Abstract
The aim of this paper is to use a new approach of performance evaluation, Grey Relation Analysis (GRA), which is a concept borrowed from the study of industry and increasingly applied to commerce. GRA is used to evaluate the relative performance of three investment Taiwanese trust firms, which have been reorganized into banks. The result of the study indicates that although the sample size is small and the distribution of data is unknown, GRA can still be successfully used in evaluating bank performance. In addition, this paper compares the GRA results with the Financial Statement Analysis (FSA) and shows that the same result can be obtained.
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Acknowledgements
The author is grateful for the assistance of Manager Liao of Human Resources Department and Miss Chang Chui-Ping of the library in China Trust Commercial Bank, Miss Chou Pei-Chun of the library in Chinfon Bank and Miss Chen Huei-Ling of the Accounting Department, Section Manager Chen of Human Resources of the United World Chinese Bank, for their generous supply of financial data.
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Appendices
Appendix A: Calculation steps of TOPSIS
Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) was proposed by Hwang and Yoon (1981). The purpose is to find a solution closest to the ‘positive ideal solution’ and farthest from the ‘negative ideal solution’. ‘Positive ideal solution’ refers to the most effective or least costly value among a set of feasible solutions. Conversely, a value of least effectiveness and highest cost would be the negative ideal solution. TOPSIS is used as the ranking method with GRA in the empirical study. The advantages of this method are that it is relatively simple and it yields a highly reliable preference order. The steps are as follows.
Step 1: Normalization of initial value: in this study, the application of GRA and TOPSIS uses vector normalization, which uses the ratio of the original value and the square root of the sum of the original indicator values. The formula is as follows:
where i is the i th bank, j is the j th financial ratio, r ij is the performance value of financial ratios after vector normalization for magnitude and direction, x ij is the original performance value of financial ratios and m is the number of bank companies.
Step 2: Find the positive ideal solution (A +) and negative ideal solution (A −):
Here, efficiency criteria imply a larger indicator value and a higher performance score: cost criteria imply a smaller indicator value and a higher performance score.
Step 3: Calculate the distance from each solution (bank) to the positive ideal solution (S i +) and to the negative ideal solution (S i −):
S i + is the shortest distance from the ideal solution (bank).
S i − is the farthest distance from the worst solution (bank).
Step 4: Calculate the proximity of each solution (bank) to the ideal solution (C i *): It is defined as
The step is to calculate the relative closeness to the ideal solution (C i *).
Step 5: Conduct the ranking among solutions (banks). Based on the value of C i * from Step 4, rank the performance among the solutions.
For TOPSIS, the chosen indicator should have the shortest distance from the ideal solution and the longest from the worst. The ideal solution is the one that enjoys the largest efficiency indicator and the smallest cost indicator among each of the substitutive bank companies. The worst solution is the one that enjoys the smallest efficiency indicator and the largest cost indicator among each of the substitutive bank companies.
Appendix B: The application of financial statement analysis
Following are the detailed introduction and application of five-power analysis (Tables B1, B2, B3, B4, B5 and B6) in the empirical study.
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Ho, CT. Measuring bank operations performance: an approach based on Grey Relation Analysis. J Oper Res Soc 57, 337–349 (2006). https://doi.org/10.1057/palgrave.jors.2601985
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DOI: https://doi.org/10.1057/palgrave.jors.2601985