International Review of Financial Analysis 72 (2020) 101579
Contents lists available at ScienceDirect
International Review of Financial Analysis
journal homepage: www.elsevier.com/locate/irfa
Impact of internet finance on the performance of commercial banks in China
a,b
Jichang Dong
a
b
a,b
, Lijun Yin
, Xiaoting Liu
a,b
a,b
, Meiting Hu
a,b,⁎
, Xiuting Li
a,b
, Lei Liu
T
School of Economics and Management, University of Chinese Academy of Sciences, 100190 Beijing, China
Key Laboratory of Big Data Mining and Knowledge Management, Chinese Academy of Science, 100190 Beijing, China
ARTICLE INFO
ABSTRACT
JEL classification:
G20
O33
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.
Keywords:
Internet finance
Commercial banks
Performance
GMM
Heterogeneity
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 & Faisal, 2018). The online lending
market was mainly dominated by retail investors initially, and now an
important trend is that institutional investors, including commercial
banks and development banks, as well as non-financial institutions and
asset management companies, also gradually carry out online lending
business(Cummins, Mac an Bhaird, Rosati, & Lynn, 2020) From 2005 to
2013, the rapid development of third-party payment applications in­
dicated that Internet finance had entered an initial stage of develop­
ment. Since 2013, Internet finance has boomed. Data from iResearch(a
consulting firm focused on Chinese Internet finance) indicates that
third-party mobile payment transactions in China were 39 billion yuan
in 2009 and 190.5 trillion yuan in 2018, which was twice the GDP of
⁎
the same period. Various problems have arisen owing to the fast ex­
pansion of Internet finance. For instance, it is estimated that 2457 P2P
platforms defaulted in 2018, which has had an adverse impact on the
welfare of investors and the healthy development of the financial ser­
vices market.
The question is, whether the development of Internet finance has
had an impact on the profitability, liquidity, security, potential growth
and integrated performance of commercial banks in China? If so, how
great is this impact and is the impact positive or negative? In other
words, is Internet finance a “catalyst” for technological development or
a “flooding beast" which crowds the financial services market to the
detriment of commercial banks? Answering these questions is of great
significance to the Chinese government, who may seek to put in place
measures to regulate financial services market accordingly.
Existing research holds different views about the effect of Internet
finance on commercial banks. On the one hand, Internet finance has
intensified competition in the financial services market in China, af­
fecting the traditional business of commercial banks (Funk, 2019). Also,
it may impact on the banking system and operational models of banks
(Gonzalez & Loureiro, 2014). On the other hand, it cannot be denied
that Internet finance has also provided the impetus and opportunity for
commercial banks to reform. Information asymmetry and transaction
Corresponding author at: Zhongguancun East Road 80, Haidian District, 100190 Beijing, China.
E-mail addresses: lindaall@163.com, lixiuting@ucas.ac.cn (X. Li).
https://doi.org/10.1016/j.irfa.2020.101579
Received 11 October 2019; Received in revised form 24 June 2020; Accepted 31 August 2020
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
(http://creativecommons.org/licenses/by-nc-nd/4.0/).
International Review of Financial Analysis 72 (2020) 101579
J. Dong, et al.
costs 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 & Strahan, 1999;
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, & Nolle, 2007). However, the heterogeneity of different com­
mercial banks cannot be ignored since there are certain differences
among state-owned banks, joint-stock banks and city commercial banks
in terms of the fund size, management system and strategic positioning
(Liu, 2016; Shen & Guo, 2015).
This paper aims to theoretically and empirically investigate the
impact of Internet finance on commercial banks in China. First, we
theoretically analyze the impact of Internet finance on commercial
banks and an impact mechanism is constructed. Secondly, an Internet
finance index is constructed based on search engine Baidu's search
index and the factor analysis method to describe the dynamic devel­
opment of Internet finance. Additionally, indexes for profitability, se­
curity, liquidity, potential growth and integrated performance are de­
signed to trace the characteristics of commercial banks. Then, an
empirical analysis is carried out which explores the impact of Internet
finance on commercial banks by employing a static panel model and a
dynamic panel model. Robustness tests are then carried out by repla­
cing the Internet finance index with the sum of third-party, P2P and
crowdfunding transactions. Finally, this paper discusses the hetero­
geneity of Internet finance on city commercial banks, joint-stock banks
and state-owned commercial banks. The results reveal that: 1) the de­
velopment 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; 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.
2.1. 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 & Rafiei, 2019).There are three major sources of profit for
commercial banks: asset business, liability business and intermediary
business. For their asset business, commercial banks only deal with
high-value customers, such as large- and medium-sized enterprises or
individuals with good solvency. As a result, small- and micro-sized
enterprises as well as ordinary individuals are neglected; Internet fi­
nance has cornered this neglected market (Liu & Zhao, 2019). Further,
interest rates are generally much higher on loans from Internet finance
companies than from commercial banks. Consequently, Internet finance
does not put pressure on the asset business of commercial banks in
terms of customers or loan costs.
As for the liability business of commercial banks, deposits are the
basis for activities such as loans, investment and so on. It cannot be
denied that the development of Internet finance has impacted upon the
deposit business of commercial banks (Gomber, Kauffman, Parker, &
Weber, 2018). For example, online wealth management products such
as Yu'ebao have changed the ordinary customer's approach to invest­
ment. Customers have begun to transfer deposits from commercial
banks to Internet financial management platforms to enjoy higher
yields, which is problematic for the deposit business of commercial
banks. In response, commercial banks have developed their own online
wealth management products, leading to a slight improvement in the
deposit business of commercial banks.
Lastly, the development of third-party payment applications has
partly eroded the intermediary business of commercial banks. The re­
lationship between banks and customers has become less tight as a
result and customers have turned to third-party payment platforms for
online consumption, investment and wealth management, which has
inhibited the development of intermediary business for commercial
banks.
In summary, the development of Internet finance has undoubtably
impacted negatively on the profitability of commercial banks; 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 & Dos Santos, 2008). Lastly, Internet finance breaks the
information isolation of traditional commercial banks and improves
risk management efficiency (Jing, 2015). Therefore, we hypothesize
that:
2. Theoretical analysis
Hypothesis 2. Internet finance has a positive impact on the security of
commercial banks.
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.
2.1.3. The impact of Internet finance on the growth of commercial banks
The rapid development of Internet finance put pressure on com­
mercial banks but it also accelerated the rate at which commercial
banks changed, reformed and adopted technological improvements.
2
International Review of Financial Analysis 72 (2020) 101579
J. Dong, et al.
Internet finance was the catalyst for commercial banks to reform. For
example, commercial banks began to change their traditional practices
and pay more attention to technology and innovation. Additionally,
“long tail” customers have become the target of commercial banks and
this market is growing larger and larger (Feng, Gao, Peng, & Zhang,
2017). Furthermore, Internet finance has provided raw materials, such
as technology and various financial products, for commercial banks.
The reform and innovation of commercial banks is inseparable from the
influence of Internet finance; consequently, hypothesis 3 is proposed as
follows:
Third, the imitation effect. The technological capability and busi­
ness models of Internet finance companies provide benchmarks by
which commercial banks may set their targets for reform, again en­
couraging commercial banks to break with their traditional practices
and promote innovation. Thus, overall performance is improved by
learning from Internet finance and by innovating to compete.
