本文關(guān)鍵詞：基于因子分析和超效率DEA的我國商業(yè)銀行效率研究　出處：《湖南大學(xué)》2014年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 商業(yè)銀行效率 因子分析 超效率DEA Tobit模型

【摘要】：自從我國對外資銀行開放以來，我國銀行業(yè)面臨的經(jīng)營環(huán)境發(fā)生了改變。除了外資銀行不斷涌入帶來的競爭壓力，我國商業(yè)銀行體系也在逐漸發(fā)生改變，中小銀行數(shù)量增加，政策性銀行也轉(zhuǎn)向市場化，加上許多影子銀行參與進(jìn)來，使得我國銀行業(yè)的競爭加劇。在這樣的宏觀環(huán)境下，我國商業(yè)銀行必須進(jìn)行改革和創(chuàng)新，不斷提高其效率，這樣才能在激烈的競爭中立于不敗之地。效率問題是經(jīng)濟(jì)學(xué)的核心命題之一，商業(yè)銀行效率問題研究也是學(xué)術(shù)界和銀行監(jiān)管部門關(guān)注的焦點問題。 傳統(tǒng)的DEA方法在計算商業(yè)銀行效率時不能較好地解決效率值均為1的銀行之間的比較，為此本文選擇了超效率DEA方法，則可以對效率前沿銀行進(jìn)行合理的比較。但運用這兩個模型時投入產(chǎn)出變量個數(shù)受到硬約束的限制，即樣本個數(shù)至少要大于投入、產(chǎn)出變量數(shù)量之和的兩倍。此外，投入和產(chǎn)出變量之間還要滿足相關(guān)性較強，，投入（產(chǎn)出）變量間要滿足相關(guān)性較弱的原則。針對指標(biāo)選取全面性和指標(biāo)個數(shù)太多的這種矛盾，本文利用因子分析的思想對超效率DEA方法進(jìn)行改進(jìn)，構(gòu)建出了因子分析-超效率DEA方法，然后運用該方法測算出我國16家商業(yè)銀行的效率。 計算出的效率值展現(xiàn)了各家商業(yè)銀行效率的高低，然而研究和分析影響商業(yè)銀行效率高低的各種因素更具有理論價值和現(xiàn)實意義。本文以2012年各個銀行的效率得分為因變量，從產(chǎn)權(quán)結(jié)構(gòu)、資產(chǎn)質(zhì)量、銀行規(guī)模、資產(chǎn)配置、人力資源和創(chuàng)新能力六個方面選取指標(biāo)作為自變量，運用Tobit模型對我國銀行效率的影響因素進(jìn)行分析。研究發(fā)現(xiàn)：我國商業(yè)銀行X效率整體上呈現(xiàn)上升態(tài)勢，而且近幾年大型國有銀行效率的提升幅度大于股份制商業(yè)銀行，但其效率值仍然低于股份制商業(yè)銀行；資產(chǎn)配置及創(chuàng)新能力與我國商業(yè)銀行的效率呈正相關(guān)關(guān)系，而產(chǎn)權(quán)結(jié)構(gòu)、銀行規(guī)模和資產(chǎn)質(zhì)量呈負(fù)相關(guān)關(guān)系，并且資產(chǎn)配置、銀行規(guī)模和產(chǎn)權(quán)結(jié)構(gòu)依次為最具影響力的三個因素。
[Abstract]:Since the opening of our country to foreign banks, the operating environment of our banking industry has changed. In addition to the competitive pressure brought by the continuous influx of foreign banks, the commercial banking system of our country is also gradually changing. The increase in the number of small and medium-sized banks, the policy banks have also turned to marketization, plus the participation of many shadow banks, which makes the competition of our banking industry intensified. In such a macro environment. The commercial banks of our country must carry on the reform and innovation, improve their efficiency constantly, so as to be in an invincible position in the fierce competition. Efficiency is one of the core propositions of economics. The efficiency of commercial banks is also the focus of academic and banking regulators. The traditional DEA method can not solve the problem of comparing the efficiency of commercial banks with efficiency value of 1. For this reason, this paper chooses the super-efficiency DEA method. But the number of input-output variables is restricted by hard constraints, that is, the number of samples is at least larger than the input. The number of output variables is twice as large. In addition, there is a strong correlation between input and output variables. The input-output variables should satisfy the principle of weak correlation. In view of the contradiction between the comprehensive selection of indicators and the number of indicators, this paper uses the idea of factor analysis to improve the super-efficiency DEA method. The factor analysis-super-efficiency DEA method is constructed, and then the efficiency of 16 commercial banks in China is calculated by using this method. The calculated efficiency shows the efficiency of commercial banks. However, it is of theoretical value and practical significance to study and analyze the factors that affect the efficiency of commercial banks. This paper takes the efficiency score of each bank in 2012 as a dependent variable, from the property structure, asset quality. The bank size, asset allocation, human resources and innovation ability are selected as independent variables. Tobit model is used to analyze the influencing factors of bank efficiency in China. The study finds that the X efficiency of commercial banks in our country is on the whole rising. In recent years, the efficiency of large state-owned banks is higher than that of joint-stock commercial banks, but its efficiency is still lower than that of joint-stock commercial banks. Asset allocation and innovation ability are positively correlated with the efficiency of commercial banks in China, while property structure, bank size and asset quality are negatively correlated, and asset allocation. Bank size and property structure are the three most influential factors.
【學(xué)位授予單位】：湖南大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：F224;F832.33

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