Hospitality Bankruptcy in United States of America: A Multiple Discriminant Analysis-Logit Model Comparison

References

• Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance, 23(4), 589–609. doi:10.1111/j.1540-6261.1968.tb00843.x
   Web of Science ®Google Scholar
• Altman, E. I. (1993). Corporate financial distress and bankruptcy (2nd ed.). New York, NY: John Wiley and Sons.
   Google Scholar
• Altman, E. I., Iwanicz-Drozdowska, M., Laitinen, E. K., & Suvas, A. (2014). Distressed firm and bankruptcy prediction in an international context: A review and empirical analysis of Altman’s Z-Score model. Available at SSRN 2536340. Retrieved from http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2536340
   Google Scholar
• American Bankruptcy Institute (2014, November 11). Statistics from the Administrative Office of the U.S. Courts. Retrieved from http://news.abi.org/statistics
   Google Scholar
• Andrew, W. P., Schmidgall, R. S., & Damitio, J. W. (1993). Financial management for the hospitality industry. East Lansing, MI: Educational Institute of the American Hotel & Motel Association.
   Google Scholar
• Beaver, W. (1966). Financial ratios as predictors of failure. Empirical research in accounting: Selected studies. Supplement to Journal of Accounting Research, 4, 71–111. doi:10.2307/2490171
   Web of Science ®Google Scholar
• Borenstein, S., & Rose, N. L. (2003). The impact of bankruptcy on airline service levels. The American Economic Review, 93(2), 415–419. doi:10.1257/000282803321947443
   Web of Science ®Google Scholar
• Canbas, S., Cabuk, A., & Kilic, S. B. (2005). Prediction of commercial bank failure via multivariate statistical analysis of financial structures: The Turkish case. European Journal of Operational Research, 166(2), 528–546. doi:10.1016/j.ejor.2004.03.023
   Web of Science ®Google Scholar
• Chan, E. S., & Lam, D. (2013). Hotel safety and security systems: Bridging the gap between managers and guests. International Journal of Hospitality Management, 32, 202–216. doi:10.1016/j.ijhm.2012.05.010
   Web of Science ®Google Scholar
• Chi, L.-C., & Tang, T.-C. (2006). Bankruptcy prediction: Application of Logit analysis in export credit risks. Australian Journal of Management, 31(1), 17–27. doi:10.1177/031289620603100102
   Google Scholar
• Chintrakarn, P., Jiraporn, P., & Kim, J. C. (2013). The effect of corporate governance on CEO luck: Evidence from the Institutional Shareholder Services (ISS). Finance Research Letters, 10(4), 169–174. doi:10.1016/j.frl.2013.06.001
   Web of Science ®Google Scholar
• Cook, R. A., & Nelson, J. L. (1998). A conspectus of business failure forecasting. Small Business Institute Director’s Association. Available at: http://sbida.org/Resources/Documents/Proceedings/1988%20Proceedings.pdf#page=205
   Google Scholar
• Diakomihalis, M. N. (2012). The accuracy of Altman’s models in predicting hotel bankruptcy. International Journal of Accounting and Financial Reporting, 2(2), 96. doi:10.5296/ijafr.v2i2.2367
   Google Scholar
• Etheridge, H. L., Sriram, R. S., & Hsu, H. Y. K. (2000). A comparison of selected artificial neural networks that help auditors evaluate client financial viability. Decision Sciences, 31(2), 531–550. doi:10.1111/deci.2000.31.issue-2
   Web of Science ®Google Scholar
• Fulmer, J. G., Moon, J. E., Gavin, T. A., & Erwin, M. (1984). A bankruptcy classification model for small firms. Journal of Commercial Bank Lending, 66(11), 25–37.
   Google Scholar
• Gu, Z. (2002). Analyzing bankruptcy in the restaurant industry: A multiple discriminant model. International Journal of Hospitality Management, 21(1), 25–42. doi:10.1016/S0278-4319(01)00013-5
   Google Scholar
• Gu, Z., & Gao, L. (2000). A multivariate model for predicting business failures of hospitality firms. Tourism and Hospitality Research, 2(1), 37–49. doi:10.1177/146735840000200108
   Google Scholar
• Hamdi, M., & Mestiri, S. (2014). Bankruptcy prediction for Tunisian firms: An application of semi-parametric logistic regression and neural networks approach. Economics Bulletin, 34(1), 133–143.