These three effects lead us to Hypothesis 5:
Hypothesis 5. The development of Internet finance promotes the
comprehensive business performance of commercial banks.
Hypothesis 3. Internet finance has a positive impact on the growth of
commercial banks.
2.3. Heterogeneity analysis of the impact of internet finance on commercial
Banks' Comprehensive business performance
2.1.4. The impact of Internet finance on the liquidity of commercial banks
In the long run, the development of Internet finance has reduced the
liquidity of commercial banks by disrupting the stability of deposits and
accelerating the deterioration of asset quality (Hu, Ding, Li, Chen, &
Yang, 2019). Internet finance has undermined the stability of deposits
in several ways. Firstly, the deposits of residents and non-financial in­
stitutions have changed to non-financial institutions' interbank de­
posits. Secondly, money funds are becoming more active, which in­
tensifies the liquidity risk of debts. As a result, commercial banks have
to change their asset structures and reduce liquidity with the rapid
development of Internet finance. The deterioration of asset quality has
accelerated as commercial banks are forced to lend money to some
small and micro enterprises and individual customers with poor re­
payment ability. At the same time, they increase the amount of loan loss
reserves, and the liquidity is reduced as well. Therefore, we hypothesize
that:
In recent years, China has endeavored to accelerate financial system
reform. Various types of banks – such as state-owned commercial
banks, joint-stock commercial banks and city commercial bank –
compete and cooperate all the time. This paper explores the impact
mechanism of Internet finance on different types of commercial banks.
First, large state-owned commercial banks. These were established
earlier, have more assets and are more preferred by customers and
governments as compared with the other types of bank. However, there
are also a lot of defects of large state-owned commercial banks in­
cluding a lack of clear definition of property rights, the lengthy agency
chain, internal bureaucratic operation mechanisms and so on (Yao &
Jiang, 2011). Moreover, a lack of motivation to compete and innovate
caused higher costs and inefficient resource allocation. Therefore, we
assume that the impact of Internet finance on large state-owned com­
mercial banks is the weakest among these types of bank.
Second, as compared with large state-owned commercial banks,
joint-stock commercial banks and city commercial banks have less
preferential policies and lower market positions, but it is in virtue of
their self-financing operational models that a greater motivation to
compete with Internet finance has led to innovations in operating ef­
ficiency. Being less bureaucratic and hierarchical, and having more
flexible organizational structures and management methods, joint-stock
commercial banks and city commercial banks are more able to acquire
knowledge and technology (Hasan & Marton, 2003; Lyles & Salk, 1996).
Therefore, this paper supposes that the impact of Internet finance on
joint-stock commercial banks and city commercial banks is more sig­
nificant than that of state-owned commercial banks:
Hypothesis 4. Internet finance has a negative impact on the liquidity of
commercial banks.
2.2. Impact of internet finance on the comprehensive performance of
commercial banks
In recent years, the rapid development of Internet finance has partly
affected the traditional business of commercial banks, but it cannot be
denied that it also promoted the reform and development of commer­
cial banks. Based on the technology spill-over theory, companies with
advanced technology will spread their advanced technologies and ad­
vanced business models to other enterprises. Assuming this is so,
Internet finance would have a positive impact on the overall perfor­
mance of commercial banks. Specifically, Internet finance would im­
prove the performance of commercial banks through the following
three effects.
First, the competitive effect. The development of Internet finance
has increased competition in financial markets, prompting commercial
banks to improve their own operational efficiency. Internet finance has
expanded the scope of its business to encompass the functions of tra­
ditional commercial banks, affecting the deposit business, payment
business and other core businesses of commercial banks (Riedl, 2013).
The penetration of Internet finance will force commercial banks to
change their traditional approach and to innovate.
Second, the linkage effect. Support from commercial banks is ne­
cessary for Internet finance companies to operate. For example, Internet
finance companies have to share information with commercial banks,
commercial banks provide clearing and fund transfer services for
Internet finance companies, and so on. Cooperation offers the oppor­
tunity for commercial banks to absorb the advanced technology and
experience of Internet finance, thus further promoting the development
and reform of commercial banks. Diversification is the management
strategy adopted by most commercial banks at present. Investing in
different resources for different financial assets can effectively reduce
risks and thus improve efficiency(Olalere, Omar, & Kamil, 2017).
Hypothesis 6. The impact of Internet finance on different commercial
banks' comprehensive business performance is heterogeneous, with
little impact on large state-owned commercial banks, and greater
impact on joint-stock banks and city commercial banks.
On the whole, the impact of Internet finance on commercial banks
can be summarized as shown in Fig. 1. Internet finance provides the
impetus for commercial banks to reform. Specifically, advanced In­
ternet technology, abundant capital and big data lead to a reduction in
costs and an increase in innovation within commercial banks. There­
fore, the profitability of commercial banks is improved in the long run.
At the same time, Internet finance makes the risk management system
of commercial banks more intelligent and scientific, and security is
improved as well. Innovation is the source of development for com­
mercial banks. Internet finance has forced commercial banks to make
changes and innovations. However, the development of Internet finance
has also deteriorated the structure and quality of assets, and reduced
the liquidity of commercial banks. On the whole, Internet finance has
forced improves to the business performance of commercial banks
through competitive effects, linkage effects and imitation effects. Lastly,
the impact of Internet finance on the business performance of com­
mercial banks is heterogeneous. Specifically, the effect of Internet fi­
nance on city commercial banks is the most significant, followed by
joint-stock commercial banks, and large state-owned commercial banks
are the least affected.
3
International Review of Financial Analysis 72 (2020) 101579
J. Dong, et al.
Profitability
Loan
business
Growth
Intermediate
business
Deposit
business
Large state-owned
commercial banks
(weakest)
Business Innovation
Technology
Internet finance
Joint-stock
commercial banks
(weaker)
Data
Fund
Fundamental resources
Digitization
Intelligent
Scientific
Urban commercial
banks
(strongest)
Deposit stability
Deterioration of assets
Security
Liquidity
Security
Growth
Deposits, loans
and liquid assets
Liquidity
Profitability
Risk
Management
Competition
Linkage
Imitation
comprehensive business
performance
Fig. 1. The effect mechanism of Internet finance on commercial banks.
3. Model and variables
5
bopit =
0
+
1 bopit 1
1 piit 1
+
2 ifiit
+
j controljit
+ µi +
it
j=3
(1)
10
liit =
0
+
1 liit 1
+
2 ifiit
+
j controljit
+ µi +
it
(2)
j=3
0
+
1 si it 1
+
2 ifiit
+
j controljit
+ µi +
it
+ µi +
it
j=3
(3)
0
+
j controljit
+ µi +
it
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-toincome ratio and the net profit (Lee & Shin, 2018). In this paper, factor
analysis is used to compute the profitability indicators of commercial
10
giit =
+
3.2. Variables
10
siit =
3 ifiit
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. μi represents the fixed effect of commercial banks and εit is
a random error term. i = 1, 2, 3 … N, N represents 24 commercial bank
samples, t = 1, 2, 3 … T.
