Predicting Korean lodging firm failures: An artificial neural network model along with a logistic regression model

International Journal of Hospitality Management

Volumn 29, Issue 1, 2010, Pages 120-127

  Youn, Hyewon ^a   Gu, Zheng ^b  

^a UNIVERSITY OF NORTH TEXAS   (United States)

^b UNIVERSITY OF NEVADA   (United States)

Author keywords

Artificial neural networks; Failure prediction; Financial ratios; Korean lodging firms; Leverage; Logistic regression

Indexed keywords

EID: 71549144059     PISSN: 02784319     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ijhm.2009.06.007     Document Type: Article

References (54)

1. Altman E.I. The success of business failure prediction models. Journal of Banking & Finance 8 2 (1984) 171-198
2. Altman E.I., Eom Y.H., and Kim D.W. Failure prediction: evidence from Korea. Journal of International Financial Management & Accounting 6 3 (1995) 230-249
3. Altman E.I., Haldeman R., and Narayanan P. ZETA analysis: a new model to identify bankruptcy risk of corporations. Journal of Banking and Finance 1 (1977) 29-54
4. Anandarajan M., Lee P., and Anandarajan A. Bankruptcy prediction of financially stressed firms: an examination of the predictive accuracy of artificial neural networks. International Journal of Intelligent Systems in Accounting, Finance and Management 10 2 (2001) 69-81
5. Aziz M.A., and Dar H.A. Predicting corporate bankruptcy: where we stand?. Corporate Governance 6 1 (2006) 18-33
6. Bank of Korea, 2008. Korean economy. Retrieved from: .
7. Beaver W.H. Financial ratios as prediction of failure. Empirical research in accounting: selected studies. Journal of Accounting Research 4 3 (1966) 71-111
8. Bongini P., Ferri G., and Hahm H. Corporate bankruptcy in Korea: only the strong survive?. The Financial Review 35 4 (2000) 31-50
9. Bose I., and Pal R. Predicting the survival of failure of click-and-mortar corporations: a knowledge discovery approach. European Journal of Operational Research 174 (2006) 959-982
10. Charalambous C., Charitou A., and Kaourou F. Comparative analysis of artificial neural network models: application in bankruptcy prediction. Annals of Operations Research 99 1 (2000) 403-425
11. Charitou A., Neophytou E., and Charalambous C. Predicting corporate failure: empirical evidence for the UK. European Accounting Review 13 3 (2004) 465-497
12. Chi L.C., and Tang T.C. Bankruptcy prediction: application of logit analysis in export credit risks. Australian Journal of Management 13 1 (2006) 17-28
13. Cho, M., 1994. Predicting business failure in the hospitality industry: an application of logit model. Unpublished doctoral dissertation, Virginia Polytechnic Institute and State University.
14. Choi, H., 2008. More than 70% of lodging and restaurants firms fail within the first 5 years. Seoul Economy. Retrieved from: .
15. Denton J.W., Hung M.S., and Osyk B.A. A neural network approach to the classification problem. Expert Systems with Applications 1 4 (1990) 417-424
16. Dimitras A.I., Zanakis S.H., and 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
17. Etheridge H.L., Sriram R.S., and Hsu H.Y.K. A comparison of selected artificial neural networks that help auditors evaluate client financial viability. Decision Sciences 31 2 (2000) 531-550
18. Goss E.P., and Ramchandani H. Comparing classification accuracy of neural networks, binary logit regression and discriminant analysis for insolvency prediction of life insurers. Journal of Economics & Finance 19 3 (1995) 1-18
19. Gu Z. Analyzing bankruptcy in the restaurant industry: a multiple discriminant model. International Journal of Hospitality Management 21 1 (2002) 25-42
20. Hamer M.M. Failure prediction: sensitivity of classification accuracy to alternative statistical methods and variable sets. Journal of Accounting and Public Policy 2 (1983) 289-307
21. Hospitality Net, 2001. U.S. Hotel occupancy hits 10-year low-Ernst & Young revises 2001 national lodging forecast after September 11 attack. Retrieved from: .
22. Jain B.A., and Nag B.N. Performance evaluation of neural network decision models. Journal of Management Information Systems 14 2 (1997) 201-216
23. Kim, D., 2003. Special features of hotel industry from the perspective of credit valuation. Retrieved from: .
24. Kim H., and Gu Z. A logistic regression analysis for predicting bankruptcy in the hospitality industry. The Journal of Hospitality Financial Management 14 1 (2006) 17-34