   Google Scholar
• Hua, N., & Upneja, A. (2012). Financial characteristics and outperformance: Evidence of a contemporary framework from the US lodging industry. International Journal of Contemporary Hospitality Management, 24(4), 574–593. doi:10.1108/09596111211226833
   Web of Science ®Google Scholar
• Kim, H. (2006). A logistic regression analysis for predicting bankruptcy in the hospitality industry. The Journal of Hospitality Financial Management, 14(1), 17–34. doi:10.1080/10913211.2006.10653812
   Google Scholar
• Kim, H., & Gu, Z. (2006). Predicting restaurant bankruptcy: A Logit model in comparison with a discriminant model. Journal of Hospitality & Tourism Research, 30(4), 474–493. doi:10.1177/1096348006290114
   Google Scholar
• Kim, H., & Gu, Z. (2010). A logistic regression analysis for predicting bankruptcy in the hospitality industry. Journal of Hospitality Financial Management, 14(1), 24.
   Google Scholar
• Kim, M.-J., & Kang, D.-K. (2010). Ensemble with neural networks for bankruptcy prediction. Expert Systems with Applications, 37(4), 3373–3379. doi:10.1016/j.eswa.2009.10.012
   Web of Science ®Google Scholar
• Kim, S. Y. (2011). Prediction of hotel bankruptcy using support vector machine, artificial neural network, logistic regression, and multivariate discriminant analysis. The Service Industries Journal, 31(3), 441–468. doi:10.1080/02642060802712848
   Web of Science ®Google Scholar
• Kim, S. Y., & Upneja, A. (2014). Predicting restaurant financial distress using decision tree and AdaBoosted decision tree models. Economic Modelling, 36, 354–362. doi:10.1016/j.econmod.2013.10.005
   Web of Science ®Google Scholar
• Kim Yen, P, & Manh Hiep, N. (2014). Modeling of financial distress probability for Vietnamese listed companies. Journal of Economics and Development, 16(3), 68.
   Google Scholar
• Klecka, W. R. (1985). Discriminant analysis. Thousand Oaks, CA: Sage.
   Google Scholar
• Kwak, W., Cheng, X., Ni, J., Shi, Y., Gong, G., & Yan, N. (2014). Bankruptcy prediction for Chinese firms: Comparing data mining tools with logit analysis. Journal of Modern Accounting and Auditing, 10, 10.
   Google Scholar
• Lahiri, M. (2013). Measuring the financial health of indian oil corporation limited using Z score model. Fleet Card, 32.
   Google Scholar
• Lam, M., & Trinkle, B. S. (2014). Using prediction intervals to improve information quality of bankruptcy prediction models. In Advances in business and management forecasting (pp. 37–52). Bingley, UK: Emerald Group Publishing Limited.
   Google Scholar
• Lee, S., & Choi, W. S. (2013). A multi-industry bankruptcy prediction model using back- propagation neural network and multivariate discriminant analysis. Expert Systems with Applications, 40(8), 2941–2946. doi:10.1016/j.eswa.2012.12.009
   Web of Science ®Google Scholar
• Lee, S., Koh, Y., & Xiao, Q. (2014). Internationalization and financial health in the US hotel industry. Tourism Economics, 20(1), 87–105. doi:10.5367/te.2013.0261
   Web of Science ®Google Scholar
• Lennox, C. (1999). Identifying failing companies: A re-evaluation of the Logit, probit and DA approaches. Journal of Economics and Business, 51(4), 347–364. doi:10.1016/S0148-6195(99)00009-0
   Google Scholar
• Li, H., & Sun, J. (2012). Forecasting business failure: The use of nearest-neighbor support vectors and correcting imbalanced samples–Evidence from the Chinese hotel industry. Tourism Management, 33(3), 622–634. doi:10.1016/j.tourman.2011.07.004
   Web of Science ®Google Scholar
• Lo, A. W. (1986). Logit versus discriminant analysis: A specification test and application to corporate bankruptcies. Journal of Econometrics, 31(2), 151–178. doi:10.1016/0304-4076(86)90046-1
   Web of Science ®Google Scholar
• Lukason, O., & Hoffman, R. C. (2014). Firm bankruptcy probability and causes: An integrated study. International Journal of Business and Management, 9(11), 80. doi:10.5539/ijbm.v9n11p80
   Google Scholar
• Lussier, R. N. (1995). A nonfinancial business success versus failure prediction model for young firms. Journal of Small Business Management, 33(1), 8–20.
   Web of Science ®Google Scholar
• Mao, Z. (2006). Investigation of the relationship between firm-wise financial factors and firm performance in the hospitality industry. International Journal of Contemporary Hospitality Management, 12, 2.
   Google Scholar
• Nam, J.-H., & Jinn, T. H. (2000). Bankruptcy prediction: Evidence from Korean listed companies during the IMF crisis. Journal of International Financial Management and Accounting, 11(3), 178–197. doi:10.1111/jifm.2000.11.issue-3
   Google Scholar
• Neophytou, E., & Molinero, C. M. (2004). Predicting corporate failure in the UK: A multidimensional scaling approach. Journal of Business Finance and Accounting, 31, 677–710. doi:10.1111/j.0306-686X.2004.00553.x
   Google Scholar
• Pang, J., & Kogel, M. (2013). Retail bankruptcy prediction. American Journal of Economics and Business Administration, 5(1), 29–46. doi:10.3844/ajebasp.2013.29.46
   Google Scholar
• Park, S., & Kim, B. (2000). A study on the prediction of small and medium firm’s failure in Korea. Journal of Business Research, 15(1), 27–53.