10
+
+
(5)
We need to consider the dynamic, heterogeneous and endogenous
characteristics of the econometric model when choosing an estimation
method. The structure of the sample data used in this paper has the
characteristics of “big N and small T". Pool regression ignoring sample
characteristics will lead to errors in empirical results; 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:
0
2 gdpit 1
j=4
3.1. Model
piit =
+
1 giit 1
+
2 ifiit
+
j controljit
j=3
(4)
4
International Review of Financial Analysis 72 (2020) 101579
J. Dong, et al.
banks. Specifically, principal component analysis is carried on the three
indicators of total return on assets, cost-to-income ratio and net profit of
commercial banks. In order to maintain the consistency of the meaning
of index size, the reciprocal of cost-to-income ratio is used.
online in 2006, the amount of data has gradually enriched, and the
search process has been more optimized. Baidu Index has become an
important tool for mining and analyzing Internet data. In 2019, Baidu
search accounted for 70.3%, Shenma search for 15.62%, Sogou search
for 4.74%, 360 search for 4.45%, Google search for 2.57% and Bing
search for 2.01%.
3.2.2.1.3. Screening keywords. The word frequency of the initial
keyword is first standardized and then the correlation coefficient
between the initial word frequency and the overall business
performance of the commercial bank is calculated. The results are
shown in Table 1. According to Larson and Farber (2000), the
correlation is non-weakly related if the correlation coefficient is
greater than 0.3; this leaves us with six keywords.
3.2.2.1.4. Integrate Internet finance index. Using the factor analysis
method, we reduce the dimensions of the six keywords and synthesize a
common factor as the Internet finance index to measure the
development of Internet finance. The results are shown in Table 1
and Fig. 2.
Internet financial transaction scale
As Internet finance gradually integrates into all functions of finance
and is adopted by more and more users, various innovative Internet
financial models have become a reality in China. Xu (2017) proposed
three main modes of Internet finance: Internet extension of traditional
financial services, financial Internet intermediation services and In­
ternet financial services. Internet finance comprises six major models:
third-party payment, P2P network loan platforms, big data finance,
crowdfunding, informationized financial institutions and Internet fi­
nancial portals in China (Deng, 2015). Considering data availability and
representativeness, we choose the third-party payment transaction
scale, P2P network loan transaction scale and crowdfunding transaction
scale to measure the scale of China's Internet financial transactions, in
order to represent the development of China's Internet finance.
3.2.1.2. Commercial bank liquidity index. The proxy variables of
commercial banks' liquidity mainly include loan-to-deposit ratio and
liquidity ratio. Similarly, this paper uses the factor analysis method to
synthesize the commercial bank liquidity index. The tested KMO value
is greater than 0.5 and the data is suitable for principal component
analysis.
3.2.1.3. Commercial bank security index. The indicators for measuring
the security of commercial banks mainly include non-performing loan
ratio, capital adequacy ratio and core capital adequacy ratio (Xu &
Chen, 2012). Rehman (2019) believe that bank capital adequacy ratio is
an important part of bank credit risk management and plays an
important role in reducing or eliminating credit risk. Altunbas,
Gambacorta, and Marques-Ibanez (2010) asserted that the expected
default rate is a suitable indicator by which to measure a bank's risk. As
there is no database of defaults in China, we are unable to obtain data
on the expected default rate of Chinese commercial banks. Therefore,
we use the factor analysis method to analyze the non-performing loan
ratio, capital adequacy ratio and core capital adequacy ratio of
commercial banks in order to synthesize the commercial bank
security index. The reciprocal of non-performing loan ratio is used.
3.2.1.4. Commercial bank growth index. The article analyzes the factors
of deposit growth rate, total asset growth rate and loan growth rate to
synthesize a commercial bank growth index.
3.2.1.5. Commercial bank integrated business performance index. The
purpose of constructing a commercial bank performance evaluation
system is to select reasonable performance evaluation indicators. Based
on the above principles, this paper selects 11 indicators according to the
four aspects of profitability, liquidity, security and growth, which are
used to evaluate the comprehensive performance of commercial banks.
After testing, the selected 11 indicators have greater correlation, the
KMO value is greater than 0.5 and the Bartlett test has a statistically
significant P value of less than 0.01. Therefore, the sample data can be
subjected to factor analysis. Factor analysis is used to reduce the
dimension and to construct the overall business performance index of
commercial banks.
3.2.3. Control variables
There are many factors that affect the operating performance of
commercial banks. Changes in the macro environment, adjustment of
the industry environment, intensified market competition, and the
strength of bank assets and liabilities will have a significant impact on
the operating performance of commercial banks (Klumpes, 2004; Xing,
Sun, & Yan, 2013). Therefore, this paper selects three levels of control
variables at the macro, industry, and bank levels: the macro level in­
cludes the macroeconomic level; the industry level includes the con­
centration of the banking industry; the bank level includes the share­
holders' equity ratio, the size of the bank's assets, and the bank's listing.
3.2.2. Explanatory variables
3.2.2.1. Internet finance index. The scientific construction of the
Internet finance index is an important prerequisite for empirically
testing the impact of Internet finance on commercial banks. This paper
draws on the research results of Shen and Guo (2015) and uses the
Baidu search index to build an Internet finance index to measure the
development of Internet finance.
3.2.2.1.1. Foundation lexicon. Drawing on the research results of
Shen and Guo (2015) and based on the financial function perspective,
16 keywords were selected from the payment settlement dimension,
resource allocation dimension, risk management dimension and
network channel dimension to establish the basic vocabulary.
Considering some changes in the development of Internet finance in
recent years, we have made some improvements to the original basic
vocabulary. Our 16 keywords are displayed in Table 1.
3.2.2.1.2. Keyword frequency from querying Baidu's search
engine. The annual word frequency of the initial keyword is queried
in Baidu's database as the basis of the Internet finance index. In existing
literature, scholars mostly use Google index as the web search results
for the description and analysis of related issues, while Baidu search
occupies half of the Internet search engine in China, so scholars mostly
use Baidu Index for problem analysis in China. Since Baidu index went
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 bank's assets are invested by the owner. An extremely small equity
ratio would indicate that the bank is over-indebted, which easily
weakens the company's 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 China's 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
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International Review of Financial Analysis 72 (2020) 101579
J. Dong, et al.
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
Risk management dimension
Network channel dimension
P2P (0.2604)
Internet finance (−0.0345)
Online banking (0.7768)***
Crowdfunding (−0.0800)
Internet insurance (−0.0645)
Electronic Bank (0.6631)***
Network investment(0.4239)*
Internet banking (0.1158)
Network bank (−0.0050)
Online loan (−0.0056)
Online banking (0.7655)***
Online bank (0.6763)**
Note: *, ** and *** indicate levels of significance of 10%, 5% and 1%, respectively.
1.5
1
0.5
0
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
-0.5
-1
-1.5
-2
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
Profitability Index
Liquidity Index
Security Index
Growth Index
Internet Finance Index
Scale of Internet Financial Transactions
Shareholder Equity Ratio
Banking Concentration
Bank Total Asset
Macroeconomic Level
bop
pi
li
si
gi
ifi
tt
er
cr4
ta
gdp
312
312
312
312
312
312
312
312
312
312
312
0.3986
0.6997
0.5408
0.6227
0.7857
0.7109
0.9623
1.0000
1.0000
1.0000
0.9623
−1.2563
−2.7222
−1.0121
−3.0357
−1.3047
−1.4311
−0.5478
−4.0356
−1.2795
−0.6612
−1.4648
1.4392
2.6240
1.9792
4.5696
4.1736
0.9477
2.7435
4.2963
1.7877
4.2418
1.6787
Explanatory variables
Control variable
(2012) 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.
