Performance Classification, Technical Efficiency and Early Warning System-An Empirical Study of Taiwanese Financial Industry

• Main Authors: Hung-Chih Liu, 劉虹芝
• Other Authors: Ting-Kun Liu
• Format: Others
• Language: zh-TW
• Published: 2008
• Online Access: http://ndltd.ncl.edu.tw/handle/22997661667084846336

碩士 === 朝陽科技大學 === 財務金融系碩士班 === 96 === After implementing the financial innovation, more and more large-scale financial holding companies have established to make the financial industry competed closely. But the loan quality and non-performing loans problems have already badly to weaken the bank. It is important to build and construct the complete early warning model of finance. By many literatures, always count the financial ratio and macroeconomics to forecast the financial crisis. However, the internal managerial problems will still cause a financial distress. Therefore, this study uses efficiency to forecast the financial crisis. This study aims to build an early warning model by using the financial information among 36 domestic banks from the first season in 2001 to the third season in 2007.This study employs scores of factor analysis approach to evaluate the performance. This study classifies the banks into three categories according to their performance evaluation results: excellent, ordinary and problematic. Then we adopt data envelopment analysis to judge the operation performance of banks. Further, this study intends to investigate the impact of three variables, financial ratios, and macroeconomics and efficiency value on the performance of banks by using the multiple discriminate analysis of financial distress. The major findings of the empirical result: most of the banks are known for their history and scale, and financial holding companies have the highest total efficiency value by the data envelopment analysis. The empirical results also show that according to the efficiency there are seven banks of holding company on these banks. In addition, by using the multiple discriminate analysis of financial distress the prediction rate is approached to 69.2 percents.