Default Prediction for Small-Medium Enterprises in Emerging Market: Evidence from Thailand
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Small-medium enterprises (SMEs) play an important role in the economy worldwide and they normally need to borrow funds from financial institutions. Thus, an accurate credit risk model to predict the probability that these firms might be bankrupt and cannot pay back the loans on time is very crucial. However, the studies based on SME data are very rare especially for those in emerging markets. This study develops the SME models by employing both the Multivariate Discriminant Analysis (MDA) and Logistic Regression Analysis (Logit) model in predicting bankruptcy of SMEs in Thailand. The samples cover the period 2000 – 2010. The result shows that the Logit model gives higher predictive accuracy level at 85.5 percent for out-of-sample test. Moreover, the combined forecasts of bankruptcy firms from both MDA and Logit models could help achieve even higher predictive accuracy level.
Abstract
Small-medium enterprises (SMEs) play an important role in the economy worldwide and they normally need to borrow funds from financial institutions. Thus, an accurate credit risk model to predict the probability that these firms might be bankrupt and cannot pay back the loans on time is very crucial. However, the studies based on SME data are very rare especially for those in emerging markets. This study develops the SME models by employing both the Multivariate Discriminant Analysis (MDA) and Logistic Regression Analysis (Logit) model in predicting bankruptcy of SMEs in Thailand. The samples cover the period 2000 – 2010. The result shows that the Logit model gives higher predictive accuracy level at 85.5 percent for out-of-sample test. Moreover, the combined forecasts of bankruptcy firms from both MDA and Logit models could help achieve even higher predictive accuracy level.
참고문헌 (39)
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