4. Data and descriptive statistics
4.1. Data
This paper selects China's 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.
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.
4.2. Descriptive statistics
Considering the different dimensions of variables, we standardize
the data before conducting empirical research, as shown in Table 2.
6
International Review of Financial Analysis 72 (2020) 101579
J. Dong, et al.
Table 3
HT stationarity test for each variable.
Variables
Statistic
Z
P-value
bop
pi
li
si
gi
ifi
tt
ipo
er
cr4
gdp
0.5484
0.7024
0.6572
0.4634
0.0667
0.0000
0.0000
0.5962
0.5705
0.0000
0.0000
−5.3101
−1.8635
−2.8757
−7.2129
−16.0897
−17.5833
−17.5833
−4.2417
−4.8154
−17.5833
−17.5833
***
**
**
***
***
***
***
***
***
***
***
Table 5
Empirical test on the impact of internet finance on the profitability of com­
mercial banks.
Variables
POOL
FE
RE
DiffGMM
SYSGMM
ifi
0.3900⁎⁎⁎
(0.0702)
−0.0376
(0.0656)
0.1553⁎
(0.0806)
0.0773
(0.0491)
−0.1116
(0.1687)
−0.1317⁎⁎⁎
(0.0354)
0.2877⁎⁎⁎
(0.0550)
0.0741
(0.1782)
0.1289⁎⁎⁎
(0.0368)
0.4363⁎⁎⁎
(0.0599)
−0.2009⁎
(0.1135)
−0.1455⁎
(0.0822)
0.0236
(0.0465)
−0.1194
(0.1513)
−0.0529
(0.0497)
0.3563⁎⁎⁎
(0.0563)
0.2136
(0.1517)
0.4379⁎⁎⁎
(0.0805)
0.4096⁎⁎⁎
(0.0603)
−0.1217
(0.0906)
−0.0398
(0.0792)
0.0628
(0.0452)
−0.0894
(0.1489)
−0.1203⁎⁎⁎
(0.0420)
0.3387⁎⁎⁎
(0.0552)
0.1376
(0.1528)
0.2236⁎⁎⁎
(0.0534)
0.4189
0.4156
***
0.3078
0.1141⁎⁎⁎
(0.0230)
0.0316
(0.0526)
0.1481⁎⁎⁎
(0.0492)
−0.1786⁎⁎⁎
(0.0132)
0.0774
(0.0853)
0.0990⁎⁎⁎
(0.0253)
−0.0109
(0.0485)
0.2818⁎⁎⁎
(0.0533)
−0.0580
(0.1879)
0.6424⁎⁎⁎
(0.0392)
0.1290⁎⁎⁎
(0.0232)
−0.0675
(0.0666)
0.1514⁎⁎⁎
(0.0539)
−0.1617⁎⁎⁎
(0.0160)
0.1489
(0.2501)
0.0898⁎⁎⁎
(0.0257)
−0.0058
(0.0816)
0.3761⁎⁎
(0.1823)
0.1727
(0.1639)
0.6058⁎⁎⁎
(0.0353)
0.2334
0.2499
0.9457
0.9988
li
si
gi
gdp
ipo
er
5. Empirical analysis
cr4
5.1. Correlation and stationarity test
ta
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, Considering the data with big N and small
T, we take HT stationarity tests to the datain order to avoid spurious
regression. The results in Table 3 show that all of the variables's P-value
are significant, so we can reject the null hypothesis that the panel has a
unit root. The panel data is stationary.
L.pi
r2
F-test
LM-test
Hausmantest
AR(2) Pvalue
Sargan-test
P-value
5.2. Empirical results
5.2.1. Empirical test on the impact of Internet finance on profitability,
liquidity, security and growth of commercial banks
To investigate the impact of Internet finance on the profitability,
liquidity, security and growth of commercial banks, we use the pooled
OLS (POOL), the fixed effect model (FE) and the random effect model
(RE) to estimate Eqs. (1)–(5). The optimal static panel regression model
is selected according to F test, LM test and Hausman test. Considering
the endogeneity caused by the possible existence of the simultaneous
relationship among explanatory variables, we perform the GMM esti­
mation for the sample data, and the abond test is performed on the
disturbance items of the differential GMM and the system GMM and the
sargan test is made on the instrument variables. GMM is a common
empirical method in financial econometrics. Shahzad, Fareed, and
Zulfiqar (2019) used GMM method to empirically test the impact of
loan growth on non-performing loans and solvency, taking the bank
data of Turkey as an example. The empirical results are illustrated in
Tables 4–11.
Table 4 shows that the F test rejects the pooled estimate at a sig­
nificant level of 1% and the LM test significantly rejects the null hy­
pothesis that “there is no individual random effect", thus the random
effect model is selected rather than the pooled OLS. Further, the
Hausman test indicates that the fixed effect model is prior to the
random effect model. We choose the fixed effect model to estimate
equations. Considering the simultaneous relationship among variables,
in order to increase the robustness of the regression results, we use the
differential GMM and the system GMM for regression analysis. By
performing the abond test, the regression results are not significant.
***
***
Therefore, the original hypothesis that “the disturbance term has no
autocorrelation” can be accepted and there is no second-order auto­
correlation. By performing the sargan test, it cannot reject the null
hypothesis that “all instrument variables are valid” at the 5% level,
implying that the choice of instrumental variables is reasonable.
The results in Table 5 show that the Internet finance index (ifi)
coefficients of the static panel fixed effect regression model and the
dynamic panel regression model are significantly positive, indicating
that the development of Internet finance has a significantly positive
impact on the profitability of China's commercial banks. Internet fi­
nance diverted the deposit business of commercial banks via its ad­
vantages of channel, information, customer and capital; it broke the
monopolistic competition pattern of commercial banks to a certain
extent, pushed up the capital cost of commercial banks and reduced
their profitability to a certain extent. However, the emergence of In­
ternet finance has gradually introduced advanced technologies and
business ideas into commercial banks through imitation effects, com­
petitive effects, linkage effects and talent flow effects. The empirical
study of Mo (2014) demonstrated that the financial business practices
carried out by Internet finance companies has gradually expanded to
traditional commercial banks. The rapid development and penetration
of Internet companies in the financial industry will inevitably lead
commercial banks to change their traditional business ideas, promote
innovation, upgrade their technology and improve business perfor­
mance. The operating cost of bank can be reduced from 60% to
15%–20% by moving from offline to online (Thakur & Srivastava,
Table 4
Static panel model selection for profitability.
Test method
Results
F-test
F(23,279) = 7.17; P > F = 0.0000
FER. Model is prior to Pooled OLS
LM-test
Hausman-test
chi2(1) = 120.11; P > chi2 = 0.0000
chi2(9) = 30.75; P > chi2 = 0.0003
RER.Model is prior to Pooled OLS
FER. Model is prior to RER Model
7
International Review of Financial Analysis 72 (2020) 101579
J. Dong, et al.
Table 6
Static panel model selection for liquidity.