   Google Scholar
• Park, S. S. (2008). A comparative study of Logit and artificial neural networks in predicting bankruptcy in the hospitality industry. Ann Arbor, MI: ProQuest.
   Google Scholar
• Parsa, H. G., Self, J. T., Njite, D., & King, T. (2005). Why restaurants fail. Cornell Hotel and Restaurant Administration Quarterly, 46(3), 304–322. doi:10.1177/0010880405275598
   Google Scholar
• Parsa, H. G., Gregory, A., & Terry, M. (2010). Why do restaurants fail? Part III: An analysis of macro and micro factors. Emerging Aspects Redefining Tourism and Hospitality, 1(1), 16–25.
   Google Scholar
• Schmidgall, R., & Damitio, J. (2006). Hospitality financial accounting (3rd ed.). Washington, DC: EI-AH&LA.
   Google Scholar
• Sheppard, J. P. (1994). The dilemma of matched pairs and diversified firms in bankruptcy prediction models. The Mid-Atlantic Journal of Business, 30(1), 9–25.
   Google Scholar
• Soon, N. K., Mohammed, A. A. E., & Mostafa, M. R. (2014). Using Altman’s Z-Score model to predict the financial hardship of companies listed in the trading services sector of Malaysian stock exchange. Australian Journal of Basic & Applied Sciences, 8(6), 379–384.
   Google Scholar
• Springate, G. L. V. (1978). Predicting the possibility of failure in a Canadian firm: A discriminant analysis ( Unpublished M.B.A). Research project, Simon Fraser University, Burnaby, Canada.
   Google Scholar
• Standard & Poor’s. (2010). Standard & Poor’s industry surveys. S&P Net Advantage database. New York, NY: McGraw-Hill Financial.
   Google Scholar
• Stickney, C. P. (1996). Financial reporting and statement analysis (3rd ed.). Worth, TX: The Dryden Press. Ft.
   Google Scholar
• Storey, D., Keasey, K., Watson, R., & &Wynarczyk, P. (1990). The performance of small firms: Profits, jobs, and failures. London, UK: Routledge Small Business Series.
   Google Scholar
• Sun, J., Li, H., Huang, Q.-H., & He, K.-Y. (2014). Predicting financial distress and corporate failure: A review from the state-of-the-art definitions, modeling, sampling, and featuring approaches. Knowledge-Based Systems, 57, 41–56. doi:10.1016/j.knosys.2013.12.006
   Web of Science ®Google Scholar
• Theodossious, P. (1991). Alternative models for assessing the financial condition of business in Greece. Journal of Business Finance & Accounting, 18(5), 697–720. doi:10.1111/jbfa.1991.18.issue-5
   Google Scholar
• U.S. Courts. (2014a, November 12), Bankruptcy basics. Retrieved from http://www.uscourts.gov/bankruptcycourts/bankruptcybasics/process.html
   Google Scholar
• U.S. Courts. (2014b). Bankruptcy basics. Retrieved from http://www.uscourts.gov/FederalCourts/Bankruptcy/BankruptcyBasics/Chapter7.aspx
   Google Scholar
• Weitzel, W., & Jonsson, E. (1989). Decline in organizations: A literature integration and extension. Administrative Science Quarterly, 91–109.
   Web of Science ®Google Scholar
• Wruck, K. H. (1990). Financial distress, reorganization, and organizational efficiency. Journal of Financial Economics, 27(2), 419–444. doi:10.1016/0304-405X(90)90063-6
   Web of Science ®Google Scholar
• Wu, C. Y. (2004). Using non-financial information to predict bankruptcy: A study of public companies in Taiwan. International Journal of Management, 21(2), 194–201.
   Google Scholar
• Yang, H. (2006). Bankruptcy situation model in small business: The case of restaurant firms. FIU Hospitality Review, 24(2), 49–58.
   Google Scholar
• Youn, H., & Gu, Z. (2010). Predict US restaurant firm failures: The artificial neural network model versus logistic regression model. Tourism and Hospitality Research, 10(3), 171–187. doi:10.1057/thr.2010.2
   Google Scholar
• Yu, R. (2009). Travel News. Retrieved from http://usatoday30.usatoday.com/travel/hotels/2009-03-05-foreclosed-hotels_N.htm
   Google Scholar
• Zavgren, C. (1983). The prediction of corporate failure: The state of the art. Journal of Accounting Literature, 2(1), 1–38.
   Google Scholar