E-ISSN : 2233-5382
Purpose - This current study will investigate the average financial ratio of top and failed five-star hotels in the Jeju area. A total of 14 financial ratio variables are utilized. This study aims to; first, assess financial ratio of the first-class hotels in Jeju to establishing variables, second, develop distress prediction model for the first-class hotels in Jeju district by using logit analysis and third, evaluate distress prediction capacity for the first-class hotels in Jeju district by using logit analysis. Research design, data, and methodology - The sample was collected from year 2015 and 14 financial ratios of 12 first-class hotels in Jeju district. The results from the samples were analyzed by t-test, and the independent variables were chosen. This was an empirical study where the distress prediction model was evaluated by logit analysis. This current research has focused on critically analyzing and differentiating between the top and failed hotels in the Jeju area by utilizing the 14 financial ratio variables. Results - The verification result of the accuracy estimated by logit analysis has shown to indicate that the distress prediction model's distress prediction capacity was 83.3%. In order to extract the factors that differentiated the top hotels in the Jeju area from the failed hotels among the 14 chosen, the analysis of t-black was utilized by independent variables. Logit analysis was also used in this study. As a result, it was observed that 5 variables were statistically significant and are included in the logit analysis for discernment of top and failed hotels in the Jeju area. Conclusions - The distress prediction press' prediction capability was compared in this research analysis. The distress prediction press prediction capability was shown to range from 75-85% by logit analysis from a previous study. In this current research, the study's prediction capacity was shown to be 83.33%. It was considered a high number and was found to belong to the range of the previous study's prediction capacity range. From a practical perspective, the capacity of the assessment of the distress prediction model in the top and failed hotels in the Jeju area was considered to be a prominent factor in applications of future hotel appraisal.
Altman, E. I. (1968). Altman, Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy. Journal of Finance, 23(4), 589-609.
Azayite, F. Z., & Achchab, S. (2016). Hybrid Discriminant Neural Networks for Bankruptcy Prediction and Risk Scoring. Computer Science, 83, 670-674.
Baek, H. G. (2011). An Empirical Study on Predicting Corporate Failure. Master’s thesis, Yonsei University.
Beaver, W. H. (1966). Financial Ratios as Predictors of Failure. Journal of Accounting Research, 5, 71-111.
Deakin, E. B. (1972). A Discriminant Analysis of Predictors of Business Failure. Journal of Accounting Research, 16(Spring), 167-179.
Ho, L. C. (2013). Relationship Between Stock Price Indices of Abu Dhabi, Jordan, and USA – Evidence from the Panel Threshold Regression Model. International Journal of Industrial Distribution &Business. 4(2), 13-19.
Hong, S. H. (1999). Using GA based Input Selection Method for Artificial Neural Networks Modeling:Application to Bankruptcy Prediction. Master’s thesis, Ewha Womans University.
Iturriaga, F. J. L., & Sanz, I. P. (2015). Bankruptcy Visualization and Prediction Using Neural Networks:A Study of U.S. Commercial Banks. Expert Systems with Applications, 42(6), 2857-2869.
Jo. S. W. (2007). A Study on the Forecasting of Accounting Fraud. Doctorial dissertation, Dankuk University.
Jung, K. W. (2014). A Study on the Default Prediction Model of SMES after Supporting the Credit Guarantee. Master’s thesis, Hanyang University.
Jung, Y. L. (2009). Financial Ratio and the Prediction of Corporate Financial Distress. Master’s thesis, Ewha Womans University.
Kang, K. H. (2012). Developing a Model to Predict the Insolvency of Medium and Small General Contractors. Master’s thesis, Hanyang University.
Kassar, T. A. A., & Soileau, J. S. (2014). Financial Performance Evaluation and Bankruptcy Prediction (failure). Arab Economics and Business Journal, 9, 147-155.
Kim, H. K. (2012). Management Performance Evaluation and Failure Prediction Models for Financial Institutions: Focusing on the cooperative financial institutions, Doctorial dissertation, Hankuk University of Foreign Studies.
Kim, S. J. (2005). Comparing Distress Prediction Models to the Hotel Corporate Structure: Based on Predictive Power. Journal of Tourism Science, 28(4), 9-26.
Kwon, O. K. (2011). An Empirical Study on the Forecasting Model of Specialty Constructors’ Insolvency. Master’s thesis, Chungang University.
Laitinen, E. K., & Suvas, A. (2016). Financial Distress Prediction in an International Context: Moderating Effects of Hofstede’s Original Cultural Dimensions. Journal of Behavioral and Experimental Finance, 9, 98-118.
Li, H. L., & Sun, J. (2011). Empirical Research of Hybridizing Principal Component Analysis with Multi-variate Discriminant Analysis and Logistic Regression for Business Failure Prediction. Expert Systems with Applications, 38, 6244-6253.
Lin, T. H. (2009). A Cross Model Study of Corporate Financial Distress Prediction in Taiwan: Multiple discriminant analysis, logit, probit and neural networks models. Neurocomputing, 72, 3507-3516.
Lin, F., Liang, D., & Chen, E. (2011). Financial ratio selection for business crisis prediction. Expert Systems with Applications. 38(12). 15094-15102.
Ma, S. S. (2012). The Usefulness of Earnings Management Information on Failure Prediction. Doctorial dissertation, Chonnam National University.
Mohammadi, S., & Esmaeilioghaz, H. (2017). A Study of the Impact of Accounting Information Quality and Information Asymmetry on Underinvestment in Iran. International Journal of Industrial Distribution &Business. 8(1), 33-39.
Nam, J. H., & Yi, K. B. (2002). Non-Financial Information and Comparison of Bankruptcy Prediction Model. Seogang Economic Review, 31(1), 1-29.
Ohlson, J. A. (1980). Financial Ratios and the Probability Prediction of Bankruptcy. Journal of Accounting Research, 18(1), 109-131.
Ok, J. K. (2010). Integrated Corporate Bankruptcy Prediction Model Using Genetic Algorithms. Doctorial dissertation, Dongkuk University.
Park, J. E., & Hong, J. B. (2010. 10). The Empirical Study to Identify the Distress Causes of Public Companies after Financial Crisis with Survival Analysis. Journal of the Korean Data Analysis Society, 12[5(B)], 713-724.
Sayari, N., & Mugan, C. S. (2017). Industry specific financial distress modeling. Business Research Quarterly, 20(1), 45-62.
Shirzad. A., Mohammadi, S., & Haghighi. R. (2015). Effect of Financial Performance on Earnings Management in the Drug Distribution Industry. International Journal of Industrial Distribution & Business, 6(4), 23-26.
Smaranda, C. (2014). Scoring Functions and Bankruptcy Prediction Models – Case Study for Rumanian Companies. Economics and Finance, 10, 217-226.
Xu, W., Xiao, Z., Dang, X., Yang, D., & Yang, X. (2014). Financial ratio selection for business failure prediction using soft set theory. Knowledge-Based Systems. 63, 59-67.
Zhao, Y. (2016). Research on the Environmental Issues in China’s Sustainable Economic Development. International Journal of Industrial Distribution &Business. 7(1), 15-17.