Test method
Results
F-test
F(23,279) = 41.8; P > F = 0.0000
FER. Model is prior to Pooled OLS
LM-test
Hausman-test
chi2(1) = 790.90; P > chi2 = 0.0000
chi2(9) = 4.03; P > chi2 = 0.9094
RER. Model is prior to Pooled OLS
RER. Model is prior to FER Model
regression model is the fixed effect model. There is no second-order
autocorrelation in the dynamic panel GMM model and the instrumental
variables passed the validity over-identification test.
The regression results in Table 9 show that the Internet finance
index coefficients of the static panel fixed effect model and dynamic
panel GMM model are both significantly positive, indicating that In­
ternet finance has a significantly positive impact on the security of
commercial banks. The security of commercial banks is mainly reflected
in their risk-taking. The empirical result of Liu (2016) shows that the
development of Internet finance has reduced the risk-taking of com­
mercial banks. The rapid development of Internet finance has effec­
tively improved the risk management reform of commercial banks,
making up for the adverse impacts of risks and reducing the bankruptcy
risk of commercial banks, which in turn promotes the stability of the
entire financial system. Internet finance was also able to change the
asset-liability structure of commercial banks by affecting their business
models and business scale, which in turn affects the banks' cost-benefit
ratio and ultimately improve the banks' risk (Gai, Qiu, & Sun, 2018).
The test results in Table 10 suggest that the optimal static panel
regression model is the random effect model. There is no second-order
autocorrelation in the dynamic panel GMM model and the instrumental
variables passed the validity over-identification test.
It can be seen from Table 11 that the coefficients of the Internet
finance index and other variables are insignificant, indicating that the
explanatory variables and error term are not independent and, fur­
thermore, that Eq. (4) is endogenous. Thus, we performed the dynamic
GMM model to estimate the equation. The results show that the coef­
ficient of the Internet finance index is significantly positive and that the
other explanatory variables are also ideally fitted, implying that the
development of Internet finance has promoted the long-term develop­
ment of commercial banks. Commercial banks can use the Internet fi­
nancial model to provide loans to small- and micro-enterprises, thereby
reducing the company's operating costs (Hu et al., 2019). Furthermore,
financial technology will lead to an overhaul of the traditional banking
business model, forcing banks to upgrade and transform. Although In­
ternet finance has an impact on the development of commercial banks,
it can also provide the impetus and opportunity for the reform of
commercial banks in the long run.
Table 7
Empirical test on the impact of internet finance on the liquidity of commercial
banks.
Variables
POOL
FE
ifi
−0.0368
(0.0646)
−0.0289
(0.0504)
−0.3558⁎⁎⁎
(0.0680)
−0.0923⁎⁎
(0.0429)
0.4362⁎⁎⁎
(0.1458)
−0.0075
(0.0318)
0.2794⁎⁎⁎
(0.0477)
0.3590⁎⁎
(0.1549)
−0.0721⁎⁎
(0.0327)
−0.0590
(0.0341)
−0.0553⁎
(0.0312)
−0.0610
(0.0432)
0.0245
(0.0243)
0.4886⁎⁎⁎
(0.0738)
−0.0883⁎⁎⁎
(0.0256)
0.1330⁎⁎⁎
(0.0306)
0.3467⁎⁎⁎
(0.0771)
0.1342⁎⁎⁎
(0.0437)
−0.0609
(0.0342)
−0.0524⁎
(0.0312)
−0.0698
(0.0430)
0.0233
(0.0243)
0.4931⁎⁎⁎
(0.0744)
−0.0896⁎⁎⁎
(0.0248)
0.1387⁎⁎⁎
(0.0307)
0.3438⁎⁎⁎
(0.0780)
0.1069⁎⁎⁎
(0.0396)
0.2525
0.4795
***
0.4785
pi
si
gi
gdp
ipo
er
cr4
ta
L.li
r2
F-test
LM-test
Hausman-test
AR(2)-P value
Sargan-test Pvalue
RE
⁎
⁎
***
non-sig
DIFFGMM
SYSGMM
0.0273
(0.0191)
−0.0022
(0.0291)
−0.0204
(0.0271)
0.0146
(0.0116)
0.4723⁎⁎⁎
(0.0591)
−0.0242
(0.0208)
0.0380
(0.0360)
0.3555⁎⁎⁎
(0.0571)
0.0933⁎
(0.0505)
0.2469⁎
(0.1493)
0.0325
(0.0240)
−0.0301
(0.0311)
−0.0887
(0.0347)
−0.0009
(0.0483)
0.4499⁎⁎⁎
(0.0935)
−0.0390
(0.0248)
0.0766⁎
(0.0452)
0.3986⁎⁎⁎
(0.0952)
0.2383
(0.2088)
0.4542⁎
(0.2350)
0.4285
0.2154
0.7365
1.0000
2014). The reduction in operating costs will greatly increase the prof­
itability of commercial banks.
The test results in Table 6 suggest that the random effect model is
the optimal model. There is no second-order autocorrelation in the
dynamic panel GMM model and the instrumental variables also passed
the validity of the over-identification test.
The regression results from Table 7 show that the Internet finance
index coefficient of the static panel random effect model is significantly
negative, while the Internet finance index coefficient of the dynamic
panel GMM model is insignificantly positive, and the significance of
other control variables is weak. Therefore, we use the regression results
of the static panel random effects model to explain the negative impact
of Internet finance on the liquidity of commercial banks.
Deposit is the main business by which commercial banks obtain loan
capital, but the recent adoption of Internet finance has diverted deposits
away from commercial banks. Due to its technological advantages,
Internet finance can provide customers with higher deposit interest
than commercial banks (Hou, Gao, & Wang, 2016). Yao, Di, Zheng, and
Xu (2018) claims that third-party payment platforms have a large im­
pact on the potential customers, basic payment functions and liabilitybased businesses (which mainly consist of deposits) of commercial
banks. In recent years, the growth rate of deposits of commercial banks
has gradually declined, which indicates that athe commercial liabilities
of commercial banks are indeed affected by the emergence of Internet
finance, especially the rapid development of third-party payments,
which has weakened the deposits of commercial banks to a certain
extent and reduced the liquidity of commercial banks.
The test results in Table 8 suggest that the optimal static panel
5.2.2. Empirical test on the impact of Internet finance on the integrated
business performance of commercial banks
From the test results in Table 12, we conclude that the random ef­
fect model is the optimal static panel regression model. There is no
second-order autocorrelation in the dynamic panel GMM model and the
instrumental variables also passed the validity over-identification test.
As Table 13 shows, the coefficients of the static panel and dynamic
panel regression model are significantly positive. The difference be­
tween the Internet finance index coefficients of the differential GMM
and the system GMM is not significant whereas the coefficients of other
control variables are significant, which implies that the development of
Internet finance has promoted the integrated business performance of
commercial banks. This result is in line with a study by Arnold and
Ewijk (2011), which demonstrated that Internet finance has a certain
impact on the core business of commercial banks. The development of
Internet finance may affect traditional formal and informal financial
models, thus threatening the survival of commercial banks (Funk,
8
International Review of Financial Analysis 72 (2020) 101579
J. Dong, et al.
Table 8
Static panel model selection for security.
