互联网金融对中国商业银行绩效的影响（英文版）.pdf

Contents lists available at ScienceDirect International Review of Financial Analysis journal homepage: Impact of internet finance on the performance of commercial banks in China Jichang Dong a,b , Lijun Yin a,b , Xiaoting Liu a,b , Meiting Hu a,b , Xiuting Li a,b, , Lei Liu a,b a School of Economics and Management, University of Chinese Academy of Sciences, 100190 Beijing, China b Key Laboratory of Big Data Mining and Knowledge Management, Chinese Academy of Science, 100190 Beijing, China ARTICLE INFO JEL classification: G20 O33 Keywords: Internet finance Commercial banks Performance GMM Heterogeneity ABSTRACT The rapid development of Internet finance has certainly affected the operation of commercial banks. This paper investigates the impact of Internet finance on commercial banks. First, a theoretical influence mechanism of Internet finance on commercial banks is explored, and the Internet finance index and integrated performance index of commercial banks are constructed using factor analysis. Then, a static panel and a dynamic panel model are established to empirically examine the impact of Internet finance on the profitability, security, liquidity and growth as well as the comprehensive business performance of commercial banks. Finally, the heterogenous impacts of Internet finance on city commercial banks, joint-stock banks and state-owned commercial banks are discussed. The results show that the development of Internet finance has a positive impact on the profitability, security and growth of commercial banks, and has a negative impact on the liquidity of commercial banks. In addition, Internet finance has promoted the improvement of the comprehensive business performance of com- mercial banks. Moreover, the impact of Internet finance on different types of commercial banks is heterogeneous with the impact on state-owned commercial banks being the weakest and the impact on city commercial banks is the most significant. 1. Introduction In the past decade, the rapid development of technology has rapidly changed the way of financial services. In financial business, from digital currency to the application of blockchain, the financial world is rapidly innovating (Lucey, Vigne, Ballester, et al., 2018). Internet finance is a systematic combination of internet, technology and finance. In China, Internet finance has developed rapidly in recent years. Foreign scholars have also studied how to develop effective online advertising to im- prove the availability of online banking, such as providing clear user guide for customers (Alhassany Received in revised form 24 June 2020; Accepted 31 August 2020 Corresponding author at: Zhongguancun East Road 80, Haidian District, 100190 Beijing, China. E-mail addresses: , (X. Li). International Review of Financial Analysis 72 (2020) 101579 Available online 09 September 2020 1057-5219/ 2020 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (creativecommons/licenses/by-nc-nd/4.0/). Tcosts are reduced due to the development of Internet technology, which contributes to the expansion of the financial market and the re- construction of the financial system (Mishkin Srivastava, 2014; Wu, 2014). Furthermore, it has been argued that In- ternet finance promoted the performance of commercial banks because it forced the traditional commercial banks to innovate their business models and to improve their overall operational efficiency (DeYoung, Lang, Shen 2) Internet finance has improved the overall operational efficiency of commercial banks; 3) the impact of Internet finance on different types of commercial banks is hetero- geneous, with the weakest impact on state-owned commercial banks, strong influence on joint-stock commercial banks and the strongest impact on city commercial banks. The paper innovatively uses multi-source data to construct Internet finance index, which includes Internet search data and Internet fi- nancial transaction data. We construct Internet finance index based on the above mentioned two types of data and conduct robustness tests against each other. Most of the existing literature pay more attention to the impact of internet finance on a certain business of a bank. This paper studies the impact of internet finance on various businesses of commercial banks from a more comprehensive perspective, and ex- plores the impact mechanism from the four dimensions of profitability, liquidity, security and growth. We also conducted empirical tests on these four dimensions. Finally, considering the differences in the nature of banks, we empirically study the heterogeneity of the impact of Internet finance on different types of commercial banks. The remainder of this paper is organized as follows. In Section 2, a theoretical mechanism is explored based on existing research and six hypotheses are proposed. Then, the empirical methodology, the index construction and the variable selection are specified in Section 3. Section 4 introduces the data and descriptive statistics, and in Section 5 the empirical results are discussed, and the heterogeneity analysis is carried out. Section 6 concludes and presents policy implications. 