The objectives of this study are to predict bankruptcy risk among SMEs in the hospitality industry for a three-year horizon period and to investigate the factors that are significant in determining bankruptcy. The contribution of SMEs in the hospitality industry is essential as businesses in the hospitality industry are dominated by SME operators. However, the failure rate among SMEs is relatively high and almost 50 percent of hospitality establishments do not survive beyond five years of operation. The Stepwise logistic model was employed to determine significant predictors that could predict bankruptcy for the period of one year, two years and three years before bankruptcy. Return on assets and firm age were found to be significant in all periods while other variables were identified to be important at a specific period prior to bankruptcy. In addition to return on assets and firm age, debt ratio and total assets turnover were found to be significant predictors of bankruptcy one-year prior to bankruptcy. However, in the two years prior to bankruptcy, debt ratio and total assets turnover were no longer important but current ratio, ownership concentration and gender diversity were found to be significant. As for the three years prior to bankruptcy, additional variables namely debt-to-equity ratio and board size were found to be significant, but ownership concentration and gender diversity ceased to be important. The findings of this study contribute to the limited literature in predicting the bankruptcy risk of small firms for a three-year horizon period by providing empirical evidence from SMEs in the hospitality industry of Malaysia.

SMEs are an important segment of the Malaysian economy and contribute significantly to the country's economic growth. Nonetheless, SMEs are riskier and associated with a high failure rate. Hence, the aim of this study is to develop a failure prediction model for SMEs in the hospitality industry by using the logit and artificial neural network (ANN) approach for 82 SMEs over the period 2000 to 2016. The findings show that the ANN model predicts better than the logit model in both the estimation and holdout sample with a predictive accuracy rate of 98.2% and 92%, respectively, while the logit model provides overall accuracy rates of 86% and 80%, respectively. This study also finds that both models identify return on assets and board size as an important signal of business failure. The models could be used to assist investors, creditors and lenders to screen out failing SMEs, and the authorities could decide on policies to improve SMEs in the hospitality industry.