Test method
Results
F-test
F(23,279) = 6.42; P > F = 0.0000
FER. Model is prior to Pooled OLS
LM-test
Hausman-test
chi2(1) = 71.88; P > chi2 = 0.0000
chi2(9) = 92.08; P > chi2 = 0.0000
RER. Model is prior to Pooled OLS
FER. Model is prior to RER Model
Table 9
Empirical test on the impact of internet finance on the security of commercial
banks.
Variables
POOL
FE
ifi
0.0361
(0.0523)
−0.2334⁎⁎⁎
(0.0446)
0.0783⁎
(0.0406)
0.0629⁎
(0.0348)
0.1072
(0.1197)
−0.0353
(0.0257)
0.5018⁎⁎⁎
(0.0288)
0.2007
(0.1260)
0.0261
(0.0266)
0.1427
(0.0465)
−0.1163
(0.0824)
−0.0763⁎
(0.0431)
−0.0482
(0.0335)
−0.0960
(0.1095)
0.0719⁎⁎
(0.0358)
0.5120⁎⁎⁎
(0.0310)
0.2110⁎
(0.1095)
0.2505⁎⁎⁎
(0.0595)
0.0823
(0.0474)
−0.1883⁎⁎⁎
(0.0606)
−0.0044
(0.0417)
0.0091
(0.0332)
0.0273
(0.1094)
−0.0088
(0.0298)
0.5241⁎⁎⁎
(0.0298)
0.1955⁎
(0.1126)
0.0647⁎
(0.0359)
0.6301
0.5988
***
0.5760
li
pi
gi
gdp
ipo
er
cr4
ta
L.si
r2
F-test
LM-test
Hausmantest
AR(2)-P
value
Sargan-test
P-value
RE
⁎⁎⁎
⁎
Table 11
Empirical test on the impact of internet finance on the growth of commercial
banks.
DIFFGMM
SYSGMM
Variables
POOL
FE
RE
DIFFGMM
SYSGMM
0.1200
(0.0214)
0.0633
(0.0441)
−0.0578⁎⁎
(0.0256)
−0.0358⁎⁎⁎
(0.0130)
−0.2531⁎⁎⁎
(0.0848)
0.1151⁎⁎⁎
(0.0237)
0.3827⁎⁎⁎
(0.0395)
0.1516⁎⁎⁎
(0.0508)
0.5070⁎
(0.2883)
0.2012⁎⁎⁎
(0.0343)
0.1382
(0.0275)
0.0744
(0.0486)
−0.0424
(0.0369)
−0.0247⁎
(0.0137)
−0.1226⁎⁎
(0.0581)
0.0746⁎⁎⁎
(0.0237)
0.4020⁎⁎⁎
(0.0359)
0.1298⁎⁎⁎
(0.0445)
0.1398
(0.0874)
0.2041⁎⁎⁎
(0.0395)
ifi
−0.0387
(0.0861)
−0.1638⁎⁎
(0.0761)
0.1702⁎
(0.0941)
0.1054
(0.0669)
−0.4213⁎⁎
(0.1956)
0.1468⁎⁎⁎
(0.0415)
−0.1267⁎
(0.0667)
−0.1344
(0.2080)
−0.2900⁎⁎⁎
(0.0406)
0.0892
(0.0840)
0.1481
(0.1468)
−0.1525
(0.1061)
0.0392
(0.0771)
−0.7142⁎⁎⁎
(0.1904)
0.2757⁎⁎⁎
(0.0621)
−0.0117
(0.0776)
−0.1043
(0.1960)
0.1808⁎
(0.1086)
−0.0257
(0.0837)
−0.0919
(0.0935)
0.0832
(0.0980)
0.1138
(0.0704)
−0.4736⁎⁎
(0.1896)
0.1469⁎⁎⁎
(0.0473)
−0.0902
(0.0714)
−0.1419
(0.1999)
−0.2348⁎⁎⁎
(0.0518)
0.3715
0.3682
***
0.3172
0.1257
(0.0446)
0.4090⁎⁎⁎
(0.0935)
−0.2819⁎⁎⁎
(0.0904)
−0.4611⁎⁎⁎
(0.0701)
−0.6468⁎⁎⁎
(0.0673)
0.1861⁎⁎⁎
(0.0431)
−0.3281⁎⁎⁎
(0.0750)
0.0772
(0.0836)
0.7053⁎⁎
(0.3019)
0.0998⁎⁎⁎
(0.0296)
−0.0048
(0.0376)
0.0013
(0.0995)
−0.0652
(0.0918)
−0.2613⁎⁎⁎
(0.0685)
−0.0343
(0.0985)
−0.0449
(0.0657)
−0.4855⁎⁎⁎
(0.0924)
0.2924⁎⁎
(0.1205)
0.3318
(0.2590)
0.1925⁎⁎⁎
(0.0218)
0.5982
0.6762
0.8955
0.9967
⁎⁎⁎
⁎⁎⁎
li
si
pi
gdp
ipo
er
cr4
ta
L.gi
r2
F-test
LM-test
Hausmantest
AR(2)-P
value
Sargantest Pvalue
***
***
0.4583
0.5131
0.9597
0.9998
2019). However, tremendous challenges are accompanied by opportu­
nities. The development of Internet finance has created an opportunity
for the reform of China's financial system. The rapid development of
Internet finance has effectively promoted the reform of global financial
services and promoted the realization of financial disintermediation
(Franklin, McAndrews, & Strahan, 2002). Internet finance has pro­
moted innovation and the reform of traditional financial services by
applying e-finance to traditional financial services (Chen & Zhen,
2011), thus it has been conducive to the development of traditional
commercial banks. In addition, Internet finance affects the development
of the bank deposit business by influencing the risk assessment methods
of commercial banks, which changes the market rules to some extent
(Qiao, Chen, & Xia, 2018). On the whole, the development of Internet
finance, especially the rapid development of third-party payment ser­
vices, affects the personal deposit business of commercial banks to a
certain extent. Although the development of Internet finance reduces
the liquidity of commercial banks, it positively affects their
⁎⁎⁎
***
non-sig
profitability, security and growth. Moreover, it is conductive to the
comprehensive improvement of the performance of commercial banks.
5.2.3. Empirical test of the heterogeneity of the impact of Internet finance on
commercial banks' comprehensive business performance
Based on the heterogeneity of commercial banks, we divide the
sample into large state-owned commercial banks, joint-stock banks and
city commercial banks. Considering the number of degrees of freedom
that entail from this number of sample and variables, we design three
subsamples for dynamic regression estimation: subsample 1 includes
joint-stock commercial banks and city commercial banks, but excludes
state-owned commercial banks; subsample 2 includes large state-owned
commercial banks and city commercial banks, but excludes joint-stock
commercial banks; and subsample 3 includes large state-owned com­
mercial banks and joint-stock commercial banks, but excludes city
commercial banks. The regression results of the three subsamples are
Table 10
Static panel model selection for growth.
Test method
Results
F-test
F(23,279) = 3.73; P > F = 0.0000
FER. Model is prior to Pooled OLS
LM-test
Hausman-test
chi2(1) = 10.05; P > chi2 = 0.0008
chi2(9) = 0.57; P > chi2 = 0.9999
RER. Model is prior to Pooled OLS
RER. Model is prior to FER Model
9
International Review of Financial Analysis 72 (2020) 101579
J. Dong, et al.
Table 12
Static panel model selection for integrated business performance.
