Research on the Impact of Finance on the Profitability of Commercial
Banks under the Background of Internet+
Yanxian Tan
a
,
Hui Wang and Xueqin Tan
Software Engineering Institute of Guangzhou, Guangdong, China
Keywords: Internet+, Commercial Banking, Financial Markets, Profitability, Decision Making Research.
Abstract: With the continuous progress and prosperity of mobile Internet science and technology, relying on it and the
operation of Internet finance is also natural, and with its advantages of high transparency, good cooperation,
intermediate costs and low cost, quickly occupied China's financial market. Based on the measurement and
analysis of the risk value of Internet finance and commercial banks, this paper studies the risk spillover effect
of Internet finance on commercial banks from the macro and micro levels. Based on the principle of minimum
AIC, BIC and maximum likelihood, this paper selects the best ArMA-GARCH model for the selected 2 groups
of index series and 13 groups of return series, and calculates their risk value and risk spillover value on the
basis of fitting (Wang 2021). In the aspect of risk measurement, the value of risk is quantized by calculating
the value of risk (Va R), and in the aspect of risk spillover, the risk spillover effect is comprehensively
analyzed by calculating the conditional value of risk (Co Va R), its derived index risk spillover value (Co
Va R) and relative risk spillover degree (%Co Va R). The empirical results show that the impact of Internet
financial risks makes commercial banks suffer from positive risk spillover, but there is no consistency between
macro and micro in the direction of risk spillover (Liu ). On the macro level, the empirical results show that
Internet finance has positive risk spillover to commercial banks, but on the micro level, Yu 'ebao has positive
and negative risk spillover to 12 commercial banks, and the spillover directions of different commercial banks
are not consistent. In terms of overflow intensity, there is no uniform rule for different types of commercial
banks.
1 INTRODUCTION
Internet financial in the process of its development
has a direct impact to the traditional financial
industry, the traditional financial sector operate a
shift in the direction of formalization and digitization
two, so the Internet finance and traditional finance
also closely relates in together, when the Internet
through financial risk, the risk was amplified,
endanger the development of the traditional financial
industry, That is, there is a spillover effect. In China's
financial system, the banking industry has always
played a very important role, and the competition and
cooperation between Internet finance and banks in
business will inevitably bear the brunt of the risk
spillover effect of Internet finance. Exploring the risk
spillover effect of Internet finance on China's banking
industry, on the one hand, can provide reference
opinions for Internet finance enterprises and
commercial banks to carry out scientific and
reasonable risk control and improve the risk
assessment system; On the other hand, it can also help
investors to have a clearer understanding of
investment risks, so that investment tends to be
transparent and reasonable, and create a healthy
investment environment (Yan 2019).
On November 3, 2020, ant Financial, a giant
Internet financial company, was suspended from
listing, which once again shows that behind the rapid
development of Internet finance, there is actually a
huge financial risk. This risk not only affects ant
itself, but also spreads to other financial institutions
through the whole network of financial system.
Serious or even the outbreak of systemic financial
risks (Chen 2021). The bank is the core of the
financial system, the safety of the bank is related to
the security of the whole country's economy. At the
same time, on the one hand, the development of
Internet finance has impacted all kinds of business of
banks; On the other hand, the arrival of THE 5G era
also speeds up the pace of banking capitalization,
resulting in the formation of numerous links between
818
Tan, Y., Wang, H. and Tan, X.
Research on the Impact of Finance on the Profitability of Commercial Banks under the Background of Internet+.
DOI: 10.5220/0011350200003440
In Proceedings of the International Conference on Big Data Economy and Digital Management (BDEDM 2022), pages 818-824
ISBN: 978-989-758-593-7
Copyright
c
2022 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
banks and Internet finance. Therefore, paying close
attention to the risk of Internet finance and the
spillover effect of such risk on commercial banks is
the basis of providing some reference suggestions for
reasonable control of such risk.
2 THE RELATED THEORY
2.1 Internet Finance
On October 13, 2016, The General Office of the State
Council issued the Notice on the Implementation Plan
of the Special Rectification of Internet Financial
Risks and pointed out that: Internet finance is not
simply the combination of the Internet and finance,
but a new model and business, of course, it is created
to meet the new needs of users after the realization of
security, need to trust and accept mobile radio and
other network technology (Wang 2021).
