| Summary: | 碩士 === 國立臺北大學 === 國際財務金融碩士在職專班 === 107 === The business model of commercial banks mainly focuses on absorbing the public deposits and lending to the capital demander. Banks play the role of credit intermediary. Therefore, credit risk is the biggest risk for banks. With the quantification of risk assessment standard by Basel and IFRS, credit risk model has become an important tool to control risks at financial institutions.
This study is based on Taiwan listed and OTC companies (including emerging stock market) of the manufacturing industry during from 2000 to 2017. Paired samples of having one defaulted company paired with two non-defaulted companies have been adopted. There are four financial reports including the defaulted year (T) and the previous three years (T-1, T-2, T-3) for each company. In total there are 668 financial reports of defaulted companies and 1,312 reports of non-defaulted companies in this study. Furthermore, the paired samples have been divided into two groups - 80% of in-sample and 20% of out-of-sample. In-sample is used to build up the model and out-of-sample is used to validate the model. Logit model was adopted so as to build a stable early warning model of financial distress. Then threshold logistic regression is applied to Logit model, hence this study is called “Threshold Logistic Regression Model”. The debt ratio is be the threshold variable. It is to explore the changes of financial features after considering threshold variable by the model.
The final model has seven variables including debt ratio, the ratio of working capital to total asset, total asset turnover, the ratio of retained earnings to asset, basic earning power, shareholding ratio of directors and company scale. Each variable has significant effects for the prediction of company’s financial distress. The results of validation show that the AUC of in-sample is 88.80% and the predicting accuracy is 83.17%; the AUC of out-of-sample is 86.78% and the predicting accuracy is 82.65%. In the defaulted year and the previous three years, the predicting accuracy of in-sample is 87.66%, 87.12%, 81.12% and 76.67% respectively. The more long away from the defaulted time, the more declining the discriminating power the model is and the validation result of out-of-sample is the same as in-sample. However, the declining degree is not serious and it represents that the model has a good and stable prediction.
In consideration of the threshold effect, the threshold value of debt ratio is 60.488% and some variables are not significant effects for financial distress prediction in each group, including the “debt ratio”, “the ratio of retained earnings to asset” in high debt group and the“total asset turnover” in low debt group. It means that companies with different debt level have the different causes of financial distress. Besides, the variable “company scale” in high debt group shows that the bigger the company scale is, the higher chance to have financial distress. This conclusion is different from past studies showing that the large companies have lower chance to have financial distress. The results of coefficient test show that the two variables “the ratio of working capital to total asset” and “company scale” in the two groups of high debt and low debt are different. Under the threshold effect of debt ratio, it shows different impact on leading to financial distress.
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