Financial early warning system model and data mining application for risk detection

Expert Systems with Applications

Volumn 39, Issue 6, 2012, Pages 6238-6253

  Koyuncugil, Ali Serhan ^a   Ozgulbas, Nermin ^b  

^a Çankaya   (Turkey)

^b BASKENT UNIVERSITY   (Turkey)

Author keywords

CHAID; Data mining; Early warning systems; Financial distress; Financial risk; SMEs

Indexed keywords

CHAID; EARLY WARNING SYSTEM; FINANCIAL DISTRESS; FINANCIAL RISKS; SMES;

ALARM SYSTEMS; DATA MINING; INDUSTRY;

FINANCE;

EID: 84856512918     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2011.12.021     Document Type: Article

References (65)

1. N.I. Abumustafa Development of an early warning model for currency crises in emerging economies: An empirical study among Middle Eastern countries International Journal of Management 23 3 2006 403
2. E. Altman Financial ratios, discriminant analysis and the prediction of corporate bankruptcy Journal of Finance September 1968 589 609
3. E.I. Altman, G. Haldeman, and P. Narayanan Zeta analysis: A new model to identify bankruptcy risk of corporations Journal of Banking and Finance, June 1977 29 54
4. J.M. Ambrose, and J.A. Seward Best's ratings financial ratios and prior probabilities in insolvency prediction Journal of Risk and Insurance 55 1998 229 244
5. R. Barniv, and J. Hathorn The merger or insolvency alternative in the insurance industry Journal of Risk and Insurance 64 1 1997 89 113 (Pubitemid 127459014)
6. R. Barniv, and J.B. McDonald Identifying financial distress in the insurance industry: A synthesis of methodological and empirical issues Journal of Risk and Insurance 59 1992 543 573
7. W. Beaver Financial ratios as predictors of failure Journal of Accounting Research 4 1966 71 111
8. Berg, A., Borensztein, E., & Pattillo, C. (2004). Assessing early warning systems: How have they worked in practice? IMF Working Paper, March 2004. Retrieved April, 2009, from .
9. Brockett, P. L., & Cooper, W. W. (1990). Report to the State Auditor and the State Board of Insurance on Early Warning Systems to Monitor the Performance of Insurance Companies in Texas. Office of the State Auditor, Austin, TX.
10. P.L. Brockett, L.L. Golden, J. Jang, and C. Yang A comparison of neural network, statistical methods and variable Journal of Risk and Insurance 73 3 2006 397 419
11. S. Chang, H. Chang, C. Lin, and S. Kao The effect of organizational attributes on the adoption of data mining techniques in the financial service industry: An empirical study in Taiwan International Journal of Management 20 1 2003 497 503 (Pubitemid 38835489)
12. Commission Recommendation of 6 May 2003 Concerning the Definition of Micro, Small and Medium-sized Enterprises (2003/361/EC), L 124/36 2003. Official Journal of the European Union,Retrieved August 2010, from .
13. E.P. Davis, and D. Karim Comparing early warning systems for banking crises Journal of Financial Stability 4 2 2008 89 120
14. E.P. Davis, and D. Karim Could early warning systems have helped to predict the sub-prime crisis? National Institute Economic Review 206 1 2008 35 47
15. E.B. Deakin A discriminant analysis of predictors of business failure Journal of Accounting Research 10 1 1972 167 179
16. B.L. Derby Data mining for improper payments The Journal of Government Financial Management 52 1 2003 10 13
17. H.J. Edison Do indicators of financial crises work? An evaluation of an early earning system International Journal of Finance and Economics 8 1 2003 11 53 (Pubitemid 36199424)
18. T. Eklund, B. Back, H. Vanharanta, and A. Visa Using the self-organizing map as a visualization tool in financial benchmarking Information Visualization 2 3 2003 171 181
19. A. El-Shazly Early warning of currency crises: An econometric analysis for Egypt The Middle East Business and Economic Review 18 1 2003 34 48
20. G. Fayyad, P. Piatetsky-Shapiro, and P. Symth From data mining to knowledge discovery in databases AI Magazine 17 3 1996 37 54
21. W. Frawley, G. Piatetsky-Shapiro, and C. Matheus Knowledge discovery in databases: An overview AI Magazine: Fall 1992 213 228
