Going-concern prediction using hybrid random forests and rough set approach

Information Sciences

Volumn 254, Issue , 2014, Pages 98-110

  Yeh, Ching Chiang ^a   Chi, Der Jang ^b   Lin, Yi Rong ^a  

^a NATIONAL TAIPEI UNIVERSITY OF BUSINESS   (Taiwan)

^b CHINESE CULTURE UNIVERSITY   (Taiwan)

Author keywords

Going concern prediction; Intellectual capital; Random forest; Rough set theory

Indexed keywords

DECISION TREES; FORECASTING; INFORMATION MANAGEMENT; KNOWLEDGE MANAGEMENT; SET THEORY;

CLASSIFICATION RATES; EXPLANATORY POWER; GOING CONCERN; HYBRID RANDOM FORESTS; INTELLECTUAL CAPITAL; PREDICTIVE VARIABLES; RANDOM FORESTS; ROUGH SET THEORY (RST);

ROUGH SET THEORY;

EID: 84885187385     PISSN: 00200255     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ins.2013.07.011     Document Type: Article

References (67)

1. E.H. Aboul, E.A. Mohamed, S.O. Hala, Rough sets data analysis in knowledge discovery: a case of Kuwaiti diabetic children patients, Advance in Fuzzy Systems 8 (2008) 1-13.
2. M. Anandarajan, A. Anandarajan, A comparison of machine learning techniques with a qualitative response model for auditors' going concern reporting, Expert Systems with Applications 16 (4) (1999) 385-392. (Pubitemid 129667290)
3. P. Barnes, H. Huan, The auditor's going concern decision: some UK evidence concerning independence and competence, Journal of Business Finance & Accounting 20 (2) (1993) 213-228.
4. B.K. Behn, S.E. Kaplan, K.P. Krumwiede, Further evidence on the auditor's going-concern report: the influence of management plans, Auditing: A Journal of Practice & Theory 20 (1) (2001) 13-29.
5. T. Bell, R. Tabor, Empirical analysis of audit uncertainty qualifications, Journal of Accounting Research 29 (2) (1991) 350-370.
6. J.L. Bellovary, D.E. Giacomino, M.D. Akers, A review of going concern prediction studies: 1976 to present, Journal of Business & Economic Research 5 (2007) (1976) 9-28.
7. M. Beynon, M. Peel, Variable precision rough set theory and data discrimination: an application to corporate failure prediction, Omega 29 (2001) 561-576. (Pubitemid 33628771)
8. G. Biau, L. Devroye, G. Lugosi, Consistency of random forests and other averaging classifiers, Journal of Machine Learning Research 9 (2008) 2015-2033.
9. N. Bontis, Intellectual capital: an exploratory study that develops measures and models, Management Decision 36 (2) (1998) 63-76. (Pubitemid 128046195)
10. L. Breiman, Random forests, Machine Learning 45 (2001) 5-32. (Pubitemid 32933532)
11. Y. Cao, G. Wan, F. Wang, Predicting financial distress of Chinese listed companies using rough set theory and support vector machine, Asia-Pacific Journal of Operational Research 28 (1) (2011) 95-109.
12. J.V. Carcello, T.L. Neal, Audit committee composition and auditor reporting, Accounting Review 75 (4) (2000) 453-467.
13. J.R. Casterella, B.L. Lewis, P.L. Walker, Modeling the audit opinions issued to bankrupt companies: a two-stage empirical, Journal of Business Finance & Accounting 32 (2000) 204-229.
14. C.C. Chang, C.J. Lin, LIBSVM: a library for support vector machines, 2001. http://www.csie.ntu.edu.tw/cjlin/libsvm (accessed 29.04.13).
15. K.C.W. Chen, B.K. Church, Default on debt obligations and the issuance of going-concern opinions, Auditing: A Journal of Practice & Theory 11 (2) (1992) 30-50.
16. D. Cornier, M. Magnan, B. Morard, The auditor's consideration of the going concern assumption: a diagnostic model, Journal of Accounting, Auditing & Finance 10 (2) (1995) 201-221.
17. M. Daubie, P. Levecq, N. Meskens, A comparison of rough sets and recursive partitioning induction approaches: an application to commercial loans, International Transactions in Operational Research 9 (2002) 681-694.
18. E.B. Deakin, A discriminant analysis of predictors of business failure, Journal of Accounting Research 10 (1972) 167-179.
