Business health characterization: A hybrid regression and support vector machine analysis

Expert Systems with Applications

Volumn 49, Issue , 2016, Pages 48-59

  Pal, Rudrajeet ^a   Kupka, Karel ^b   Aneja, Arun P ^c   Militky, Jiri ^d  

^a UNIVERSITY OF BORÅS   (Sweden)

^b TriloByte Statistical Software Ltd   (Czech Republic)

^c East Carolina State University   (United States)

^d TECHNICAL UNIVERSITY OF LIBEREC   (Czech Republic)

Author keywords

Bankruptcy prediction; Business health; Credit rating; Predictive classification model; Support vector machine; Variable selection

Indexed keywords

EXPERT SYSTEMS; FORECASTING; HEALTH; PLANT SHUTDOWNS; REGRESSION ANALYSIS;

BANKRUPTCY PREDICTION; BUSINESS PRACTITIONERS; CLASSIFICATION MODELS; CREDIT RATINGS; HYBRID METHODOLOGIES; STAGEWISE REGRESSIONS; VARIABLE SELECTION; VOLATILE ENVIRONMENTS;

SUPPORT VECTOR MACHINES;

EID: 84953455524     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2015.11.027     Document Type: Article

References (52)

1. Ahn B.S., Cho S.S., Kim C.Y. The integrated methodology of rough set theory and artificial neural network for business failure prediction Expert Systems with Applications 18 2 2000 65 74
2. Altman E., Avery R., Eisenbeis R., Sinkey J. Application of classification techniques in business, banking and finance Contemporary studies in economic and financial analysis 3 1981 JAI Press Greenwich, CT
3. Altman E.I. Financial ratios, discriminant analysis and the prediction of corporate bankruptcy The Journal of Finance 23 4 1968 589 609
4. Altman E.I. Predicting financial distress of companies: revisiting the z-score and zeta® models 2000 Stern Business School, New York University New York
5. Ambroise C., McLachlan G.J. Selection Bias in gene extraction on the basis of microarray gene-expression data Proceedings of the National Academy of Sciences of the United States of America 99 10 2002 6562 6566
6. Aziz M.A., Dar H.A. Predicting corporate bankruptcy: where we stand? Corporate Governance 6 1 2006 18 33
7. Balcaen S., Ooghe H. 35 years of studies on business failure: an overview of the classic statistical methodologies and their related problems British Accounting Review 38 1 2006 63 93
8. Beaver W. Financial ratios as predictors of failures, empirical research in accounting: selected studies Supplement to Journal of Accounting Research 4 3 1966 71 111
9. Bühlmann P. Boosting for high-dimensional linear models Annals of Statistics 34 2 2006 559 583
10. Carey M., Hrycay M. Parameterizing credit risk models with rating data Journal of Banking & Finance 25 2001 197 270
11. Charitou A., Neophytou E., Charalambous C. Predicting corporate failure: empirical evidence for the UK European Accounting Review 13 3 2004 465 497
12. Cielen A., Peeters L., Vanhoof K. Bankruptcy prediction using a data envelopment analysis European Journal of Operational Research 154 2 2004 526 532
13. Dimitras A.I., Zanakis S.H., Zopounidis C. A survey of business failures with an emphasis on prediction methods and industrial applications European Journal of Operational Research 90 3 1996 487 513
14. Ding Y.-S., Song X.-P., Zen Y.-M. Forecasting financial condition of Chinese listed companies based on support vector machine Expert Systems with Applications 34 4 2008 3081 3089
15. Efron B., Hastie T., Johnstone I., Tibshirani R. Least angle regression The Annals of Statistics 32 2 2004 407 451
16. Fisher R.A. The use of multiple measurements in taxonomic problems Annals of Eugenics 7 2 1936 179 188
17. Hastie T., Taylor J., Tibshirani R., Walther G. Forward stagewise regression and the monotone lasso Electronic Journal of Statistics 1 2007 1 29
18. Hastie T., Tibshirani R., Friedman F. The elements of statistical learning: data mining, inference, and prediction 2 ed. 2013 Springer
19. Hua Z., Wang Y., Xu X., Zhang B., Liang L. Predicting corporate financial distress based on integration of support vector machine and logistic regression Expert Systems with Applications 33 2 2007 434 440
20. Huang S.H., Tsai C.F., Yen D.C., Cheng Y.L. A hybrid financial analysis model for business failure prediction Expert Systems with Applications 35 3 2008 1034 1040
21. Hui X.F., Sun J. An application of support vector machine to companies' financial distress prediction V. Torra Y. Narukawa A. Valls Modeling decisions for artificial intelligence 2006 Springer-Verlag Berlin 274 282
22. Jankowitsch R., Pichler S., Schwaiger W. Modelling the economic value of credit rating systems Journal of Banking and Finance 31 2007 181 198