22. D. Hand, H. Mannila, and P. Smyth Principles of data mining 2001 MIT Press Cambridge
23. S. Hoppszallern Healthcare benchmarking Hospitals & Health Networks 77 2003 37 44
24. Jacobs, L. J. & Kuper, G. H. (2004) Indicators of financial crises do work! An early-warning system for six Asian countries. CCSO Working Paper 13. Department of Economics, University of Groningen, the Netherlands.
25. F. Jones Current techniques in bankruptcy prediction Journal of Accounting Literature 6 1987 131 164
26. M. Katz Multivariable analysis: A practical guide for clinicians 2006 Churchill-Livingstone New York
27. A. Kloptchenko, T. Eklund, J. Karlsson, B. Back, H. Vanhatanta, and A. Visa Combining data and text mining techniques for analyzing financial reports Intelligent Systems in Accounting Finance and Management 12 1 2004 29 41
28. Koyuncugil, A. S. (2006). Fuzzy Data Mining and its application to capital markets. Unpublished doctoral dissertation, Ankara University, Ankara.
29. Koyuncugil, A. S., & Ozgulbas, N. (2006a). Financial profiling of SMEs: An application by data mining. In Proceedings Book of the European Applied Business Research (EABR) Conference, Clute Institute for Academic Research, pp. 1-22.
30. A.S. Koyuncugil, and N. Ozgulbas Is there a specific measure for financial performance of SMEs? The Business Review, Cambridge 5 2 2006 314 319
31. Koyuncugil, A. S., & Ozgulbas, N. (2006c). Determination of factors affected financial distress of SMEs listed in ISE by data mining. In Proceedings Book of 3rd Congress of SMEs and Productivity, KOSGEB and Istanbul Kultur University, Istanbul.
32. Koyuncugil, A. S., & Ozgulbas, N. (2007a). Developing financial early warning system via data mining. In Proceedings Book of 4th Congress of SMEs and Productivity, Istanbul, pp. 153-166.
33. A.S. Koyuncugil, and N. Ozgulbas Detecting financial early warning signs in Istanbul Stock Exchange by data mining International Journal of Business Research VII 3 2007 188 193
34. Koyuncugil, A. S., & Ozgulbas, N. (2008a). Early warning system for SMEs as a financial risk detector. In Hakikur Rahman (Ed.), Data mining applications for empowering knowledge societies (pp. 221-240), New York: Idea Group Inc.
35. A.S. Koyuncugil, and N. Ozgulbas Strengths and weaknesses of SMEs listed in ISE: A CHAID Decision Tree application Journal of Dokuz Eylul University, Faculty of Economics and Administrative Sciences 23 1 2008 1 22
36. Koyuncugil, A. S., & Ozgulbas, N. (2009a). Measuring and hedging operational risk by data mining. In Proceedings Book of World Summit on Economic-Financial Crisis and International Business, Washington, pp. 1-6.
37. Koyuncugil, A. S., & Ozgulbas, N. (2009b). An intelligent financial early warning system model based on data mining for SMEs. In Proceedings of the International Conference on Future Computer and Communication, Kuala Lumpur, Malaysia. doi:10.1109/ICFCC.2009.118, pp. 662-666.
38. Koyuncugil, A. S., & Ozgulbas, N. (2010). Social Aid fraud detection system and poverty map model suggestion based on data mining for social risk mitigation. In A. S. Koyuncugil, & N. Ozgulbas (Eds.). Surveillance Technologies and Early Warning Systems: Data Mining Applications for Risk Detection (pp. 173-193), New York: Idea Group Inc.
39. N. Kumar, R. Krovi, and B. Rajagopalan Financial decision support with hybrid genetic and neural based modeling tools European Journal of Operational Research 103 1997 339 349 (Pubitemid 127400425)
40. Kyong, J. O, Tae, Y. K, Chiho, K., & Suk, J. L. (2006). Using neural networks to tune the fluctuation of daily financial condition indicator for financial crisis forecasting. Advances in Artificial Intelligence, DOI:10.1007/11941439-65 Volume 4304/2006.
41. K. Laitinen, and H.G. Chong Early warning system for crisis in SMEs: Preliminary evidence from Finland and the UK Journal of Small Business and Enterprise Development 6 1 1999 89 102