Traditional financial institutions begin to use new
technologies to upgrade their businesses and optimize
their own management. For example, artificial
intelligence technology is used to replace ordinary
labor, thus reducing business costs while improving
business efficiency and quality. Big data technology
can be used to mine and process customer
information to reduce information asymmetry and
thus reduce bank operation risks (Chen 2021).
2.2 Fin Tech
Fin tech is a financial disinter mediation activity
carried out by innovative enterprises using science
and technology to traditional financial institutions
(such as commercial banks). The Monetary Authority
of Singapore sees fin tech as an innovative
technology that can be used in the traditional
financial sector as well as relevant regulatory bodies.
In 1992, with the establishment of The China
Association for the Promotion of Science and
Finance, the word fin tech gradually entered the
Chinese people's vision. In recent years, with the
progress of information technology and its
development and innovation in the financial field (Li
2021), fin tech has become the driving force and
support for the innovation and development of
financial business from the intermediate link in the
beginning, and its role in the financial industry is
growing and its influence cannot be underestimated.
From the perspective of the application of technology
in financial business, the development of fin tech in
China can be divided into three stages: financial
electrification stage, Internet finance stage and deep
integration stage.
2.3 Profitability of Commercial Banks
Profitability of commercial banks refers to the nature
that banks can make profits by operating their own
assets. This nature is not only affected by the bank
itself, but also by the external operating conditions of
the bank. It is one of the three characteristics followed
by the operation and management of commercial
banks, and it is also the object that commercial banks
focus on. Specifically, profitability refers to the
ability of a bank to obtain income and achieve its
asset appreciation target through its own business
operation within a certain accounting period.
Therefore, this paper mainly measures this index
through return on total assets (ROA) (Liang 2021).
On the other hand is the income structure of
commercial banks, which mainly refers to the
composition of profits or operating revenues of
commercial banks (including: By comparing the
income structure and the proportion changes of each
part, we can effectively see the development of
commercial banks in different businesses, and
measure whether the current development of
commercial banks is reasonable. Therefore, This
paper mainly measures the contribution rate of bank's
balance sheet business and off-balance sheet business
to bank profits by the proportion of non-interest
income to total operating income, and analyzes the
impact of fin tech on the income structure of
commercial banks according to the changes of the
two.
3 RISK MEASUREMENT AND
MODEL CONSTRUCTION
3.1 Risk Measurement Method Va R
Va R is derived from English 'Value at Risk', which
refers to the maximum loss faced by a certain
financial asset or portfolio under normal market
fluctuations. In July 1993, G30 members proposed
that Va R was first used to measure financial risk. In
1999, Basel committee encouraged the use of Va R to
measure the credit risk of commercial banks.
Subsequently, in 2004, the measurement object of Va
R was expanded to the sum of credit, market and
operational risks. At the same time, many scholars
also strongly advocated using Va R to measure some
risks common to commercial banks. Since then, Va R
Research on the Impact of Finance on the Profitability of Commercial Banks under the Background of Internet+
819
has been widely used and plays a very important role
in risk measurement (Zeng 2021).
From a statistical point of view, Va R refers to the
maximum possible loss of a certain financial asset or
portfolio value in a specific period in the future at a
certain probability level or confidence level, which
can be expressed as:
(1)
P represents the value loss of a certain financial
asset or portfolio within a certain holding period; P
represents probability and α represents significance
level. Generally, the significance level is set at 5%,
which reflects the risk preference or acceptance
degree of financial asset managers. Different
significance levels represent different risk degrees.
Generally speaking, the significance level is
determined according to the investor's preference,
acceptance and acceptance degree of risk.
3.2 CoVa R
The emergence of Va R has realized the
transformation of risk analysis from qualitative to
quantitative, but it can only be used to measure the
maximum loss at risk faced by a single financial
institution or market. In the same market
environment, when a certain financial institution has
a risk, other financial institutions may also be affected
by the risk spillover, but Va R cannot calculate the
size of the risk spillover or judge the direction of the
spillover.