2. Theoretical analysis The rapid development of Internet finance will partly affect char- acteristics of commercial banks including profitability, liquidity, se- curity and growth, and thus have an impact on the integrated perfor- mance of commercial banks. Based on economic theory and existing literature, this paper systematically analyzes how Internet finance af- fects commercial banks and six hypotheses are proposed as a con- sequence of this analysis. 2.1. Impact mechanism of internet finance on commercial banks 2.1.1. The impact of Internet finance on the profitability of commercial banks Commercial bank is a kind of financial institution that accepts de- posits, commercial loans and provides basic investment products (Moradi Internet fi- nance has cornered this neglected market (Liu at the same time, however, it has prompted commercial banks to evolve their approach to their customers, improve their service offering, introduce advanced technologies, optimize capital structure, reduce operating costs and promote overall profitability (Srivastava, 2014). As a result, hypothesis 1 can be proposed as follows: Hypothesis 1. Internet finance has a positive impact on the profitability of commercial banks. 2.1.2. The impact of Internet finance on the security of commercial banks Credit risk identification is more effective and accurate with the development of Internet technologies that utilize big data, cloud com- puting and artificial intelligence. As a result, commercial banks can assess and manage risk more effectively (Wu, 2015). In addition, In- ternet finance has reduced the information asymmetry between banks and borrowers, thereby contributing to bank risk management (Lapavitsas fixed effect re- gression (FE) and random effect regression (RE), which control in- dividual characteristics, will help to solve the heterogeneity problem. Firstly, pooled effect, random effect and fixed effect panel regression are used to estimate the model (1)(5). Considering that the operational performance of commercial banks has the characteristic of “stickiness”, we introduce the lag term of the explanatory variable into the model. The profitability, liquidity, security, growth, shareholder equity ratio and concentration of commercial banks may have a causal relationship with each other. Therefore, the empirical model may have an internal causal relationship. In this paper, the SYSGMM and DIFFGMM are used to estimate the model (1)(5). The model settings are as follows: = + + + + + = pi pi ifi control it it it j j jit i it 0 1 1 2 3 10 (1) = + + + + + = li li ifi control it it it j j jit i it 0 1 1 2 3 10 (2) = + + + + + = si si ifi control it it it j j jit i it 0 1 1 2 3 10 (3) = + + + + + = gi gi ifi control it it it j j jit i it 0 1 1 2 3 10 (4) = + + + + + + = bop bop gdp ifi control it it it it j j jit i it 0 1 1 2 1 3 4 5 (5) The explanatory variables of models (1)(4) are the commercial banks profitability index (pi), liquidity index (li), security index (si) and growth index (gi) respectively. The main explanatory variable is the Internet finance index (ifi). Control represents control variables in- cluding shareholder equity ratio (er), bank concentration (cr4), bank asset size (ta), bank listing (ipo) and macroeconomic level (gdp), which are general control variables. In addition, there are control variables including liquidity, security and growth in model (1); profitability, se- curity and growth in model (2); profitability, liquidity and growth in model (3); and profitability, liquidity and security in model (4). Considering that the operation of commercial banks has “dynamic stickiness”, the lag term of the dependent variables is added to each model. The explanatory variable of model (5) is commercial banks overall business performance level (bop). The main explanatory vari- able is the Internet finance index (ifi); the control variables are the proportion of shareholders equity (er), bank concentration (cr4), bank listing (ipo) and macroeconomic level (gdp). Since the viscous char- acteristics of macroeconomic variables affecting the performance of commercial banks have been documented, the model introduces a lag term for gdp. i represents the fixed effect of commercial banks and it is a random error term. i = 1, 2, 3 N, N represents 24 commercial bank samples, t = 1, 2, 3 T. 3.2. Variables 3.2.1. Dependent variables 3.2.1.1. Commercial bank profitability index. The proxy variables of commercial banks profitability include total return on assets, cost-to- income ratio and the net profit (Lee Xing, Sun, the industry level includes the con- centration of the banking industry; the bank level includes the share- holders equity ratio, the size of the banks assets, and the banks listing. 