Applicable Study on Taiwan Electronic Industry Financial Crisis Predictive Model Based on Logistic Regression

• Main Authors: Yi-Fang Chen, 陳義方
• Other Authors: 羅淑娟
• Format: Others
• Language: zh-TW
• Published: 2009
• Online Access: http://ndltd.ncl.edu.tw/handle/4jw82f

碩士 === 國立臺北科技大學 === 工業工程與管理研究所 === 97 === Recently, the occurrence of the landmine share contributed to great loss of public investors and most of them were the electronic industries which were the focus of investing. To protect the rights and interests of investors and creditors, it is necessary to construct a financial alert system. Most researches used the data of year, but it was too late to get the data in the end of the year or the beginning of next year. In this study, we introduced the data of quarters to the alert model. The result showed that the data of quarter was better than data of year, especially one year before crisis. To matching principle, most domestic researches only assumed the matching principle without experimenting it. This study categorized the data to pair (1:1) and non-pair (1:3). The result showed that obviously the pair data were better than non-pair data. To variables selection, most researches used financial balances to construct alert model but the result did not satisfying. This study added non-financial variables(governance variables and credit rating) to derive the most suitable model. The result showed that the accurate rate of the model adding governance variables and credit rating increased to 90.9%. The result showed that debt ratios、EPS and Total asset turnover ratio are all prior index. Among governance variables and Credit rating, Debt ratio、Flow rate、Total asset turnover ratio、EPS、Pledge ratio of directors and credit rating of TCRI were prior index discriminating normal and crisis. The posterior index were Growth rate of total assets. The best model in this study used financial balances, governance variables and credit rating of quarter. Hopefully, we could provide the public of investors and managers a tool to examine enterprises and lower the investing risk.