Predicting business failure using forward ranking-order case-based reasoning

Expert Systems with Applications

Volumn 38, Issue 4, 2011, Pages 3075-3084

  Li, Hui ^a   Sun, Jie ^a  

^a ZHEJIANG NORMAL UNIVERSITY   (China)

Author keywords

Business failure prediction (BFP); Chinese listed companies; Forward ranking order case based reasoning (FRCBR); Wrapper

Indexed keywords

BUSINESS COMPUTING; BUSINESS FAILURE; BUSINESS FAILURE PREDICTION; CBR; CHINESE LISTED COMPANIES; COMPARATIVE METHODS; DISCRIMINATE ANALYSIS; EMPIRICAL DATA; EMPIRICAL RESULTS; EUCLIDEAN METRICS; FEATURE SELECTION; FEATURE SELECTION METHODS; FEATURE SUBSET; K-NEAREST NEIGHBORS; LOGISTIC REGRESSIONS; RANKING-ORDER CASE-BASED REASONING; RAPID DEVELOPMENT; STOCK EXCHANGE; WRAPPER; WRAPPER APPROACH;

FEATURE EXTRACTION; FORECASTING; INDUSTRY; MULTIVARIANT ANALYSIS; OPTIMIZATION; PLANT SHUTDOWNS; SUPPORT VECTOR MACHINES;

CASE BASED REASONING;

EID: 78650692575     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2010.08.098     Document Type: Article

References (61)