3.2.3.1. Shareholder equity ratio. The shareholder equity ratio is the ratio of shareholders equity to total assets, which reflects how much of the banks assets are invested by the owner. An extremely small equity ratio would indicate that the bank is over-indebted, which easily weakens the companys ability to withstand external shocks; an extremely large equity ratio means that the bank does not actively use financial leverage to expand its operations. 3.2.3.2. Banking concentration. At present, the mainstream methods for measuring the concentration of commercial banks include cr4, H value, the P-R method and the Lerner index. Considering the availability of data and the reality that Chinas large commercial banks have long dominated the market, we adopt cr4 which is the proportion of the assets of the top four banks as a measure of the market structure of the banking industry. 3.2.3.3. Bank asset size. The existing literature contains disagreement over the scale of bank assets and the performance of bank operations. On the one hand, some scholars assert that commercial banks with large asset scales have greater moral hazard, so the larger the bank size the higher the risk of bankruptcy. On the other hand, Jiang and Chen J. Dong, et al. International Review of Financial Analysis 72 (2020) 101579 52012) assert that the larger the bank, the more capable it is of diversifying risk through the diversification of assets, the more able it is to manage and control risks and the smaller its risk exposure. Halkos and Salamouris (2004) shows that the performance of commercial banks changes with the scale of its assets. When the scale of assets increases, so too does the operational efficiency and performance of commercial banks. 3.2.3.4. Bank listing. We consider banks that are publicly listed as dummy variables. We take 0 before listing and 1 after listing. 3.2.3.5. Macroeconomic level. Most scholars have found that when the economy develops well, commercial banks have a greater propensity to lend and, consequently, the interest income of banks increases. Nevertheless, they are also more likely to generate non-performing loans, which lead to increased risk exposure (Gray, 2012). We use the gdp to measure the macroeconomic level. 4. Data and descriptive statistics 4.1. Data This paper selects Chinas 24 A-share listed commercial banks as samples, including five large state-owned commercial banks, eight joint-stock commercial banks and 11 city commercial banks. The data is collected from the iFinD and Wind database and covers 2006 to 2018. Some of the missing values are supplemented with data from the annual reports of major banks and the China Financial Yearbook. The Internet finance index is constructed based on Baidu search engine data. The scale of Internet financial transactions comes from iResearch Consulting. 4.2. Descriptive statistics Considering the different dimensions of variables, we standardize the data before conducting empirical research, as shown in Table 2. Table 1 Relevant coefficient between internet financial keywords and commercial banks overall operating performance variables. Dimension Specific description Payment settlement dimension Third party payment (0.3347)* Internet payment (0.0434) Online payment (0.0780) Mobile payment (0.2866) Resource configuration dimension P2P (0.2604) Crowdfunding (0.0800) Network investment(0.4239)* Online loan (0.0056) Risk management dimension Internet finance (0.0345) Internet insurance (0.0645) Internet banking (0.1158) Online banking (0.7655)* Network channel dimension Online banking (0.7768)* Electronic Bank (0.6631)* Network bank (0.0050) Online bank (0.6763)* Note: *, * and * indicate levels of significance of 10%, 5% and 1%, respectively. -2 -1.5 -1 -0.5 0 0.5 1 1.5 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 Fig. 2. Trend of Internet Finance Index from 2006 to 2018. Table 2 Descriptive statistics of variables. Variable type Variable name Symbol Sample size Standard deviation Min Max Dependent variable Performance of Banks bop 312 0.3986 1.2563 1.4392 Profitability Index pi 312 0.6997 2.7222 2.6240 Liquidity Index li 312 0.5408 1.0121 1.9792 Security Index si 312 0.6227 3.0357 4.5696 Growth Index gi 312 0.7857 1.3047 4.1736 Explanatory variables Internet Finance Index ifi 312 0.7109 1.4311 0.9477 Scale of Internet Financial Transactions tt 312 0.9623 0.5478 2.7435 Control variable Shareholder Equity Ratio er 312 1.0000 4.0356 4.2963 Banking Concentration cr4 312 1.0000 1.2795 1.7877 Bank Total Asset ta 312 1.0000 0.6612 4.2418 Macroeconomic Level gdp 312 0.9623 1.4648 1.6787 J. Dong, et al. International Review of Financial Analysis 72 (2020) 101579 65. Empirical analysis 5.1. Correlation and stationarity test To avoid multicollinearity, a correlation analysis of explanatory variables is conducted. The coefficients are not statistically significant, indicating that there would be no severity multicollinearity problem in regression analysis. Further, C