Soft Set Theory Oriented Forecast Combination Method for Business Failure Prediction



Xu, Wei;Xiao, Zhi

  • 투고 : 2014.04.02
  • 심사 : 2014.09.12
  • 발행 : 2016.03.31


This paper presents a new combined forecasting method that is guided by the soft set theory (CFBSS) to predict business failures with different sample sizes. The proposed method combines both qualitative analysis and quantitative analysis to improve forecasting performance. We considered an expert system (ES), logistic regression (LR), and support vector machine (SVM) as forecasting components whose weights are determined by the receiver operating characteristic (ROC) curve. The proposed procedure was applied to real data sets from Chinese listed firms. For performance comparison, single ES, LR, and SVM methods, the combined forecasting method based on equal weights (CFBEWs), the combined forecasting method based on neural networks (CFBNNs), and the combined forecasting method based on rough sets and the D-S theory (CFBRSDS) were also included in the empirical experiment. CFBSS obtains the highest forecasting accuracy and the second-best forecasting stability. The empirical results demonstrate the superior forecasting performance of our method in terms of accuracy and stability.


Business Failure Prediction;Combined Forecasting Method;Qualitative Analysis;Quantitative Analysis;Receiver Operating Characteristic Curve;Soft Set Theory


  1. G. Wang, J. Ma, and S. Yang, "An improved boosting based on feature selection for corporate bankruptcy prediction," Expert Systems with Applications, vol. 41, no. 5, pp. 2353-2361, 2014.
  2. J. Sun, H. Li, Q. H. Huang, and K. Y. He, "Predicting financial distress and corporate failure: a review from the state-of-the-art definitions, modeling, sampling, and featuring approaches," Knowledge-Based Systems, vol. 57, pp. 41-56, 2014.
  3. C. C. Yeh, D. J. Chi, and M. F. Hsu, "A hybrid approach of DEA, rough set and support vector machines for business failure prediction," Expert Systems with Applications, vol. 37, no. 2, pp. 1535-1541, 2010.
  4. W. H. Beaver, "Financial ratios and predictors of failure," Journal of Accounting Research, vol. 4, no. 4, pp. 71- 111, 1966.
  5. E. I. Altman, "Financial ratios, discriminant analysis and the prediction of corporate bankruptcy," Journal of Finance, vol. 23, no. 4, pp. 589-609, 1968.
  6. J. A. Ohlson, "Financial ratios and the probabilistic prediction of bankruptcy," Journal of Accounting Research, vol. 18, no. 1, pp. 109-131, 1980.
  7. H. J. Zmijewski, "Methodological issues related to the estimation of financial distress prediction models," Journal of Accounting Research, vol. 22, pp. 59-82, 1984.
  8. S. M. Bryant, "A case-based reasoning approach to bankruptcy prediction modeling," Intelligent Systems in Accounting, Finance and Management, vol. 6, no. 3,pp. 195-214, 1997.<195::AID-ISAF132>3.0.CO;2-F
  9. M. Adya and F. Collopy, "How effective are neural networks at forecasting and prediction? A review and evaluation," Journal of Forecasting, vol. 17, no. 5-6, pp. 481-495, 1998.<481::AID-FOR709>3.0.CO;2-Q
  10. F. Varetto, "Genetic algorithm application in the analysis of insolvency risk," Journal of Banking and Finance, vol. 22, no. 10, pp. 1421-1439, 1998.
  11. A. I. Dimitras, R. Slowinski, R. Susmaga, and C. Zopounidis, "Business failure prediction using rough sets," European Journal of Operational Research, vol. 114, no. 2, pp. 263-280, 1999.
  12. T. E. McKee and M. Greenstein, "Predicting bankruptcy using recursive partitioning and a realistically proportioned data set," Journal of Forecasting, vol. 19, no. 3, pp. 219-230, 2000.<219::AID-FOR752>3.0.CO;2-J
  13. R. C. Hwang, K. F. Cheng, and J. C. Lee, "A semiparametric method for predicting bankruptcy," Journal of Forecasting, vol. 26, no. 5, pp. 317-342, 2007.
  14. C. W. Nam, T. S. Kim, N. J. Park, and H. K. Lee , "Bankruptcy prediction using a discrete-time duration model incorporating temporal and macroeconomic dependencies," Journal of Forecasting, vol. 27, no. 6, pp. 493-506, 2008.
  15. W. Hardle, Y. J. Lee, D. Schafer, and Y. R. Yeh, "Variable selection and oversampling in the use of smooth support vector machines for predicting the default risk of companies," Journal of Forecasting, vol. 28, no. 6, pp. 512-534, 2009.
  16. Y. C. Hu and J. Ansell, "Retail default prediction by using sequential minimal optimization technique," Journal of Forecasting, vol. 28, no. 8, pp. 651-666, 2009.
