| Summary: | 碩士 === 健行科技大學 === 國際企業管理研究所 === 102 === This paper primarily used statistical methods to establish logistic credit risk models on companies, especially technology companies, and tried to find out the possible factors of Taiwan''s technology industry which cause financial risk. By using accounting data, it was possible for the company to predict the probability that the company experienced financial risks, to achieve the effect of prior warning and risk management. The sample was selected from technology firms which had financial risks and were listed on the Taiwan Stock Exchange or the OTC from 2000 to 2013.
The study compared 140 companies which experienced a financial crisis during this period with 140 healthy companies, being in the same industry with a similar scale of fixed assets, for a total of 280 samples, using the Beaver (1966) 1:1 principle.
This study considered six factors. They were financial structure, ability of paying debt, managing ability, profitability, cash flow and corporate governance. The author built up a credit risk model using K-S test, descriptive statistics, M-U test and the logistic regression model, to filter out the significant variables affecting corporate performance. The empirical results show that the ability of paying debt and the managing ability caused the main impact on the performance of the technology industry.
By improving the cash flow adequacy ratio ( the β value showed negative effects), and adjusting the interest coverage ratio ( the β value presented by the forward turned negative effects) can improve operations and profitability of non-family industry to reduce the likelihood of credit risk.
In particular, company should consider lowering the debt ratio and improve pre-tax net income and cash flow adequacy ratio. This will improve the company''s financial structure, enhance the ability of paying debt and managing ability. Therefore, the empirical value and managerial implications for the company''s accounting information to build a credit risk model can be effectively applied to financial forecasting and reduce the likelihood of credit risk.
Empirical values, management implications, and using accounting information to establish credit risk models, can effectively be applied to financial forecasting, in order to reduce the possibility of credit risk for the technology industry.
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