| Summary: | 碩士 === 國立高雄第一科技大學 === 財務管理所 === 97 === Crediting loans for enterprises is one of major banking business. The crediting quality influences bank structure. Amid drastically changeable economical situation, enterprises face uncertainty. The banks have to set up a specific credit evaluation mode and make scrupulous evaluation on enterprise credit with a view to heightening crediting quality and secure creditability. This evaluation mode is based on financial statements and ratio in order to effectively provide capital for enterprise development leading to win-win status.
The study at first examines a new version of 11financial ratios of enterprise credit evaluation from a local bank if they are effective for predicting financial distress. The debt-equity ratio and total asset turnover are significant variables on crediting quality which can be used as priority reference, but growth rate and debt/equity are not taken into consideration. There are few bankruptcy cases due to liquidation law here. Therefore to select enterprises on financial distress, the companies with full-cash delivery stocks, suspending trading on the market and delisted stocks from Taiwan Stock Exchange are my study samples. Financial distress prediction model is thus constructed for warning and is applicable for bank credit employee’s decision-making.
According to forward regression and backward regression experiment, the most decisive variable is total asset turnover (3 years/2 years/1 year before financial distress) followed by debt ratio((3 years/1 year before financial distress), debt/equity(2 years/1 year before financial distress) and total asset turnover growth rate(1 year before financial distress). Furthermore, 2 years before financial distress, debt/equity and total asset growth turnover rate are the most critical variables by means of forward regression and backward regression. Total asset turnover is significantly negative- related to financial distress occurrence. In addition, debt ratio, debt/equity and return on total assets are significantly positive- related to financial distress occurrence. With constructing financial distress prediction model, the prediction accuracy rate of the pilot sample or verified sample in financial distress ranging from 1 year ago to 3 years ago is 82%-91%. Especially that of 2 years ago enjoys the highest prediction accuracy.
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