19. P. De Saa-Perez, J.M. Garcia-Falcon, A resource-based view of human resource management and organizational capability development, International Journal of Human Resource Management 13 (1) (2002) 123-140.
20. R. Diaz-Uriarte, S. Alvarez de Andres, Gene selection and classification of microarray data using random forest, BMC Bioinformatics 7 (2006) 3.
21. A. Dimitras, R. Slowinski, R. Susmaga, C. Zopounidis, Business failure using rough sets, European Journal Operational Research 114 (1999) 263-280.
22. N. Dopuch, R. Holthausen, R. Leftwich, Predicting audit qualifications with financial and market variables, Accounting Review 63 (3) (1987) 431-453.
23. B. Efron, R. Tibshirani, An Introduction to the Bootstrap, Chapman & Hall/CRC, New York, 1993.
24. H.L. Etheridge, R.S. Sriram, H.Y.K. Hsu, A comparison of selected artificial neural networks that help auditors evaluate client financial viability, Decision Sciences 31 (2) (2000) 531-550.
25. B. Foster, T. Ward, J. Woodroof, An analysis of the usefulness of debt defaults and going concern opinions in bankruptcy risk assessment, Journal of Accounting, Auditing & Finance 13 (3) (1998) 351-371.
26. C. Gaganis, F. Pasiouras, M. Doumpos, Probabilistic neural networks for the identification of qualified audit opinions, Expert Systems with Applications 32 (2007) 114-124. (Pubitemid 44311469)
27. J.W. Grzymala-Busse, A new version of the rule induction system LERS, Fundamental Informaticae 31 (1997) 27-39. (Pubitemid 126541542)
28. J.W. Grzymala-Busse, R. Slowinski, LERS -a system for learning from examples based on rough sets, in: R. Slowinski (Ed.), Intelligent Decision Support: Handbook of Applications and Advances in Rough Set Theory, Kluwer Academic Publishers, London, 1992.
29. J.V. Hansen, J.B. McDonald, J.D. Stice, Artificial intelligence and generalized qualitative-response models: an empirical test on two audit decisionmaking domains, Decision Science 23 (3) (1992) 708-723.
30. W. Hopwood, J.C. Mckeown, J.F. Mutchler, A reexamination of auditor versus model accuracy within the context of the going-concern opinion decision, Contemporary Accounting Research 10 (1994) 409-431.
31. R.A. Johnson, D.W. Wichern, Applied Multivariate Statistical Analysis, Prentice-Hall, New York, 1998.
32. T. Kida, An investigation into auditors' continuity and related qualification judgments, Journal of Accounting Research 18 (2) (1980) 506-523.
33. E. Kirkos, C. Spathis, A. Nanopoulos, Y. Manolopoulos, Identifying qualified auditors' opinions: a data mining approach, Journal of Emerging Technologies in Accounting 4 (1) (2007) 183-197.
34. H.C. Koh, R. Brown, Probit prediction of going and non-going concerns, Managerial Auditing Journal 6 (3) (1991) 18-23.
35. H.C. Koh, C.K. Low, Going concern prediction using data mining techniques, Managerial Auditing Journal 19 (3) (2004) 462-476.
36. E. Krusinska, R. Slowinski, J. Stefanowski, Discriminant versus rough set approach to vague data analysis, Applied Stochastic Models and Data Analysis 8 (1) (1992) 43-56.
37. M.J. Lenard, P. Alam, G.R. Madey, The application of neural networks and a qualitative response model to the auditor's going concern uncertainty decision, Decision Sciences 26 (2) (1995) 209-227.
38. H. Li, J. Sun, Predicting business failure using multiple case-based reasoning combined with support vector machine, Expert Systems with Applications 36 (2009) 10085-10096.
39. A. Liaw, M. Wiener, Classification and regression by random forest, R News 2-3 (2002) 18-22.
40. K.L. Lunetta, L.B. Hayward, J. Segal, P.V. Eerdewegh, Screening large-scale association study data: exploiting interactions using random forests, BMC Genetics 5 (2004) 32.
41. D. Martens, L. Bruynseels, B. Baesens, M. Willekens, J. Vanthienen, Predicting going concern opinion with data mining, Decision Support Systems 45 (4) (2008) 765-777.
