industry-level scientific topic, State Bank.

60. Nguyen Thi Hong Vinh (2015), 'Factors affecting bad debt of Vietnamese commercial banks', Economic Development Journal , No. 26, pp.80-98.

61. Nguyen Thi Luong (2014), Applying the Merton-KMV model to measure the default risk of listed enterprises in Vietnam, Master's thesis in economics, National Economics University, Hanoi.

62. Nguyen Trong Hoa (2009), Building a social enterprise model for Vietnamese enterprises in a transitional economy, PhD thesis in economics, National Economics University, Hanoi.

63. Nguyen Thanh Cuong and Pham The Anh (2010), 'Assessing the bankruptcy risk of seafood processing enterprises listed on the Vietnamese stock market', Journal of Fisheries Science and Technology , No. 2, pp. 27-33.

64. Nguyen Van Tien (2003), Commercial banking textbook , Statistical Publishing House.

65. Nguyen Viet Hung, Ha Quynh Hoa (2012), Currency crisis forecasting models and warning applications for Vietnam , National Economic Publishing House, Hanoi.

66. Nguyen Xuan Thanh (2016), Vietnam's commercial banks: From changes in laws and policies in the period 2006-2010 to restructuring events in the period 2011-2015 , accessed April 16, 2016, from http://www.thesaigontimes,vn

/143595/Buc-tranh-ngan-hang-Viet-Nam-10-nam-qua.html.

67. Odom, M. and R. Sharda (1993),'A neural network model for bankruptcy prediction', Article in Neural networks in Finance and Investing: Using Artificial Intelligence to improve Real-World Performance , R. Trippi and E. Turban (eds.). Chicago, IL: Probus Publishing Co.

68. Ohlson, J. (1980), 'Financial ratios and probabilistic prediction of bankruptcy',

Journal of Accounting Research 18(1), p. 109-131.

69. Phan Hong Mai (2012), Bankruptcy risk of listed construction joint stock companies in Vietnam , Basic science topic, National Economics University.

70. Phan Hong Mai, Cao Duc Anh (2014), 'Factors affecting the bad debt ratio of Vietnamese commercial banks', Journal of Economics & Development , No. 207(II), pp.80-89.

71. Pham Thi Kim Anh (2014), 'Bad debt in banks and issues that need to be handled', Science and Technology Magazine , February 2014.

72. National Assembly (2010), Law on Credit Institutions , promulgated on June 16, 2010.

73. National Assembly (2014), Bankruptcy Law , promulgated on June 19, 2014.

74. Ravi, V. and C. Pramodh (2008), 'Threshold accepting trained principal component neural network and feature subset selection: application to bankruptcy prediction in banks', Applied Soft Computing, Vol. 8 (4), p.1539-1548.

75. Resti, Andrea (1995), 'Linear Programming and Econometric Methods for Bank Efficiency Evaluation: An Empirical Comparison Based on a Panel of Italian Banks' , working paper.

76. Salchenberger, LM, Cinar, EM and Lash, NA (1992), 'Neural networks: a new tool for predicting thrift failures', Decision Sciences , Vol. 23, p. 899-916.

77. Shumway, T. (2001), 'Forecasting bankruptcy more accurately: a simple hazard model', The Journal of Businesses , 74, p. 101-124.