Test method
Results
F-test
F(23,283) = 7.98; P > F = 0.0000
FER. Model is prior to Polled OLS
LM-test
Hausman-test
chi2(1) = 205.02; P > chi2 = 0.0000
chi2(5) = 2.21; P > chi2 = 0.8187
RER.Model is prior to Pooled OLS
RER. Model is prior to FER Model
1, subsample 2 and subsample 3 are 0.1187, 0.0820 and 0.0951, re­
spectively, and the transformation ratios are 31.27%, −9.29%
and − 21.01%, respectively. Sub-sample 1 excludes large state-owned
commercial banks, and the coefficient of the Internet Finance Index has
increased significantly, indicating that Internet finance has a greater
impact on joint-stock banks and city commercial banks than large stateowned commercial banks. Sub-sample 3 excludes city commercial
banks, the regression coefficient dropped the most, indicating that In­
ternet finance has a greater impact on city commercial banks. This
suggests that Hypothesis 6 is verified – that is, the impact of Internet
finance on commercial banks is heterogeneous. Specifically, Internet
finance has the greatest impact on city commercial banks followed by
joint-stock banks while large state-owned commercial banks are the
least affected by Internet finance. City commercial banks are generally
small in scale and possess a more flexible operational system compared
with state-owned commercial banks. When they are impacted by In­
ternet finance, they can quickly change their business strategies.
Therefore, the impact of Internet finance is also the largest; the bu­
reaucracy of large state-owned commercial banks is more prominent.
The lack of flexibility in the personnel management system will hinder
the spillover effect of Internet finance in some extent, so Internet fi­
nance has a weaker impact on large state-owned commercial banks.
Table 13
Empirical test on the impact of internet finance on the integrated business
performance of commercial banks.
Variables
POOL
ifi
0.2596
(0.0439)
−0.0154
(0.0208)
0.1340⁎⁎⁎
(0.0213)
0.0759
(0.1107)
0.2698
(0.0357)
0.0454
(0.0279)
0.1125⁎⁎⁎
(0.0229)
0.0915
(0.0899)
0.2669
(0.0357)
0.0281
(0.0253)
0.1186⁎⁎⁎
(0.0218)
0.0870
(0.0898)
0.2832
0.3503
***
0.3494
ipo
er
cr4
L.bop
FE
⁎⁎⁎
RE
⁎⁎⁎
⁎⁎⁎
L.gdp
r2
F-test
LM-test
Hausman-test
AR(2)-P value
Sargan-test P-value
***
non-sig
SYSGMM
DIFFGMM
0.0904
(0.0141)
0.0591⁎⁎⁎
(0.0211)
−0.0065
(0.0162)
0.3621⁎⁎⁎
(0.0373)
0.5016⁎⁎⁎
(0.0517)
0.9421⁎⁎⁎
(0.1340)
0.1204⁎⁎⁎
(0.0135)
0.1509⁎⁎⁎
(0.0148)
−0.0021
(0.0163)
0.3930⁎⁎⁎
(0.0419)
0.5216⁎⁎⁎
(0.0479)
1.0637⁎⁎⁎
(0.1632)
0.0722
1.0000
0.0733
0.9740
⁎⁎⁎
then compared. If the coefficient of the core explanatory variables in­
creases in the subsample regression, it indicates that the commercial
bank excluded from that subsample has less response to Internet fi­
nance; the larger the regression coefficient, the smaller the response of
the excluded commercial bank to Internet finance. If the coefficient of
the core explanatory variables in the subsample regression is reduced,
the impact of Internet finance on the excluded commercial bank will be
greater. Finally, we test the significance of the difference between the
estimated coefficients of the subsamples according to Acquaah's (2012)
inter-group difference t-test method and Bootstrap's empirical P-value.
As shown in Table 14, there is no second-order autocorrelation of
the dynamic regression models of the three subsamples at the 5% sig­
nificance level and all the instrumental variables were selected rea­
sonably. The coefficients of the core explanatory variables of subsample
5.3. Robustness checks
To ensure the reliability of our conclusion, this subsection provides
a robustness test to check our empirical results. Internet finance in
China includes these six major models: third-party payments, P2P on­
line loan platforms, big data, crowdfunding, informatization financial
institutes and the Internet financial portals (Deng, 2015). from an in­
terrogation of several sources, third-party payments, P2P loans and
crowdfunding are the models that have developed the fastest and which
have the most accessible data. So, we use the sum of third-party pay­
ments, P2P loans and crowdfunding as a substitute for the Internet fi­
nance index in order to make our robustness test. There is no significant
difference between the major conclusions, as Tables 15, 16 and
17shows. -7.7%, -14.56% and -44.34% represent changes in the coef­
ficients. The more the coefficient goes down, the impact of Internet
finance on the excluded commercial bank will be greater.
Table 14
Heterogeneity test of the impact of internet finance on the integrated business
performance of commercial banks.
Variables
Subsample 1
Subsample 2
Subsample 3
L.bop
0.4743⁎⁎⁎
(0.0690)
−0.8851⁎⁎⁎
(0.2426)
1.1192⁎⁎⁎
(0.2951)
0.1187⁎⁎⁎
(0.0321)
+31.27%
0.0798⁎⁎
(0.0325)
−0.0099
(0.0351)
0.4230⁎⁎⁎
(0.0722)
0.0985
1.0000
0.6428⁎⁎⁎
(0.0462)
−1.0645⁎⁎⁎
(0.2109)
1.3690⁎⁎⁎
(0.2425)
0.0820⁎⁎
(0.0368)
−9.29%
0.0371⁎
(0.0212)
−0.0367⁎⁎
(0.0175)
0.4204⁎⁎⁎
(0.0753)
0.4212
1.0000
0.4007⁎⁎⁎
(0.0975)
−0.6158⁎⁎
(0.2516)
0.8708⁎⁎
(0.4026)
0.0951⁎⁎⁎
(0.0282)
−21.01%
0.1882⁎⁎
(0.0748)
−0.2021⁎⁎
(0.0995)
0.2405⁎⁎
(0.0941)
0.0529
1.0000
gdp
L.gdp
ifi
ipo
er
cr4
AR(2)-P value
Sargan-test P-value
6. Conclusion and policy implications
The development of Internet finance has challenged the traditional
models of financial institutions, forcing banks and other financial in­
stitutions to accelerate reform and to upgrade their technology. In
China, large Internet companies such as Alibaba and JD have rapidly
encroached upon the financial sector with their advanced technology
and strong customer base. Although this has greatly affected the busi­
ness and development direction of traditional financial institutions,
especially commercial banks, it has also brought new development
opportunities and promoted reformation within commercial banks.
This paper has constructed an Internet finance index based on the
Baidu search database and selected 11 sub-indicators according to the
profitability, liquidity, security and growth management principles of
commercial banks, then used factor analysis to reduce the dimension to
construct a comprehensive business performance index of commercial
10
International Review of Financial Analysis 72 (2020) 101579
J. Dong, et al.
Table 15
Robustness test of the influence of Internet finance on the profitability, li­
quidity, safety and growth of commercial banks.
