金控銀行經營績效影響因素之研究

A Study of Influential Factors in Bank Operating Performance of Financial Holding Companies

謝淑珍(Shu-Chen Hsieh)

指導教授 ： 古永嘉

國立臺北大學/商學院/國際財務金融碩士在職專班/碩士(2009年)

摘要

本文旨在探討影響金控銀行經營績效的關鍵因素，係以國內包括華銀、一銀、兆豐、永豐、玉山、日盛、台新、富邦、開發、元大、中信銀、國泰世華等十二家金控銀行為研究對象，針對其近五年共二十季（92年第4季至97年第3季）的財務資料來研究。為了建立經營績效之評估，採用每股盈餘（EPS）、總資產報酬率（ROA）與股東權益報酬率（ROE）三項獲利指標為應變數，並另選取二十六項財務比率為自變數；然後運用因素分析法、集群分析法、逐步廻歸分析、區別分析法、一般線性檢定法、Duncan's多重比較法等統計方法建立經營績效之評估模型。依各因子權重計算績效分數，將研究樣本分數在各營運特性不同之群組，並據以評估其過去經營績效優劣及評比; 檢測績效排名，並作為經營績效改善之參考。
透過命中率矩陣法來分析，實證結果顯示模型的誤差率為4.53%，命中率為95.47%，表示此模型之錯判力極低。利用因素分析可將二十六個財務比率萃取為五個營運特性指標，而此五個營運特性指標經由逐步區別分析，實證獲致其重要排名順序依序為：管理能力指標（F5）、資產品質指標（F4）、效率性指標（F2）、成長力指標（F3），以及資產規模組合指標（F1）；且其對應之權重分別為0.40、0.26、0.19、0.13，與0.01。另外，經由華德最小變異數法執行集群分析，結果顯示將銀行經營績效分為5組來驗證分群效果最佳；而群組在各營運指標之表現上，其優劣順序依序為群組2、群組1、群組3、群組4、群組5。
績效評估結果顯示季平均加權評分超過產業平均水平50分的有7家，依序為國泰世華、華銀、中信銀、兆豐銀、一銀、開發銀，以及富邦銀，而未達50分的則有5家，依序為玉山銀、台新銀、永豐銀、元大銀，以及日盛銀。
營運指標對經營績效之影響：透過營運指標之變動趨勢可窺知 (營運指標與樣本平均之差額)，即營運指標影響程度多寡。當 大於0時（即高於同業水平）， 值愈大係表示該項營運指標之正面影響相對較大；而若 小於0時（即低於同業水平）， 絕對值愈大則該項營運指標之負面影響相對較大。根據本研究結果，金控銀行經營績效之優劣，發現其勝負關鍵因素有三：1.管理能力指標（F5）：包含每人營業利益、稅後淨利成長率、利率敏感性缺口/淨值，2.資產品質指標（F4）：包含逾放比、淨利息收益，3.效率性指標（F2）：包含利息支出/存款、存借款平均利率等財務比率。

關鍵字

因素分析法、集群分析法、逐步廻

並列摘要

The main purpose in this study aims to investigate the key factors of influence on the managing performance for banks of the Financial holding Co. Ltd. Twelve banks were adopted as studying samples which include Hua Nan Bank, First Bank, Mega International Commercial Bank, Bank SinoPac, E. Sun Bank, Jih Sun International Bank, Taishin Bank, Taipei Fubon Bank, China Development Industrial Bank, Yuanta Bank, Chinatrust Commercial Bank, and Cathay United Bank. The data of financial list for analyzing contain 20 seasons for the past five years (from 4th quarter 2003 to 3rd quarter 2008). In order to set up the assessment of the managing performance of banks, three profit-making indicators, such as the Earning per Share (EPS), the Return of Assets (ROA) and the Return of Equity (ROE), were adopted as dependent variables. In the same time, there are 26 items of financial rates chosen as independent variables. Several statistical approaches, such as Factor Analysis, Cluster Analysis, Stepwise Regression Analysis, Discriminate Analysis, General Linear Method and Duncan's Multiple Range Test, were used to establish a model for assessing the managing performance of banks. The performance grades of sampled banks in each group with different operational characteristic were calculated by the weight of grade for each factor. According to this assessment, the good and bad managing performance of banks in the past five years can be clearly presented. Then the ranking of grades was proceeded to examine the rank of the bank on the managing performance, and it can be provided as the reference of the performance improvement.
Analyzing by the Matrix Law of the Hit Rate, the result reveals the error rate of the model performance is 4.53%, and the accurate rate of that is 95.47%. It implies that the ability of false judgment for this model is extremely low. According to 26 financial proportion analyses, they can be reduced to 5 operation characteristic indicators. The rank of the importance for these five operation characteristic indicators can be obtained by calculating with the method of the Stepwise Discriminant Analysis, then yields the results in order as follows: the Management Indicator (F5), the Asset Quality Indicator (F4), the Efficiency Position Indicator (F2), the Growing Position Indicator (F3), and the Asset Combination Indicator (F1). And the values of their corresponding weight are 0.40, 0.26, 0.19, 0.13 and 0.01 respectively. In addition, through performing the Cluster Analysis of those financial data with the Ward’s Minimum Variance Method, the result reveals that the best efficiency appeared as the cluster was divided into 5 clusters for managing performance of sample banks. And the rank of each cluster on the behavior corresponding to every operational indicator can be listed in order as follows: cluster 2, cluster 1, cluster 3, cluster, and the last cluster 5.
From results of performance assessment, it reveals that there are seven banks with the weighting grade of seasonal average exceeding industry average level which grade is 50 or so. Following their ranks, those can be listed as Cathay United Bank, Hua Nan Bank, Chinatrust Commercial Bank, Mega International Commercial Bank, First Bank, China Development Industrial Bank and Taipei Fubon Bank. While there are five banks with grades not reaching 50, also those can be listed in order as E. Sun Bank, Taishin Bank, Bank SinoPac, Yuanta Bank and Jih Sun International Bank.
In order to study the influence for managing performance of the bank by optional indicator, it can be investigated through observing the variational trend of the differential values of the operational indicators. Here differential values of the operational indicators are defined as the difference between the values of operational indicator and sample banks’ average. When is greater than 0, it is implied that is higher than the values of sample banks’ average. The larger value of shows the greater positive influence by the operational indicator. On the other hand, when is smaller than 0, it is implied that is lower than the values of sample banks’ average. The larger absolute value of shows the greater negative influence by the operational indicator. According to the results in this study, it is found that there are three key factors on the determinant of win and defeat for the managing performance to the banks of the Financial holding Co. Ltd.: the first is Management Indicator (F5) which contains Operation Income Per Employee, Net Income Growth, and the ratio of Interest Rate Sensitivity Gap and Net Value. The second is Asset Quality Indicator (F4) which contains Loan Ratio and Net Interest Income. The third is Efficiency Position Indicator (F2) which contains the ratio of Interest Expenses and Deposit, and Average Interest Rate of Deposit and Loan.

參考文獻

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