基于规模的我国商业银行效率研究

发布时间：2018-02-08 21:03

本文关键词： 商业银行效率 DEA模型 Malmquist TFP指数　出处：《华东交通大学》2013年硕士论文　论文类型：学位论文

【摘要】：效率是银行竞争力的集中体现，较低的银行效率代表着较弱的竞争力，同时造成了巨大的社会浪费。所以我们应该提高我国商业银行的效率，结合我国的实际情况，对我国不同规模的商业银行进行效率研究，找出银行与银行之间存在的效率差异，再着手找出影响效率的因素，了解这些因素是以何种方式、何种程度影响着我国不同规模的商业银行，最后再有针对性的提出改进措施和解决方法。本文首先以我国上市银行资产规模和机构总数为划分依据，结合SPSS聚类分析将我国16家上市银行划分为大、中、小商业银行。再运用DEA方法，构造了MalmquistTFP指数模型对我国不同规模的商业银行2007-2012年的静态效率和动态效率变化进行度量，在此基础上构造了基于面板数据的计量经济模型并分别对我国不同规模商业银行效率影响因素进行分析。结果表明：在研究时期内，我国大、中、小型商业银行中，，中型商业银行的技术效率水平最高，小型商业银行的技术效率水平次之，大型商业银行技术效率水平最低。而Malmquist TFP生产率增长指数中型商业银行最高，大型商业银行次之，小型商业银行最低。影响因素方面，M2增长率、非利息收入占比、不良贷款率和总资产收益率都对我国商业银行具有确定性影响。资产市场占比和资本充足率方面，都对我国大型商业银行具有显著的负面影响，对我国小型商业银行具有显著的正面影响；成本收入比方面，对我国中型商业银行具有显著的正面影响，对我国小型商业银行具有显著地负面影响；流动性方面，对我国大型商业银行具有显著的负面影响，而对中、小商业银行不具有显著影响。本文共有六章。第一章为导论，主要介绍了本研究的背景与意义、国内外研究综述、主要内容方法与框架、创新与不足；第二章为商业银行效率度量的理论基础及模型，主要介绍了商业银行效率的相关概念、测度方法以及DEA和Malmquist TFP指数模型；第三章确定投入产出指标，运用DEA模型对我国不同规模的商业银行效率进行静态分析；第四章运用Malmquist TFP指数方法对我国不同规模的商业银行效率进行动态研究；第五章对影响我国不同规模商业银行效率水平的因素进行了差异分析，首先是详细介绍了哪些因素会对银行效率产生影响，然后，结合各个银行的技术效率值，通过构建面板数据模型分别对我国不同规模的商业银行进行回归分析，找到影响因素对其不同的影响方式和影响程度；第六章为结论，得出本文研究的主要结论和如何提升中国商业银行效率。
[Abstract]:Efficiency is the concentrated embodiment of bank competitiveness, low bank efficiency represents weak competitiveness, and at the same time it causes huge social waste. Therefore, we should improve the efficiency of commercial banks in our country, combining with the actual situation of our country. This paper studies the efficiency of commercial banks of different sizes in our country, finds out the differences of efficiency between banks and banks, and then starts to find out the factors that affect the efficiency, and to find out how these factors are used. To what extent does it affect the commercial banks of different sizes in our country, and finally puts forward the improvement measures and solutions. In this paper, based on the asset size and total number of institutions of listed banks in China, 16 listed banks in China are divided into large, medium and small commercial banks based on SPSS cluster analysis. Then the DEA method is used. MalmquistTFP index model is constructed to measure the changes of static and dynamic efficiency of commercial banks of different sizes in China from 2007 to 2012. On this basis, the econometric model based on panel data is constructed and the factors influencing the efficiency of commercial banks of different sizes in China are analyzed respectively. The results show that: in the period of research, large, medium and small commercial banks in China, The technical efficiency level of medium-sized commercial banks is the highest, that of small commercial banks is the second, and that of large commercial banks is the lowest. The Malmquist TFP productivity growth index is the highest for medium-sized commercial banks, followed by large commercial banks. Small commercial banks have the lowest. Influence factors such as M2 growth rate, non-interest income ratio, non-performing loan ratio and total asset return rate all have deterministic effects on Chinese commercial banks. Both have a significant negative impact on large commercial banks in China, have a significant positive impact on small commercial banks in China, and have a significant positive impact on medium-sized commercial banks in China in terms of cost-income ratio. It has a significant negative impact on the small commercial banks of our country, liquidity has a significant negative impact on the large commercial banks in China, but not on the small and medium-sized commercial banks. There are six chapters in this paper. The first chapter is the introduction, which mainly introduces the background and significance of this research, the domestic and foreign research review, the main content method and framework, innovation and insufficiency; the second chapter is the theoretical basis and model of efficiency measurement of commercial banks. This paper mainly introduces the related concepts of commercial bank efficiency, measurement method and DEA and Malmquist TFP index model, the third chapter determines the input-output index, and uses DEA model to analyze the efficiency of commercial banks of different scales in China. Chapter 4th uses Malmquist TFP index method to study the efficiency of commercial banks of different scales in China, Chapter 5th analyzes the factors affecting the efficiency level of commercial banks of different scales in China. First of all, it introduces in detail which factors will have an impact on the efficiency of banks. Then, combined with the technical efficiency of each bank, the panel data model is constructed to analyze the different scale commercial banks in China. Chapter 6th is the conclusion, the main conclusions of this paper and how to improve the efficiency of commercial banks in China.
【学位授予单位】：华东交通大学
【学位级别】：硕士
【学位授予年份】：2013
【分类号】：F832.33;F224;F272.5

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