In 2009, Adrian & Brunnermeier proposed Co Va
R, which is used to measure the economic loss that a
portfolio may face in a crisis or high risk situation
(Ren 2021). Its expression is:
α
αα
== )(
m nnmn
VaRXCoVaRXP
(2)
Where,
n
VaR
α
represents the value of risk faced
by financial institution n at a given significance level
α;
n
CoVaR
α
represents the value of risk faced by
financial institution n at a given significance level,
and the value of risk is
n
VaR
α
.
mn
CoVaR
α
is the
conditional Va R of financial institution m with
respect to financial institution n, used to measure the
total Va R of financial institution m facing risks. It is
the sum of the Va R
m
VaR
α
of financial institution
m and the risk spillover value of financial institution
n to financial institution m when the market is at a
normal fluctuation level (Luo 2021).The risk
spillover value is usually expressed as
mn
CoVaR
α
Δ
, which is the difference between the conditional Va
R
mn
CoVaR
α
and Va R
m
VaR
α
of financial
institution m.
mmnmn
VaRCoVaRCoVaR
ααα
=Δ
(3)
mn
CoVaR
α
Δ
N measures the financial
institutions for financial institutions m generated by
the size of the risk of overflow, but different size of
the risk value of financial institutions tend to have
difference, in order to better facilitate comparison and
study, to deal with the dimensional change risk tend
to overflow value, n get financial institutions for
financial institutions risk spillover, m
mn
CoVaR
α
%
.It is used to measure the proportion of risk spillover
received by a financial institution to its own risk, and
its specific mathematical expression is as follows
(Zhang 2021):
%100% ×
Δ
=
m
mn
mn
VaR
CoVaR
CoVaR
α
α
α
(4)
This paper will study the risk spillover effect of
Internet finance on commercial banks by combining
mn
CoVaR
α
,
mn
CoVaR
α
Δ
,
mn
CoVaR
α
%
and
three indicators.
3.3 Va R and Co Va R Values Are
Calculated based on ArMA-
GARCH Class Model
A mean equation plus a Va Riance equation forms a
time series. The residual of ordinary ARMA model is
the white noise sequence that cannot analyze any
information, so the Va Riance equation is ignored.
The mean value of GARCH model is usually
assumed to be a constant, and the residual has ARCH
effect, so the research focus is on the Va Riance
equation, and the mean value equation is usually
ignored. In order to model both the mean and the
difference, the two models are combined
(Teng 2021).
The ARMA(P,q)-GARCH(m,n) model is
constructed, and its expression is:
tjt
q
j
jit
p
i
it
XX
εεθφ
φ
+++
=
=
=
2
11
0
(5)
2
1
2
1
2
it
n
j
jit
m
j
it
=
=
++=
εβσαωσ
(6)
It can be seen from the above expressions that the
ArMA-GARCH model is a random process in which
the mean value meets the ARMA process and the Va
Riance meets the GARCH process.
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In this paper, arma-garch model is used to fit the
data of Internet finance and commercial banks, and
the model with the best goodness of fit is selected to
obtain the regression results, so as to calculate the
corresponding Va R and Co Va R values. For easy
understanding, we assume that ARMA (1,1) -garch
(1,1) is used.
The model best fits the data of financial institution
M as an example to illustrate the calculation
principle, and its expression is as follows (Fan 2021):
tt
mm
t
mm
it
mmm
t
SXX
εεθφφμ
++++=
11121
(7)
2
1112
2
11
2
+++=
t
mm
t
m
t
mm
t
S
εβασαωσ
(8)
Where, is the return rate of financial institution M,
is the state Va Riable, and is the conditional Va
Riance.
m
t
X
m
S
1-t
2
t
σ
m
t
m
t
m
t
m
it
q
i
m
i
m
it
p
i
m
i
m
t
mm
t
XSX
εμεεθφφ
+=++++=
=
=
11
10
(9)
Then, the risk value of financial institution M
affected by the risk of financial institution n can be
calculated by the following formula:
mn
t
CoVaR
m
t
m
t
mn
t
QXCoVaR
σα
)1( =
(10)
4 INTERNET FINANCE AND
RISK ANALYSIS OF
COMMERCIAL BANKS
This paper measures and analyzes the value-at-risk of
Internet finance and commercial banks from macro
and micro levels. First, the selection of sample data
and Va Riable symbols are explained, followed by
descriptive statistics of sample data, and then the data
are tested. On the basis of passing the test, the best
ArMA-GARCH model is selected for each sequence
to fit according to the minimum AIC and BIC
principles and the maximum likelihood principle.
Finally, Va R is calculated and analyzed.
4.1 Sample Selection
Macroscopically, the Internet financial sector selects
THE China Securities Internet Finance Index to
represent the whole industry, while the commercial
banking sector selects the China Securities Bank
Index to represent the whole industry. The two
indexes are both compiled by China Securities
Corporation and are the most authoritative indexes in
these two industries in recent years, reflecting the
overall performance of the two major industries of
Internet finance and commercial banking. The data
comes from the official website of China Securities
Index Co LTD.