25. Korean Statistical Information Service (KOSIS), 2008. Korean Statistical Information Service. Retrieved from .
26. Lee J.S. Causes for business failure: understanding the 1997 Korean crisis. Journal of Asian Economics 9 4 (1998) 637-651
27. Lee K., Booth D., and Alam P. A comparison of supervised and unsupervised neural networks in predicting bankruptcy of Korean firms. Expert Systems with Applications 29 1 (2005) 1-16
28. Lee K.C., Han I., and Kwon Y. Hybrid neural network models for bankruptcy predictions. Decision Support Systems 18 1 (1996) 63-72
29. Liao T.F. Interpreting Probability Models: Logit, Probit, and Other Generalized Linear Models (1994), SAGE Publications, Inc., Thousand Oaks, CA
30. Lo A.W. Logit versus discriminant analysis: a specification test and application to corporate bankruptcies. Journal of Econometrics 31 2 (1986) 151-178
31. Lussier R.N. A nonfinancial business success versus failure prediction model for young firms. Journal of Small Business Management 33 1 (1995) 8-20
32. Lussier R.N., and Pfeiffer S. A comparison of business success versus failure variables between U.S. and central eastern Europe Croatian entrepreneurs. Entrepreneurship Theory and Practice 24 4 (2000) 59-67
33. Luther R.K. An artificial neural network approach to predicting the outcome of chapter 11 bankruptcy. The Journal of Business and Economic Studies 4 1 (1998) 57-73
34. Nam J.H., and Jinn T.H. Bankruptcy prediction: evidence from Korean listed companies during the IMF crisis. Journal of International Financial Management and Accounting 11 3 (2000) 178-197
35. Neophytou E., and Molinero C.M. Predicting corporate failure in the UK: a multidimensional scaling approach. Journal of Business Finance and Accounting 31 5/6 (2004) 677-710
36. Olsen M., Bellas C., and Kish L. Improving the prediction of restaurant failure through ratio analysis. International Journal of Hospitality Management 2 4 (1983) 187-193
37. Palmer A., Montaño J.J., and Franconetti F.J. Sensitivity analysis applied to artificial neural networks for forecasting time series. European Journal of Research Methods for the Behavioral and Social Sciences 4 2 (2008) 80-86
38. Palmer A., Montaño J.J., and Sesé A. Designing an artificial neural network for forecasting tourism time series. Tourism Management 27 5 (2006) 781-790
39. Park, D., Choi, H., Ahn, K., 2008. KIS industry outlook: Hotels. Retrieved from: .
40. Ravi Kumar P., and 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
41. Schmidgall R.S., and DeFranco A.L. Ratio analysis: financial benchmarks for the club industry. The Journal of Hospitality Financial Management 12 1 (2004) 1-14
42. Schwei T. Key financial statement ratios. Small Business Forum 14 2 (1996) 54-55
43. Sexton R.S., Sriram R.S., and Etheridge H. Improving decision effectiveness of artificial neural networks: a modified genetic algorithm approach. Decision Sciences 34 3 (2003) 421-442
44. Shah J.R., and Murtaza M.B. A neural network based clustering procedure for bankruptcy prediction. American Business Review 18 2 (2000) 80-86
45. Shin K., and Lee Y. A genetic algorithm application in bankruptcy prediction modeling. Expert Systems with Applications 23 3 (2002) 321-328
46. Tam K.Y., and Kiang M.Y. Managerial applications of neural networks: the case of bank failure prediction. Management Science 38 7 (1992) 926-947
47. Van Horne J.C. Financial Management and Policy (1998), Prentice-Hall, Inc., Upper Saddle River, NJ
48. Warner J.B. Bankruptcy costs some evidence. The Journal of Finance 32 2 (1977) 337-347
49. Wilson R.L., and Sharda R. Bankruptcy prediction using neural networks. Decision Support Systems 11 5 (1994) 545-557
50. Wu W., Lee V.C.S., and Tan T.Y. Data preprocessing and data parsimony in corporate failure forecast models: evidence from Australian materials industry. Accounting and Finance 46 2 (2006) 327-345
51. Youn H.W., and Gu Z. A logit model for predicting Korean lodging firm business failure. World Journal of Tourism and Small Business Management 1 1 (2007) 74-82
52. Zahedi F.M. A meta-analysis of financial applications of neural networks. International Journal of Computational Intelligence and Organization 1 3 (1996) 164-178
53. Zavgren C.V. The prediction of corporate failure: the state of the art. Journal of Accounting Literature 2 (1983) 1-38
54. Zhang G., Hu M.Y., Patuwo B.E., and Indro D.C. Artificial neural networks in bankruptcy prediction: general framework and cross-validation analysis. European Journal of Operational Research 116 1 (1999) 16-32