The performance of corporate financial distress prediction models with features selection guided by domain knowledge and data mining approaches

Knowledge-Based Systems

Volumn 85, Issue , 2015, Pages 52-61

  Zhou, Ligang ^a   Lu, Dong ^b   Fujita, Hamido ^c  

^a MACAU UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Macao)

^b SICHUAN NORMAL UNIVERSITY   (China)

^c IWATE PREFECTURAL UNIVERSITY   (Japan)

Author keywords

Data mining; Domain knowledge; Features selection; Financial distress prediction

Indexed keywords

FEATURE EXTRACTION; FINANCE; FORECASTING; GENETIC ALGORITHMS; PREDICTIVE ANALYTICS;

CLASSIFICATION PERFORMANCE; DOMAIN KNOWLEDGE; FEATURE SUBSET; FEATURES SELECTION; FINANCIAL DISTRESS PREDICTION; FINANCIAL DISTRESS PREDICTION MODEL; MINING TECHNIQUES;

DATA MINING;

EID: 84937523591     PISSN: 09507051     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.knosys.2015.04.017     Document Type: Article

References (33)

1. E.I. Altman Financial ratios, discriminant analysis and the prediction of corporate bankruptcy J. Finance 23 1968 589 609
2. C. Charalambous, A. Charitou, and F. Kaourou Comparative analysis of artificial neural network models: application in bankruptcy prediction Ann. Oper. Res. 99 2000 403 425
3. R. Wilson, and R. Sharda Bankruptcy prediction using neural networks Decis. Support Syst. 11 1994 545 557
4. A. Gepp, K. Kumar, and S. Bhattacharya Business failure prediction using decision trees J. Forecast. 29 2009 536 555
5. K.S. Shin, T.S. Lee, and H.J. Kim An application of support vector machines in bankruptcy prediction model Expert Syst. Appl. 28 2005 127 135
6. Y.C. Ko, H. Fujita, and G.H. Tzeng An extended fuzzy measure on competitiveness correlation based on WCY 2011 Knowl.-Based Syst. 37 2013 86 93
7. H.L. Chen, B. Yang, G. Wang, J. Liu, X. Xu, S.J. Wang, and D.Y. Liu A novel bankruptcy prediction model based on an adaptive fuzzy k-nearest neighbor method Knowl.-Based Syst. 24 2011 1348 1359
8. M. Divsalar, M.R. Javid, A.H. Gandomi, J.B. Soofi, and M.V. Mahmood Hybrid genetic programming-based search algorithms for enterprise bankruptcy prediction Appl. Artif. Intell. 25 2011 669 692
9. A. Verikas, Z. Kalsyte, M. Bacauskiene, and A. Gelzinis Hybrid and ensemble-based soft computing techniques in bankruptcy prediction: a survey, soft computing - a fusion of foundations Methodol. Appl. 14 2010 995 1010
10. S. Cho, H. Hong, and B.-C. Ha A hybrid approach based on the combination of variable selection using decision trees and case-based reasoning using the Mahalanobis distance: for bankruptcy prediction Expert Syst. Appl. 37 2010 3482 3488
11. F.E. Mays, and N. Lynas Credit Scoring for Risk Managers: The Handbook for Lenders 2004 Thomson South-Western
12. E. Altman, M. Heine, L. Zhang, J. Yen, Corporate Financial Distress Diagnosis in China, New York University Salomon Center, Working Paper, 2007.
13. T. Shumway Forecasting bankruptcy more accurately: a simple hazard model J. Bus. 74 2001 101 124
14. M. Zmijewski Methodological issues related to the estimation of financial distress prediction models J. Account. Res. 22 1984 59 82
15. K.P. Ravi, and V. Ravi Bankruptcy prediction in banks and firms via statistical and intelligent techniques - a review Eur. J. Oper. Res. 180 2007 1 28
16. C.F. Tsai Feature selection in bankruptcy prediction Knowl.-Based Syst. 22 2009 120 127
17. P. du Jardin Predicting bankruptcy using neural networks and other classification methods: the influence of variable selection techniques on model accuracy Neurocomputing 73 2010 2047 2060
18. Z. Drezner, G.A. Marcoulides, and M.H. Stohs Financial applications of a tabu search variable selection model J. Appl. Math. Decis. Sci. 5 2001 215 234
19. L. Zhou, K.K. Lai, and J. Yen Empirical models based on features ranking techniques for corporate financial distress prediction Comput. Math. Appl. 64 2012 2484 2496
20. C. Walsh Key Management Ratios: Master the Management Metrics that Drive and Control Your Business 2003 Financial Times Management London
21. S.A. Ross, R. Westerfield, J.F. Jaffe, and G.S. Roberts Corporate Finance vol. 7 2002 McGraw-Hill/Irwin Boston
22. I. Guyon, and A. Elisseeff An introduction to variable and feature selection J. Mach. Learn. Res. 3 2003 1157 1182
23. M. Hall, and L. Smith Practical feature subset selection for machine learning Proceedings of the 21st Australian Computer Science Conference 1998 Springer 181 191
24. I. Guyon, S. Gunn, M. Nikravesh, and L.A. Zadeh Feature Extraction: Foundations and Applications vol. 207 2006 Springer
25. S. Theodoridis, and K. Koutroumbas Pattern Recognition second ed. 2003 Academic Press San Diego, USA
26. D.A. Lind, W.G. Marchal, and S.A. Wathen Statistical Techniques in Business & Economics 2012 Mcgraw-Hill
27. H. Zou, and T. Hastie Regularization and variable selection via the elastic net J. Roy. Stat. Soc. Ser. B 67 2005 301 320
28. MathWorks, MATLAB: Statistics Toolbox; User's Guide, MathWorks, 2012.
29. GTA, China Stock Market Financial Database - Financial Indices, Technical Report, 2015.
30. L. Zhou Performance of corporate bankruptcy prediction models on imbalanced dataset: the effect of sampling methods Knowl.-Based Syst. 41 2013 16 25
31. T. Fawcett, Roc Graphs: Notes and Practical Considerations for Data Mining Researchers, Report, January 2003.
32. M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, and I.H. Witten The WEKA data mining software: an update SIGKDD Explor. 11 2009 10 18
33. J. Demšar Statistical comparisons of classifiers over multiple data set J. Mach. Learn. Res. 2006 1 30