In the micro part, select a single Internet financial
product and some commercial banks, calculate their
risk value, and further study the risk spillover of
Internet financial products to commercial banks on
this basis. This paper selects the yield data of
Shanghai Composite Index as the state Va Riable.
The data of micro part and Shanghai Composite
Index come from RESSET database.
For the micro part, considering the late listing
time of some commercial banks, the first day is
selected as January 1, 2016, and the last day is
consistent with the macro part, which contains half
data of 4 years in total. Except weekends and
holidays, 1,072 observation values are included.
Since the listing dates of Bank of Shanghai and Bank
of Hangzhou were both later than January 1, 2016,
only 860 and 874 observations were included,
respectively.
4.2 Descriptive Statistics of Data
The stationarity of price series is generally poor. In
contrast, logarithmic return series has the advantages
of convenience and stability (Yu 2021). Based on
this, this paper adopts logarithmic return rate series,
whose calculation formula is as follows:
)ln()ln(
1
=
ttt
PPr
(11)
Among them, the rt Represents the logarithmic
rate of return at time t, PtRepresents the price level at
time t, Pt-1Represents the price level of the previous
period. There are 16 logarithmic return rate series
used in this paper, including return rate series of
Internet finance and commercial banks, return rate
series of Shanghai Composite Index, return rate series
of Yu 'ebao and return rate series of 12 commercial
banks. Since the value of the return rate itself is small,
in order to improve the differentiation, the return rate
and its descriptive statistics are uniformly reserved to
4 decimal places. The descriptive statistics are shown
in Table 1 and Table 2.
Table 1: Descriptive statistics of yield series (macro).
Research on the Impact of Finance on the Profitability of Commercial Banks under the Background of Internet+
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Table 2: Descriptive statistics of return rate series (micro).
Table 1 shows the data of Internet finance index,
banking index and Shanghai Composite Index. It can
be seen from the table that the average return rates of
Internet finance, banking index and Shanghai
Composite Index are all positive and decrease
successively. In contrast, the average return rate of
Internet finance index is significantly higher than the
other two indexes, reaching 0.0007, which is
consistent with the characteristics of high return of
Internet finance. The bank index followed with an
average yield of 0.0004. The Yield of the Shanghai
Composite Index is only 0.0002, which is the smallest
in comparison.
Standard deviation is used to reflect the
fluctuation of a set of data, and the greater the
standard deviation, the greater the fluctuation of the
set of data, and vice versa. It can be seen from Table
1 that the fluctuation range of the three series is not
large. The fluctuation range of the banking index is
very close to that of the SSE Composite Index, while
the standard deviation of the Internet finance index is
slightly larger than that of the first two series,
indicating that the fluctuation range of the Internet
finance index is larger than that of the banking index
and the SSE Composite Index.
4.3 Calculation and Analysis of Va R
Value
The premise of calculating Va R is to build a model
to fit the rate of return data. In order to consider both
the mean and Va Riance, this paper selects Arma-
garch class model. Different sequences of data have
different characteristics, so the selected ArMA-
GarCH model is not consistent. After the model is
established, step forward prediction is made to obtain
the step forward prediction values of mean and Va
Riance, denoted as
m
t
X
ˆ
and
m
t
σ
ˆ
respectively,
and then according to the formula:
)1(
ˆ
ˆ
ασ
= QXVaR
m
t
m
t
m
t
(12)
It can calculate the at risk values of Internet
finance index, China Securities Bank Index,
Tianhong Yu 'ebao and selected commercial banks at
a given significance level. The calculation results
are as follows.
Table 3: Calculation results of Va R (macro part).
Va
R
The
mean
The
maximum
The
median
The
minimum
value
The
standard
deviation
hlw 0. 0236 0. 0234 0. 0491 0. 1341 0. 0181
bk 0.0214 0. 0191 0. 0297 0. 1212 0. 0139
After calculation, the mean value of Va R of
Internet finance index is -0.0236, and that of China
Securities Bank index is Va R. The median risk value
of the Internet finance index is 0.0491, while the
median risk value of the China Securities Index is 0.
0297. Both the mean and the median risk value of the
Internet finance index are higher than that of the
banking index, so the risk of Internet finance is
relatively high. To compare the standard deviation,
the chart shows that the risk of Internet financial
index value of the standard deviation is 0. 0181, the
risk of bank index value of standard deviation is 0.
