1. Aharony, J., Jones, C. P., & Swary, I. (1980). An Analysis of Risk and Return Characteristics of Corporate Bankruptcy Using Capital Market Data. Journal of Finance, 1001-1016.
2. Altman, E. I. (1968). Financial ratios, discriminant analysis and the predication of corporate bankruptcy. The Journal of Finance, 23(4), 589-609.
3. Altman, E. I., & Hotchkiss, E. (2006). Corporate financial distress and bankruptcy. New Jersey: John Wiley & Sons, Inc.
4. Altman, E. I., Zang, L., & Yen, J. (2007). Corporate financial distress diagnosis in China. Salomon Center Working Paper, 1-30.
5. Aziz, A., Emanuel, D. C., & Lawson, G. H. (1988). Bankruptcy Prediction - an investigation of cash flow based models. Journal of Management Studies, 419-437.
6. Balcaen, S., & 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), 63-93.
7. Balleisen, E. (2001). Navigating failure: Bankruptcy and commercial society in Antebelum America. Chapel Hill: University of North Carolina Press.
8. Beaver, W. H. (1966). Financial ratios as predictors of failure, Empirical Research in Accounting: Selected Studies. Supplement to Journal of Accounting Research, 71-111.
9. Bellovary, J. L., Giacomino, D. E., & Akers, M. D. (2007). A Review of Bankruptcy Prediction Studies: 1930 to Present. Journal of Financial Education, 1-42.
10. Brigham, E. F., & Daves, P. R. (2003). Intermediate Financial Management, 8th Edition. New York: McGraw Hill.
11. Clark, T. A., & Weinstein, M. I. (1983). The Behavior of the Common Stock of Bankrupt Firms. Journal of Finance, 489-504.
12. Cortes, C., & Vapnik, V. (1995). Support vector network. Machine Learning 20, 273–297.
13. Härdle, W., Moro, R., & Schäfer, D. (2005). Predicting Bankruptcy with Support Vector Machines. In Statistical Tools for Finance and Insurance (pp. 225-248). Berlin, Heidelberg: Springer.
14. Ko, L. J., Blocher, E. J., & Lin, P. P. (2003). Prediction of Corporate Financial Distress: An Application of the Composite Rule Induction System. The International Journal of Digital Accounting Research, 1(1), 69-85.
15. Lin, F., Liang, D., & Chen, E. (2011). Financial ratio selection for business crisis prediction. Expert Systems with Applications(38), 15094-15102.
16. Lin, F., Yeh, C.-C., & Lee, M.-Y. (2011). The use of hybrid manifold learning and support vector machines in the prediction of business failure. Knowledge-Based Systems(24), 90-101.
17. Liou, D.-K., & Smith, M. (2006). Macroeconomic Variables in the identification of Financial distress. Perth, Western Australia: Edith Cowan University.
18. Liang, S., & Wang, J. (2020). Advanced Remote Sensing 2nd Edition. Cambridge, Massachusetts: Academic Press.
19. Liang, D., Lu, C.-C., Tsai, C.-F., & Shih, G.-A. (2016). Financial ratios and corporate governance indicators in bankruptcy prediction: A comprehensive study. European Journal of Operational Research(252), 561-572.
20. Mensah, Y. (1984). An Examination Of The Stationarity Of Multivariate Bankruptcy Prediction Models - A Methodological Study. Journal of Accounting Research, 380-395.
21. Min, J. H., & Lee, Y.-C. (2005). Bankruptcy prediction using support vector machine. Expert Systems with Applications(28), 603–614.
22. Mishra, S., & Datta-Gupta, A. (2018). Applied Statistical Modeling and Data Analytics. Cambrige, MA: Elsevier.
23. Ohlson, J. A. (1980). Financial ratio and probabilistic prediction of bankruptcy. Journal of Accounting Research, 109-131.
24. Platt, H. D., & Platt, M. B. (2002). Predicting corporate financial distress: Reflections on choice-based sample bias. Journal of Economics and Finance, 184–199.
25. Ross, S. A., Westerfield, R. W., & Jordan, B. D. (2003). Fundamentals of corporate finance (6 ed.). New York: The McGraw-Hill Companies.
26. Shin, K.-S., Lee, T. S., & Kim, H.-j. (2005). An application of support vector machines in bankruptcy prediction model. Expert Systems with Applications, 28, 127-135.
27. Springate, G. L. V. (1978). Predicting the possibility of failure in a Canadian firm (Unpublished master’s thesis). Simon Fraser University, Canada.
28. Shobha, G., & Rangaswamy, S. (2018). Handbook of Statistics Vol. 38. Elsevier
29. Sun, J., & Hui, X.-F. (2006). An application of decision tree and genetic algorithms for financial ratios' dynamic selection and financial distress prediction. Proceedings of the Fifth International Conference on Machine Learning and Cybernetics (pp. 2413-2418). Harbin: School of Management, Harbin Institute of Technology.
30. Vapnik, V. (1995). The Nature of Statistical Learning Theory. New York: Springer Verlag.
31. Wood, D., & Piesse, J. (1987). The Information Value of Mda Based Financial Indicators. Journal of Business Finance & Accounting, 27-38.
32. Woolf, B. P. (2007). Building Intelligent Interactive Tutors. San Francisco, California: Morgan Kaufmann.
33. Wruck, K. H. (1990). Financial distress, reorganization, and organizational efficiency. Journal of Financial Economics, 27(2), 419-444.
34. Xu, W., Xiao, Z., Dang, X., Yang, D., & Yang, X. (2014). Financial ratio selection for business failure prediction using soft set theory. Knowledge-Based Systems, 63, 59-67.
35. Zmijewski, M. (1984). Methodological issues related to the estimation of financial distress prediction models. Journal of Accounting Research, 59-82.
36. Zywicki, T. J. (2008). Concise encyclopedia of economics 2nd Edison. Indianapolis: Library of Economics and Liberty.