23. Kamath R., He Y. Business failure prediction in the retail industry: an empirical evaluation of generic prediction models Academy of Accounting Studies Journal 10 2 2006 97 110
24. Keasey K., Watson R. Financial distress prediction models: a review of their usefulness British Journal of Management 2 2 1991 89 102
25. Kumar P.R., Ravi V. Bankruptcy prediction in banks and firms via statistical and intelligent techniques - A review European Journal of Operational Research 180 1 2007 1 28
26. Lee S., Choi W.S. A multi-industry bankruptcy prediction model using back-propagation neural network and multivariate discriminant analysis Expert Systems with Applications 40 2 2013 2941 2946
27. Li H., Sun J. Predicting business failure using multiple case-based reasoning combined with support vector machine Expert Systems with Applications 36 2009 10085 10096
28. Li H., Sun J. Predicting business failure using support vector machines with straightforward wrapper: a re-sampling study Expert Systems with Applications 38 10 2011 12747 12756
29. Lin F., Yeh C.-C., Lee M.-Y. The use of hybrid manifold learning and support vector machines in the prediction of business failure Knowledge-Based Systems 24 2011 95 101
30. Michel R., Schnakenburg I., von Martens T. Methods of variable pre-selection for net score modeling Journal of Research in Interactive Marketing 7 4 2013 257 268
31. Min J.H., Lee Y.C. Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters Expert Systems with Applications 28 2005 603 614
32. Min S.-H., Lee J., Han I. Hybrid genetic algorithms and support vector machines for bankruptcy prediction Expert Systems with Applications 31 3 2006 652 660
33. Oelerich A., Poddig T. Evaluation of rating systems Expert Systems with Applications 30 2006 437 447
34. Pearce J.A. II, Robbins K. Toward improved theory and research on business turnaround Journal of Management 19 3 1993 613 636
35. Platt H.D., Platt M.B. Predicting corporate financial distress: reflections on choice-based sample bias Journal of Economics and Finance 26 2 2002 184 199
36. Platt H.D., Platt M.B. Industry-relative ratios revisited: the case of financial distress FMA 2004 Meeting, New Orleans (USA) 2004 6 9
37. Platt J. Probabilistic outputs for support vector machines and comparison to regularized likelihood methods Advances in Large Margin Classifiers 2000 MIT Press Cambridge, MA
38. Scholkopf B., Burges C., Vapnik V. Extracting support data for a given task International conference on knowledge discovery & data mining 1995 252
39. Scholkopf B., Sung K., Burges J.C., Girosi F., Nioyogi P., Poggio T., Vapnik V. Comparing support vector machines with Gaussian kernels to radial basis function classifiers IEEE Transactions on Signal Processing 45 11 1997 2758 2765
40. Shin K.S., Lee T.S., Kim H.J. An application of support vector machines in bankruptcy prediction model Expert Systems with Applications 28 1 2005 127 135
41. Shuai J.J., Li H.L. Using rough set and worst practice DEA in business failure prediction Lecture Notes in Computer Science 3642 2 2005 503 510
42. Song J.H., Adams C.R., Rhee Y. Developing an effective deep support network for individual investors Journal of Financial Services Marketing 12 3 2007 208 218
43. Standard, & Poor's (2014). Guide to credit rating essentials: what are credit ratings and how do they work? http://jpn.up.pt/pdf//rating.pdf (Accessed January 2015).
44. Stickel S.E. The anatomy of the performance of buy and sell recommendations Financial Analysts Journal 51 5 1995 25 39
45. Sun J., Li H. Financial distress prediction using support vector machines: ensemble vs. individual Applied Soft Computing 12 2012 2254 2265
46. Sun J., Li H., Huang Q.-H., He K.-Y. Predicting financial distress and corporate failure: a review from the state-of-the-art definitions, modeling, sampling, and featuring approaches Knowledge-Based Systems 57 2014 41 56
47. Sundström G., Hollnagel E. Learning how to create resilience in business systems E. Hollnagel D.D. Woods N. Leveson Resilience engineering: concepts and precepts 2006 Ashgate Aldershot, UK
48. Tibshirani R. Regression shrinkage and selection via the lasso Journal of the Royal Statistical Society 58 1 1996 267 288
49. Tsai C.F. Feature selection in bankrutcy prediction Knowledge-Based Systems 22 2 2009 120 127
50. Vapnik V. Statistical learning theory 1998 Wiley New York, NY
51. Wu C.-H., Tzeng G.-H., Goo Y.-J., Fang W.-C. A real-valued genetic algorithm to optimize the parameters of support vector machine for predicting bankruptcy Expert Systems with Applications 32 2007 397 408
52. Wu W.-W. Beyond business failure prediction Expert Systems with Applications 37 2010 2371 2376