0139, much smaller than the Internet financial index,
shows that the risk of Internet financial index value
of volatility than bank index, namely that stability of
the risk, the banking sector is superior to the Internet.
5 ANALYSIS OF INTERNET
FINANCE'S RISK SPILLOVER
EFFECT ON COMMERCIAL
BANKS
5.1 Risk Spillover of Internet Finance
to the Asset Business of
Commercial Banks
The business of using assets to create income is the
asset business of commercial banks, which generally
refers to Va Rious types of loans. In Internet finance,
P2P industry, online credit consumption, Internet
crowd-funding and other industries with financing
business compete with the asset business of
commercial banks. With their unique advantages, the
asset business of commercial banks is threatened and
challenged due to many alternatives. Loans from
traditional financial institutions
The business threshold is high and the approval
procedures are complicated. Small, medium and
micro enterprises often find it difficult to borrow the
funds they need because of their congenital lack of
conditions, while the private lending market is
chaotic and risky. The emergence of P2P network
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platform loans has solved these two problems well.
The PEER-to-peer (P2P) industry is gaining
popularity because of its timeliness, low barriers to
entry and simple processes. However, the financing
scale of P2P industry is limited and the risk is large,
which greatly reduces the attractiveness of customers
with superior conditions.
The emergence of crowdfunding platforms makes
it possible for many enterprises or individuals to raise
funds through the Internet. Internet crowdfunding is
popular because of its transparency and speed of
raising funds, as well as its low transaction costs. By
taking advantage of these advantages, crowdfunding
platforms have attracted a large number of customers
with capital needs, resulting in the reduction of the
loan business of traditional commercial banks and
bringing a certain impact to commercial banks. If
consumer credit platforms cooperate with banks,
consumer information can be converted into
customer credit recognized by banks. However,
Internet technology is not fully mature, and it is easy
to spread its own risks to banks, bringing default risks
and economic losses to banks.
5.2 Indirect Risk Spillover of Internet
Finance to Commercial Banks
The risk spillover of Internet finance not only directly
affects the business of commercial banks, but also
indirectly infects the risks to banks through the
financial association network, among which the most
common way is to induce and cause some systemic
risks to the commercial banks.
(1) Internet technology is not yet mature, and the
online business of commercial banks relies on mobile
Internet technology, so there are risks. The potential
risks brought by Internet technology to banks can be
analyzed from three aspects. First, there are
vulnerabilities in computers and mobile devices used
for transactions. Secondly, commercial banks' online
trading platforms or APPS have security risks.
Finally, the transaction data between customers and
banks relies on the Internet network for transmission,
and there is a risk of theft. The immaturity of Internet
technology mainly threatens the banking industry
from these three aspects. If any problem exists in any
aspect, it will cause information leakage or damage to
data integrity, and finally threaten the security of
customers' funds.
(2) Internet finance can influence the macro
economy. With the popularization and rapid
development of Internet finance in China, it has
become an important part of the whole macro
economy, so its change will have an important impact
on the whole Macro economy of China. For example,
changing the interest rate, driving the economy and
changing the supply and demand of money will lead
to the change of macro policies, which will threaten
the operation efficiency of banks and lead to the
expansion of bank credit. These are inverted shadows
It will lead to market instability, increase
economic volatility, and ultimately lead to systemic
risks.
(3) There is information contagion between
Internet finance and banking. The emergence of
Internet finance, such as platform abandonment,
centralized rectification and policy risks, has brought
crisis to the industry, which has been reported and
spread by the media and infected the
banking industry.
At the same time, the investor's psychological risk
prediction may be on their investment business
activities and financial management directly impact
behavior, spread too much negative news may even
directly affect the investor's psychological risk
prediction, make investor psychology becomes
fragile, eventually lead to investment behavior
change, reduce the profitability of commercial Banks,
eventually trigger a systemic risk.
6 CONCLUSIONS
This paper studies the risk spillover of Internet
finance to commercial banks. Before risk spillover
analysis, the risks of Internet finance and commercial
banks should be measured first. This paper measures
the risks of Internet finance and commercial banks
from macro and micro levels. From the macro point
of view, we measure and analyze the financial risks
of the two research objects by using the Internet
finance index and the bank index of China Securities
Corporation. On the micro level, the return rate series
of representative Internet financial product Antiphon
Yu 'eBay and 12 listed commercial banks of different
types are selected to calculate and analyze their
respective risk values. Secondly, the risk spillover
effect of Internet finance on commercial banks is
studied. Similarly, risk of overflow from the
macroscopic and microscopic two level, the
difference is that we study the risks are not only a
quantitative risk only when overflow of overflow
value calculation, but prior to the Internet in our
country finance for commercial bank risk caused
overflow has carried on the qualitative analysis,
qualitative analysis at the direct and indirect two
aspects to analysis the risk of overflow , let us have a
more comprehensive understanding of the risk
Research on the Impact of Finance on the Profitability of Commercial Banks under the Background of Internet+
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spillover mechanism of China's Internet finance for
commercial banks.