Variables
tt
er
cr4
gdp
L.pi
L.li
Commercial
bank
profitability
Commercial
bank liquidity
Commercial
bank security
Commercial
bank growth
Variables
Subsample 1
Subsample 2
Subsample 3
L.bop
SYSGMM
SYSGMM
SYSGMM
SYSGMM
gdp
0.0054
(0.0713)
−0.0581
(0.0506)
0.2388
(0.1721)
0.0783
(0.2243)
0.7189⁎⁎⁎
(0.0437)
0.0351
(0.0291)
0.0545⁎
(0.0291)
0.0886
(0.0659)
0.1699⁎⁎
(0.0739)
0.0670
(0.0297)
0.4085⁎⁎⁎
(0.0305)
−0.0967⁎
(0.0585)
−0.2790⁎⁎⁎
(0.0846)
0.5037
(0.0826)
−0.4113⁎⁎⁎
(0.0731)
−0.6524⁎⁎⁎
(0.1954)
−1.3349⁎⁎⁎
(0.2609)
L.gdp
0.4946⁎
(0.2943)
−0.9699
(0.7718)
0.7524
(1.0066)
0.1659⁎⁎⁎
(0.0520)
−7.7%
0.0505
(0.0529)
−0.0468
(0.0786)
0.0410
(0.1976)
0.4880
0.9786
0.5448⁎⁎⁎
(0.1642)
−1.1621⁎⁎⁎
(0.4030)
1.0220⁎⁎
(0.4954)
0.1536⁎⁎
(0.0689)
−14.56%
0.0660⁎
(0.0400)
−0.0664
(0.0569)
0.0529
(0.1797)
0.1789
0.9956
0.6481⁎⁎⁎
(0.2503)
−1.3097
(0.9259)
1.6028⁎⁎
(0.7717)
0.1001
(0.1933)
−44.34%
0.0076
(0.1021)
−0.1665
(0.1188)
0.5079
(0.6110)
0.2450
1.0000
L.si
⁎⁎
⁎⁎⁎
tt
ipo
er
cr4
1.0119⁎⁎⁎
(0.0757)
0.1953⁎⁎⁎
(0.0436)
L.gi
AR(2)-P
value
Sargantest Pvalue
Table 17
Robustness test for heterogeneous impact of internet finance on the integrated
business performance of commercial banks.
AR(2)-P value
Sargan-test P-value
0.2354
0.5278
0.2213
0.1319⁎⁎⁎
(0.0322)
0.4872
0.9961
0.4167
0.9986
0.9989
commercial banks and lastly large state-owned commercial banks as the
most affected.
Considering the above empirical findings, several policy implica­
tions are proposed as follows.
1) The Chinese government should strengthen the supervision and
regulation of Internet finance. Ways of doing this include: standar­
dize the market access system of Internet financial institutions; es­
tablish market access standards for Internet finance institutions with
reasonable strength and reputation; establish a modern financial
system with controllable risks, multiple levels and wide coverage;
and, maintain Internet financial information security. In terms of the
hardware, the government should control its information network
system to ensure the security of its information infrastructure.
Furthermore, the government should not only monitor various fi­
nancial and economic indicators but should also constantly improve
consumer information protection to maintain financial and social
security.
2) The government should implement different policy orientations for
different commercial banks. The empirical results show that the
operational efficiency of different types of commercial banks is
heterogeneous. The relevant governmental departments should en­
courage different commercial banks to enhance their social financial
supply capabilities with differentiated products, differentiated ser­
vices and refined management. The benefits of this will be financial
stability and a contribution to the overall development of the
economy.
3) The government should actively guide cooperation between com­
mercial banks and Internet finance institutions. From the perspec­
tive of demand, commercial banks and Internet financial institutions
can jointly analyze the potential needs of customers and open up
basic financial services such as savings, credit, bill acceptance and
discounting, and higher-level financial services such as investment,
wealth management, mobile payments, bond issuance, etc. From the
perspective of supply, the government should actively guide com­
mercial banks to expand upstream and downstream customer
chains, which may solve the problem of high customer cost and risk
control, and help commercial banks to obtain greater operating
profit with less marketing costs. Finally, the government should
guide both Internet finance organizations and commercial banks to
improve the management of the credit information system. Internet
finance institutions and commercial banks can complement each
other's customer information; when the two cooperate, they can
establish a legal and compliant financial data trust and sharing
platform.
Table 16
Robustness test for the impact of internet finance on the comprehensive busi­
ness performance of commercial banks.
Variables
POOL
FE
RE
DIFFGMM
SYSGMM
tt
−0.0756
(0.0752)
−0.0209
(0.0222)
0.1357⁎⁎⁎
(0.0225)
−0.1711
(0.1958)
−0.2688
(0.2517)
−0.1036
(0.0646)
0.0321
(0.0314)
0.1179⁎⁎⁎
(0.0249)
−0.1068
(0.1673)
−0.1938
(0.2141)
−0.0943
(0.0640)
0.0144
(0.0278)
0.1238⁎⁎⁎
(0.0236)
−0.1283
(0.1662)
−0.2189
(0.2129)
0.2038
0.2265
0.2256
0.1798⁎⁎⁎
(0.0442)
0.0729⁎⁎
(0.0345)
−0.0916⁎
(0.0502)
0.0345
(0.1053)
−1.1667⁎⁎⁎
(0.4004)
0.5286⁎⁎⁎
(0.1316)
0.9485⁎⁎
(0.4733)
0.1556⁎⁎⁎
(0.0459)
−0.0699
(0.0425)
−0.0446
(0.0536)
−0.0771
(0.1102)
−0.7231⁎⁎⁎
(0.2609)
0.7241⁎⁎⁎
(0.0932)
0.4717
(0.2989)
0.1642
0.8732
0.0746
1.0000
ipo
er
cr4
gdp
L.bop
L.gdp
r2
AR(2)-P value
Sargan-test Pvalue
banks. This paper has verified the effects of Internet finance on the
profitability, liquidity, security, growth and comprehensive business
performance of commercial banks in turn based on the static panel
model and the dynamic panel model. Furthermore, according to the
property of commercial banks, this paper has divided them into large
state-owned commercial banks, joint-stock systems banks and city
commercial banks. This paper has conducted a heterogeneous empirical
test of the comprehensive business performance of these three types of
banks in relation to the effect of Internet finance. Finally, we performed
a robustness test by adopting an Internet amount transaction scale and
comparing this with our Internet finance index. The empirical results
show that Internet finance has a positive impact on the profitability,
security, growth and comprehensive business performance of com­
mercial banks, but has a negative impact on the liquidity of commercial
banks. The impact of Internet finance on the comprehensive business
performance of commercial banks is heterogeneous with city com­
mercial banks being the most affected, followed by joint-stock
11
International Review of Financial Analysis 72 (2020) 101579
J. Dong, et al.
Although this paper has carried out a comprehensive analysis of the
impact mechanism of Internet finance on commercial banks, there are
still some limitations. For instance, we were only able to use listed
banks' data for our sample, so there is an extent to which our in­
vestigation of the impact of Internet finance on commercial banks is
incomplete. In future research, we wish to conduct heterogeneous
empirical tests on a larger sample.
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