42. T. McKee, Discriminant prediction of going concern status: a model for auditors, in: Proc. 6th American Accounting Association Annual Meeting, 1996, pp. 294-321.
43. J. McKeown, J. Mutchler, W. Hopwood, Towards an explanation of auditor failure to modify the audit opinions of bankrupt companies, Auditing: A Journal of Practice & Theory 10 (1991) 1-13.
44. R.E. Morris, R.S. Jerry, An examination of the effect of CPA firm type on bank regulators' closure decisions, Auditing: A Journal of Practice & Theory 18 (2) (1999) 143-158.
45. J.F. Mutchler, Auditor's perceptions of the going-concern opinion decision, Auditing: A Journal of Practice & Theory 3 (1984) 17-29.
46. J.F. Mutchler, W. Hopwood, J. McKeown, The influence of contrary information and mitigating factors on audit opinion decisions on bankrupt companies, Journal of Accounting Research 35 (2) (1997) 295-310.
47. S.K. Pal, P. Mitra, Case generation using rough sets with fuzzy representation, IEEE Transactions on Knowledge and Data Engineering 16 (2004) 293-300.
48. Z. Pawlak, Rough sets, International Journal of Information Computation Science 11 (5) (1982) 341-356.
49. Z. Pawlak, Rough Sets: Theoretical Aspects of Reasoning about Data, Springer-Verlag, New York, 1991.
50. Z. Pawlak, A. Skowron, Rudiments of rough sets, Information Sciences 177 (2007) 3-27. (Pubitemid 44556698)
51. Z. Pawlak, A. Skowron, Rough sets: some extensions, Information Sciences 177 (2007) 28-40. (Pubitemid 44556699)
52. J.R. Quinlan, C4.5: Programs for Machine Learning, Morgan Kaufmann, San Mateo, CA, 1993.
53. T.A. Stewart, Intellectual Capital: The New Wealth of Organizations, Bantam Doubleday Dell Publishing Group, New York, 1997.
54. A. Sun, E.-P. Lim, Y. Liu, On strategies for imbalanced text classification using SVM: a comparative study, Decision Support Systems 48 (2009). pp. 191-20.
55. C.-F. Tsai, Feature selection in bankruptcy prediction, Knowledge-Based Systems 22 (2009) 120-127.
56. A. Vellido, P.J.G. Lisboa, J. Vaughan, Neural networks in business: a survey of applications (1992-1998), Expert Systems with Applications 17 (1999) 51-70. (Pubitemid 129664853)
57. P. Verwijmeren, J.M.M. Derwall, Employee well-being, firm leverage, and bankruptcy risk, Journal of Banking & Finance 34 (5) (2010) 956-964.
58. M.C. Wiener, L. Obando, J. O'Neill, Building Process Understanding for Vaccine Manufacturing Using Data Mining, Quality Engineering 22 (3) (2010) 157-168.
59. Y. Wu, C. Gaunt, S. Gray, A comparison of alternative bankruptcy prediction models, Journal of Contemporary Accounting & Economics 6 (2010) 34-45.
60. W.Z. Wu, W.X. Zhang, H.Z. Li, Knowledge acquisition in incomplete fuzzy information systems via the rough set approach, Expert Systems 20 (2003) 280-286.
61. Z. Xiao, X. Yang, Y. Pang, 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 26 (2012) 196-206.
62. Y.Y. Yao, Semantics of fuzzy sets in rough set theory, Transactions on Rough Sets 2 (2004) 310-331.
63. C.-C. Yeh, D.-J. Chi, M.-F. Hsu, A hybrid approach of DEA, rough set and support vector machines for business failure prediction, Expert Systems with Applications 37 (2) (2010) 1535-1541.
64. C.-C. Yeh, F. Lin, C.-Y. Hsu, A hybrid KMV model, random forests and rough set theory approach for credit rating, Knowledge-Based Systems 33 (2012) 166-172.
65. M.A. Youndt, M. Subramaniam, S.A. Snell, Intellectual capital profiles: an examination of investments and returns, Journal of Management Studies 41 (2) (2004) 335-361. (Pubitemid 38284442)
66. J. Zhang, M. Zulkernine, Network intrusion detection using random forests, Proceedings of the Third Annual Conference on Privacy, Security and Trust, St. Andrews, New Brunswick, Canada, 2005. October.
67. J. Zhang, M. Zulkernine, A hybrid network intrusion detection technique using random forests, in: The First International Conference on Availability, Reliability and Security, 2006 (ARES''06), IEEE, Austria, 2006.