This paper innovatively selects yu 'eBay, the
largest Internet monetary fund, and 12 listed
commercial banks from a micro perspective to study
the risk spillover effect of a single Internet financial
product on different banking institutions from a new
perspective. Based on the analysis of the Internet on
commercial bank financial risk spillover, although
the macro level, it is concluded that the Internet
financial overflow the risks of commercial Banks has
positive conclusion, the empirical results show that
the micro Internet financial products celestial balance
treasure of the selected 12 listed commercial Banks
to the risk of overflow direction is negative, the
overflow direction is inconsistent, However, this
paper does not further explore the specific reasons for
different risk spillover directions at the micro level.
ACKNOWLEDGMENT
Fund project 1: This paper is the mid-term research
result of the university-level teaching and research
project of Guangzhou Institute of Software "Research
on the Impact of Internet Finance development on
risk-taking of commercial Banks" (No.: KY202007).
Fund project 2: This paper is the mid-term
research result of the higher education research
project of the "14th Five-year Plan" of Guangdong
Higher Education Association, "Research on talents
Training Mode Innovation of Application-oriented
Undergraduate Colleges from the perspective of"
New Engineering "(NO.21JYB180).
Fund project 3: This paper is a phased research
result of the 2021 Scientific research and Technology
project of Guangzhou Institute of Software
(NO.ky202115), "Application and Research of Key
Technologies in Smart Campus based on 5G
Environment".
AUTHOR INTRODUCTION
Tan Yanxian (1989-), female, MASTER candidate,
lecturer, research direction: financial measurement,
big data.
REFERENCES
Chen Hui. The impact of financial disintermediation on the
profit structure of China's Commercial Banks [D].
Shandong University, 2021.
Chen Yueyi. Research on the impact mechanism of Internet
finance on commercial banks' profitability [D]. Sichuan
University, 2021.
Fan Ruixue. The impact of Internet finance on Our
commercial bank -- Based on the perspective of
profitability and security [J]. North Finance, 2021, (05):
63-67.
Li DEzhen. Research on the impact of Internet finance on
profitability of commercial banks under the
background of fintech development [D]. Sichuan
University, 2021.
Liang Xiaoming, LIAO Yangting. Profitability, return on
Total Assets and the strategic development of
commercial banks [J]. Time Finance, 2021, (17):36-39.
Liu Mengfei, WANG Qi. Does Internet finance reduce the
profitability of commercial banks? Transactions of
Beijing Institute of Technology (Social Science
Edition), 2021,23(06):96-109.
Luo Chuyue. Analysis of the impact of Internet finance on
the profitability of China's commercial banks [J].
Today's Wealth,2021, (11):43-44.
Ren Yuting. Research on the impact of Internet Finance on
the profitability of listed Commercial Banks [D].
Shanxi University of Finance and Economics, 2021.
Teng Junnan. Research on the impact of Internet Finance
on the profitability of JS Banks in China [D]. Shandong
University of Finance and Economics, 2021.
Wang Jiao. Research on the relationship between Internet
finance and profitability of Domestic listed commercial
banks [D]. Northwestern University,2021.
Wang Yu, KAN Ba. The impact of Internet Finance on the
profitability of commercial banks [J]. Financial
Science, 2021, (11): 14-24.
Yan Zhiyu. Research on strategies to improve the
profitability of commercial banks [J]. Investment and
Entrepreneurship, 2019, 32(17):74-76.
Yu Juyang, Song Liangrong. Research on the impact of
Internet Finance on the profitability of commercial
banks [J]. Reform and Opening up,2021, (09):19-24.
Zeng XIUqin. Research on profit model of commercial
banks under the Background of Internet Finance [D].
Yunnan University of Finance and Economics,2021.
Zhang Minmin. Research on the impact of financial
technology on the profit model of commercial banks
[D]. Shandong University of Finance and Economics,
2021.
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