Efficiency and credit rating in Taiwan banking: data envelopment analysis estimation

In this article, we use the data envelopment analysis to measure the efficiency of banks before and after credit rating is taken into consideration and we also employ the Malmquist Productivity Index to measure the total factor productivity changes from 2001 to 2003. The results are as follows: (1) There is a positive relationship between the efficiency scores and credit rating, and thus, the credit rating can be a representative to evaluate the performance and quality of a bank; (2) We use the Wilconxon two-sample test of nonparametric statistic to test the influences of credit rating. The empirical result shows that the credit rating is proven to influence the efficiency of banks; (3) The efficiency scores improve in both investment grade (above tw BBB?) and speculation grade (under tw BBB?), when credit rating is taken into consideration. The empirical results show that the efficiency scores of banks with a high credit rating improved relatively more when compared to banks with a lower credit rating; (4) In this research we also adopt the Malmquist index to observe the productivity and efficiency changes from year to year. We obtain results whereby the improvement of efficiency may be influenced greatly both from pure technical and scale efficiency changes.     

Measuring process capability index <Emphasis Type="Italic">C</Emphasis><Subscript><Emphasis Type="Italic">pm</Emphasis></Subscript> with fuzzy data

The process capability index C _pm , which considers the process variance and departure of the process mean from the target value, is important in the manufacturing industry to measure process potential and performance. This paper extends its applications to calculate the process capability index [(C)\tilde]_pm{\tilde {C}_{pm} } of fuzzy numbers. In this paper, the α-cuts of fuzzy observations are first derived based on various values of α. The membership function of fuzzy process capability index [(C)\tilde]_pm{\tilde {C}_{pm} } is then constructed based on the α-cuts of fuzzy observations. An example is presented to demonstrate how the fuzzy process capability index [(C)\tilde]_pm{\tilde {C}_{pm} } is interpreted. When the quality characteristic cannot be precisely determined, the proposed method provides the most possible value and spread of fuzzy process capability index [(C)\tilde]_pm{\tilde {C}_{pm} }. With crisp data, the proposed method reduces to the classical method of process capability index C _pm .