  17. J. M. Bates and C. W. Granger, "The combination of forecasts," Operations Research Quarterly, vol. 20, no. 4, pp. 451-468, 1969.
  18. H. Jo and I. Han, "Integration of case-based forecasting, neural network, and discriminant analysis for bankruptcy," Expert Systems with Applications, vol. 11, no. 4, pp. 415-422, 1996.
  19. F. Y. Lin and S. McClean, "A data mining approach to the prediction of corporate failure," Knowledge-Based Systems, vol. 14, no. 3, pp. 189-195, 2001.
  20. J. Sun and H. Li, "Listed companies' financial distress prediction based on weighted majority voting combination of multiple classifiers," Expert Systems with Applications, vol. 35, no. 3, pp. 818-827, 2008.
  21. H. Li and J. Sun, "Majority voting combination of multiple case-based reasoning for financial distress prediction," Expert Systems with Applications, vol. 36, no. 3, pp. 4363-4373, 2009.
  22. H. Li, J. L. Yu, L. A. Yu, and J. Sun, "The clustering-based case-based reasoning for imbalanced business failure prediction: a hybrid approach through integrating unsupervised process with supervised process," International Journal of Systems Science, vol. 45, no. 5, pp. 1225-1241, 2014.
  23. C. F. Tsai, "Combining cluster analysis with classifier ensembles to predict financial distress," Information Fusion, vol. 16, pp. 46-58, 2014.
  24. Z. Xiao, K. Gong, and Y. Zou, "A combined forecasting approach based on fuzzy soft sets," Journal of Computational and Applied Mathematics, vol. 228, no. 1, pp. 326-333, 2009.
  25. Z. Xiao, X. L. Yang, Y. Pang, and X. Dang, "The prediction for listed companies financial distress by using multiple prediction methods with rough set and Dempster-Shafer evidence theory," Knowledge-Based Systems, vol. 26, pp. 196-206, 2012.
  26. H. Li and J. Sun, "Predicting business failure using an RSF-based case-based reasoning ensemble forecasting method," Journal of Forecasting, vol. 32, no. 2, pp. 180-192, 2013.
  27. D. Molodtsov, "Soft set theory: first results," Computers & Mathematics with Applications, vol. 37, no. 4-5, pp. 19-31, 1999.
  28. W. Xu, Z. Xiao, X. Dang, D. L. Yang, and X. L. Yang, "Financial ratio selection for business failure prediction using soft set theory," Knowledge-Based Systems, vol. 63, pp. 59-67, 2014.
  29. T. Fawcett, "An introduction to ROC analysis," Pattern Recognition Letters, vol. 27, no. 8, pp. 861-874, 2006.
  30. P. K. Maji, R. Biswas, and A. R. Roy, "Soft set theory," Computers & Mathematics with Applications, vol. 45, no. 4-5, pp. 555-562, 2003.
  31. D. G. Chen, E. C. C. Tsang, and D. S. Yeung, "Some notes on the parameterization reduction of soft sets," in Proceedings of International Conference on Machine Learning and Cybernetics, New Orleans, LA, 2003, pp. 1442- 1445.
  32. Z. Xiao, L. Chen, B. Zhong, and S. Ye, "Recognition for soft information based on the theory of soft sets," in Proceedings of International Conference on Services Systems and Services Management (ICSSSM'05), Chongging, China, 2005, pp. 1104-1106.
  33. D. V. Kovkov, V. M. Kolbanov, and D. A. Molodtsov, "Soft sets theory-based optimization," Journal of Computer and Systems Sciences International, vol. 46, no. 6, pp. 872-880, 2007.
  34. F. Feng, Y. M. Li, and N. Cagman, "Generalized uni-int decision making schemes based on choice value soft sets," European Journal of Operational Research, vol. 220, no. 1, pp. 162-170, 2012.
  35. A. I. Dimitras, S. H. Zanakis, and C. Zopounidis, "A survey of business failure with an emphasis on prediction method and industrial application," European Journal of Operational Research, vol. 90, no. 3, pp. 487-513, 1996.
  36. F. Lin, D. Liang, C. C. Yeh, and J. C. Huang, "Novel feature selection methods to financial distress prediction," Expert Systems with Applications, vol. 41, no. 5, pp. 2472-2483, 2014.
  37. E. Kim, W. Kim, and Y. Lee, "Combination of multiple classifiers for customer's purchase behavior prediction," Decision Support Systems, vol. 34, no. 2, pp. 167-175, 2002.
  38. J. H. Min and Y. C. Lee, "Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters," Expert Systems with Applications, vol. 28, no. 4, pp. 603-614, 2005.
  39. H. Li and J. Sun, "Forecasting business failure: the use of nearest-neighbour support vectors and correcting imbalanced samples: evidence from the Chinese hotel industry," Tourism Management, vol. 33, no. 3, pp. 622- 634, 2012.
  40. H. Li and J. Sun, "Forecasting business failure in china using case-based reasoning with hybrid case respresentation," Journal of Forecasting, vol. 29, no. 5, pp. 486-501, 2010.