| Summary: | 碩士 === 國立臺灣大學 === 會計學研究所 === 94 === Recently Taiwan’s government has positively enacted financial bills and strengthened banking supervision while labeling 2001 as “the Year of Financial Reform” to expeditiously promote several critical measures of financial improvement. As the assessment and supervision of bank’s performance are getting significant, this study intends to build a stepwise logistic regression model for the Financial Early-Warning System (hereinafter referred to as EWS model) and takes advantage of its cumulative probability function to estimate the probability of being classified as the group with unsatisfactory condition for each bank. This model can serve as a possible guide to bank monitoring in an auxiliary role for the current “Call Report Percentile Ranking System” as well as can be a reference for banking regulators to efficiently allocate examination resources. Moreover, in terms of “Deposit Insurance Risk-based Premium System”, the risk level of individual insured institution can be determined by the probability derived from such model.
In the study, model A and model B are respectively established by different definitions of problem banks. According to West (1985) and Taiwan’s “Implementation Scheme for the Deposit Insurance Risk-based Premium System”, it is single-year model 88-A, 89-A, 90-A, and three-year model 88-89-90-A that are built on the bases of “Composite scores of the Examination Data Rating System”. In contrast, to build single-year model 88-B, 89-B, 90-B, and three-year model 88-89-90-B, three main financial indicators, such as “capital adequacy ratio”, etc., are chosen from those in Taiwan’s prompt corrective action to identify problem banks.
The empirical results show that both model A and B, especially the single-year models, are able to correctly identify the problem banks of the original samples with higher percentage of correct classifications, which is from 89.13% to 95.56%, and with better average weighted efficiency indicator, which is around 66.65%. With regard to the prediction results using holdout samples of next year, model 88-A, 89-A, and 90-A outperform model 88-89-90-A and model B in every test in terms of higher percentage of correct classifications and weighted efficiency indicator, which are from 73.91% to 82.61% and up to 53.40% respectively. Moreover, the study finds that (1) in order to increase the prediction accuracy of EWS models, the data set chosen from one-year period to build the models is better than that from three-year period. (2) Timely modifications and revisions of EWS models are necessary to meet with the rapid changes in the financial market and operational risks. (3) All the areas of CAMELS rating system of the US Federal Financial Supervisory agencies are relevant to the assessment of banks’ operational condition. No one can be neglected. (4) Due to the inherited limitation of predictive errors, it is appropriate to analyze and emphasize the long-term trend of models’ results rather than just to focus on short-term results, which might happen to be an error. The EWS model must also be complemented by on-site examination and other methods to enhance its usefulness.
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