42. K.C. Lee, I. Han, and Y. Kwon Hybrid neural network models for bankruptcy predictions Decision Support Systems 18 1996 63 73
43. S.J. Lee, and K. Siau A review of data mining techniques Industrial Management & Data Systems 101 1 2001 41 46
44. S.H. Lee, and J.L. Urrutia Analysis of insolvency prediction in the property-liability insurance industry: A comparison of logit and hazard models Journal of Risk and Insurance 63 1996 121 130 (Pubitemid 126420882)
45. C. Magnusson, A. Arppe, T. Eklund, and B. Back The language of quarterly reports as an indicator of change in the company's financial status Information & Management 42 2005 561 570
46. P.A. Meyer, and H.W. Pifer Prediction of bank failures Journal of Finance XXV 4 1970 853 886
47. Monk, E., & Wagner, B. (2006). Concepts in Enterprise Resource Planning.Second Edition. Boston: Thomson Course Technologies.
48. Nomenclature statistique des activités économiques dans la Communauté européenne (NACE). (2009). Retrieved August 2010, from .
49. S. Nazem, and B. Shin Data mining: New arsenal for strategic decision making Journal of Database Management 10 1 1999 39 42
50. OECD (2009). Policy responses to the economic crisis: Investing in innovation for long-term growth. Retrieved August 2010, from .
51. Ozgulbas, N., & Koyuncugil, A. S. (2006). Profiling and determining the strengths and weaknesses of SMEs listed in ISE by the Data Mining Decision Trees Algorithm CHAID. In 10th National Finance Symposium, Izmir.
52. Ozgulbas N., & Koyuncugil A. S. (2009). Developing road maps for financial decision making by CHAID Decision Tree. In Proceeding of International Conference on Information Management and Engineering (pp. 723-727), IEEE Computer Society Press.
53. Ozgulbas, N., & Koyuncugil, A. S. (2010) Financial early warning system for risk detection and prevention from financial crisis. In Surveillance Technologies and Early Warning Systems: Data Mining Applications for Risk Detection. In A. S. Koyuncugil, & N. Ozgulbas (Eds.). Surveillance Technologies and Early Warning Systems: Data Mining Applications for Risk Detection (pp. 76-108). New York: Idea Group Inc.
54. C. Pantalone, and M. Platt Predicting failures of savings and loan associations AREUEA Journal 15 1987 46 64
55. S.A. Ross, R. Westerfield, and B.D. Jordan Fundamentals of corporate finance 2008 McGraw-Hill Publishing Company New York
56. V. Salas, and J. Saurina Credit risk in two institutional regimes: Spanish commercial and savings banks Journal of Financial Services Research 22 3 2002 203 224 (Pubitemid 37131552)
57. R.J. Taffler, and H. Tisshaw Going, going, gone-four factors which factors which predict Accountancy March 1977 50 54
58. K.Y. Tam, and M.Y. Kiang Managerial applications of neural networks: The case of bank failure predictions Decision Sciences 38 1992 926 948
59. Terdpaopong, K. (2008). Financially distressed, small and medium-sized enterprises' characteristics and discriminant analysis model: Evidence from the Thai market. In: Proceedings of the Small Enterprise Association of Australia & New Zealand (SEAANZ) Conference 2008, Sydney, Australia, ISBN: 978-0-646-50668-5 p.125-165. Retrieved August 2010, from .
60. Thearling, K. (2004). Information about data mining and analytic technologies. Retrieved August 2010, from .
61. J.S. Trieschmann, and G.E. Pinches A multivariate model for predicting financially distressed property-liability insurers Journal of Risk and Insurance 40 1973 327 338
62. J. Warner Bankruptcy costs: some evidence Journal of Finance 32 1977 337 347
63. B. Yang, X.L. Ling, J. Hai, and X. Jing An early warning system for loan risk assessment using artificial neural networks Knowledge Based Systems 14 5-6 2001 303 306 (Pubitemid 32707396)
64. C. Zavgren Assessing the vulnerability to failure of American industrial firms: A logistics analysis Journal of Accounting Research 22 1985 59 82
65. M.E. Zmijewski Methodological issues related to the estimation of financial distress prediction models Journal of Accounting Research Supplement 1984 59 82