1. B.A. Ahn, S.S. Cho, and C.Y. Kim The integrated methodology of rough set theory and artificial neural network for business failure prediction Expert Systems with Applications 18 2000 65 74
2. E.I. Altman Financial ratios, discriminant analysis and the prediction of corporate bankruptcy Journal of Finance 23 4 1968 589 609
3. B. Back, T. Laitinen, and K. Sere Neural network and genetic algorithm for bankruptcy prediction Expert Systems with Applications 11 4 1996 407 413
4. W. Beaver Financial ratios as predictors of failure Journal of Accounting Research 4 1966 71 111
5. C.E. Brown, and U. Gupta Applying case-based reasoning to the accounting domain Intelligent Systems in Accounting, Finance and Management 3 1994 205 221
6. S.M. Bryant A case-based reasoning approach to bankruptcy prediction modeling Intelligent Systems in Accounting, Finance and Management 6 1997 195 214
7. Cardie, C. (1993). Using decision trees to improve case-based reasoning. In: Proceedings of the 10th international conference on machine learning, San Francisco, CA (pp. 25-32).
8. S. Cho, J. Kim, and J. Bae An integrative model with subject weight based on neural network learning for bankruptcy prediction Expert Systems with Applications 36 1 2009 403 410
9. A. Cielen, L. Peeters, and K. Vanhoof Bankruptcy prediction using a data envelopment analysis European Journal of Operational Research 154 2 2004 526 532
10. A.I. Dimitras, R. Slowinski, and R. Susmaga Business failure prediction using rough sets European Journal of Operational Research 114 2 1999 263 280
11. H. Frydman, E.I. Altman, and D. Kao Introducing recursive partitioning for financial classification: The case of financial distress Journal of Finance 40 1 1985 269 291
12. A. Gepp, K. Kumar, and S. Bhattacharya Business failure prediction using decision trees Journal of Forecasting 29 6 2010 536 555
13. J.-W. Han, and M. Kamber Data mining concepts and techniques 2001 Morgan Kaufmann Publishers Inc. San Mateo
14. W. Hardle, Y. Lee, and D. Schafer Variable selection and oversampling in the use of smooth support vector machines for predicting the default risk of companies Journal of Forecasting 28 6 2009 512 534
15. Y.-C. Hu, and J. Ansell Measuring retail company performance using credit scoring techniques European Journal of Operational Research 183 3 2007 1595 1606
16. Z.-S. Hua, Y. Wang, and X.-Y. Xu Predicting corporate financial distress based on integration of support vector machine and logistic regression Expert Systems with Applications 33 2007 434 440
17. Hui, X.-F.; Sun, J. (2006). An application of support vector machine to companies' financial distress prediction. In V. Torra et al. (Eds.), MDAI 2006, LNAI 3885 (pp. 274-282). Berlin, Heidelberg: Springer-Verlag.
18. J.P. Ignizio, and J.R. Soltyas Simultaneous design and training of ontogenic neural network classifier Computers and Operations Research 23 6 1996 535 546
19. H. Jo, and I. Han Integration of case-based forecasting, neural network, and discriminant analysis for bankruptcy prediction Expert Systems with Applications 11 4 1996 415 422
20. H. Jo, I. Han, and H. Lee Bankruptcy prediction using case-based reasoning neural network and discriminant analysis for bankruptcy prediction Expert Systems with Applications 13 2 1997 97 108
21. S. Jones, and D.A. Hensher Predicting firm financial distress: A mixed logit model Accounting Review 79 4 2004 1011 1038
22. S. Jones, and D.A. Hensher Modelling corporate failure: A multinomial nested logit analysis for unordered outcomes The British Accounting Review 39 2007 89 107
23. R. Kohavi, and G.H. John Wrappers for feature subset selection Artificial Intelligence 97 1-2 1997 273 324
24. J.L. Kolodner An introduction to case-based reasoning Artificial Intelligence Review 6 1 1992 3 34
25. P.R. Kumar, and V. Ravi Bankruptcy prediction in banks and firms via statistical and intelligent techniques - A review European Journal of Operational Research 180 2007 1 28
26. Langley, P. (1994). Selection of relevant features in machine learning. In: Proceeding of AAAI fall symposium relevance, NO (pp. 122-126).
27. F.-Y. Lin, and S. McClean A data mining approach to the prediction of corporate failure Knowledge-Based Systems 14 2001 189 195
28. R. Lin, Y. Wang, and C. Wu Developing a business failure prediction model via RST, GRA and CBR Expert Systems with Applications 36 2 2009 1593 1600
29. H. Li, and J. Sun Ranking-order case-based reasoning for financial distress prediction Knowledge-Based Systems 21 8 2008 868 878
30. H. Li, and J. Sun Majority voting combination of multiple case-based reasoning for financial distress prediction Expert Systems with Applications 36 3 2009 4363 4373
31. H. Li, and J. Sun Hybridizing principles of the Electre method with case-based reasoning for data mining Electre-CBR-I and Electre-CBR-II European Journal Operational Research 197 1 2009 214 224
32. H. Li, and J. Sun Forecasting business failure in China using case-based reasoning with hybrid case representation Journal of Forecasting 29 5 2010 486 501
33. H. Li, J. Sun, and B. Sun Financial distress prediction based on OR-CBR in the principle of k-nearest neighbor Expert Systems with Applications 36 1 2009 643 659
34. D. Martin Early warning of bank failure: A logit regression approach Journal of Banking and Finance 1 1977 249 276
35. T.E. McKee, and T. Lensberg Genetic programming and rough sets: A hybrid approach to bankruptcy classification European Journal of Operational Research 138 2 2002 436 451
36. A.J. Miller Subset selection in regression 1990 Chapman and Hall
37. J.-H. Min, and Y.-C. Lee Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters Expert Systems with Applications 28 2005 603 614
38. S.-H. Min, J.-M. Lee, and I. Han Hybrid genetic algorithms and support vector machines for bankruptcy prediction Expert Systems with Applications 31 2006 652 660
39. L. Nanni, and A. Lumini An experimental comparison of ensemble of classifiers for bankruptcy prediction and scoring Expert Systems with Applications 36 2 2009 3028 3033
40. Odom, M.; Sharda, R. (1990). A neural networks model for bankruptcy prediction. In: Proceedings of international joint conference on neural networks, San Diego, CA (pp. 163-168).
41. J.A. Ohlson Financial rations and the probabilistic prediction of bankruptcy Journal of Accounting Research 18 1980 109 131
42. S.K. Pal, and S.C.K. Shiu Foundations of soft case-based reasoning 2004 Wiley & Sons New Jersey
43. C.-S. Park, and I. Han A case-based reasoning with the feature weights derived by analytic hierarchy process for bankruptcy prediction Expert Systems with Applications 23 3 2002 255 264
44. V. Ravi, H. Kurniawan, and P. Thai Soft computing system for bank performance prediction Applied Soft Computing 8 2008 305 315 (Pubitemid 47374565)
45. R. Schank Dynamic memory: A theory of learning in computers and people 1982 Cambridge University Press New York
46. C. Serrano-Cinca Self organizing neural networks for financial diagnosis Decision Support Systems 17 1996 227 238
47. K.-S. Shin, T.-S. Lee, and H.-J. Kim An application of support vector machines in bankruptcy prediction model Expert Systems with Applications 28 2005 127 135
48. B. Smyth, and M. Keane Adaptation-guided retrieval: Questioning the similarity assumption in reasoning Artificial Intelligence 102 1998 249 293
49. Sun, J.; Hui, X.-F. (2006). Financial distress prediction based on similarity weighted voting CBR. In X. Li et al. (Eds.), ADMA 2006, LNAI 4093 (pp. 947-958). Berlin, Heidelberg: Springer-Verlag.
50. J. Sun, and H. Li Data mining method for listed companies' financial distress prediction Knowledge-Based Systems 21 1 2008 1 5
51. J. Sun, and H. Li Listed companies' financial distress prediction based on weighted majority voting combination of multiple classifiers Expert Systems with Applications 35 3 2008 818 827
52. L. Sun, and P.P. Shenoy Using Bayesian networks for bankruptcy prediction: Some methodological issues European Journal of Operational Research 180 2 2007 738 753
53. F. Tay, and L. Shen Economic and financial prediction using rough sets model European Journal of Operational Research 141 3 2002 641 659
54. C. Tsai, and J. Wu Using neural network ensembles for bankruptcy prediction and credit scoring Expert Systems with Applications 34 2008 2639 2649
55. F. Tseng, and Y. Hu Comparing four bankruptcy prediction models: Logit, quadratic interval logit, neural and fuzzy networks Expert Systems with Applications 37 3 2010 1846 1853
56. R.L. Wilson, and R. Sharda Bankruptcy prediction using neural networks Decision Support Systems 11 1994 545 557
57. C.-H. Wu, G.-H. Tzeng, and Y.-J. Goo A real-valued genetic algorithm to optimize the parameters of support vector machine for predicting bankruptcy Expert Systems with Applications 32 2 2007 397 408
58. C. Yeh, D. Chi, and M. Hsu A hybrid approach of DEA, rough set and support vectors for business failure prediction Expert Systems with Applications 37 2 2010 1535 1541
59. Yip, A. (2004). Predicting business failure with a case-based reasoning approach. In M. Negoita et al. (Eds.), KES 2004, LNAI 3215 (pp. 665-671). Berlin, Heidelberg: Springer-Verlag.
60. Yip, A.; Deng, H. (2003). A case-based reasoning approach to business failure prediction. In V. Palade et al. (Eds.), KES 2003, LNAI 2773 (pp. 1075-1080). Berlin, Heidelberg: Springer-Verlag.
61. M. Zmijewski Methodological issues related to the estimation of financial distress prediction models Journal of Accounting Research 22 1984 59 82