Is There Gap between Operating Performance of Systemically
Important and Non-Systemically Important Banks in China?--
Empirical Study based on Public Panel Data after Release of List
DAODI YAO
Department of Business Administration,
Sejong University,
Seoul 05006,
KOREA
Abstract: - On September 22, 2023, the People's Bank of China (PBOC) and the State Administration for
Financial Regulation (SAFS) released the latest list of China Systemically Important Banks (D-SIBs). This
study aims to analyze whether there is a gap in operational performance between (D-SIBs) and (D-SIBs) under
additional regulation. The research method uses independent sample t-tests in statistics and the AHP_DEA
model for financial bank operational performance. The research results indicate that there are differences in the
operational performance of systemically important banks and non-systemically important banks in certain
indicators. systemically important banks have a larger share in the entire banking system, and systemically
important banks face more regulatory constraints than non-systemically important banks. This makes the cost
of capital restructuring for systemically important banks higher, thereby reducing the speed of capital
restructuring. However, further analysis indicates that there is no significant difference in operational
performance and risk control between banks with systemic importance and nonsystemic influence. In view of
this, systemically important banks must invest in technology and innovation to improve operational efficiency.
Key-Words: - Systemically Important Banks, Non-Systemically Important Banks, Business Performance.
Received: December 22, 2023. Revised: June 19, 2024. Accepted: July 13, 2024. Published: August 14, 2024.
1 Introduction
China's financial system is dominated by indirect
financing, and banks are the mainstay of indirect
financing. In reality, the total assets of China's
financial industry are around RMB 300 trillion, of
which the total assets of the banking industry are
RMB 268 trillion, accounting for 89% of the
financial industry. Listed banks are the
representatives of the banking industry. As of
August 31, 2023, 59 Chinese listed banks exist in
China (including A-share and Hong Kong shares).
Among them are 6 large state-owned commercial
banks, 10 joint-stock commercial banks, 30 urban
commercial banks, and 13 rural commercial banks.
The 59 listed banks can be further categorized into
15 A+H-listed banks, 27 pure A-share-listed banks,
17 pure H-share-listed banks, and the number of A-
share-listed Chinese banks is 42. The large size and
volume of assets of domestic banks and the
existence of cross-regional and cross-industry
situations are relatively standard. Since the global
financial crisis in 2008, macro-prudential policies
have gradually become the central tenet of
improving the financial regulatory system, [1].
Among them, strengthening the supervision of
systemically important financial institutions is
crucial for maintaining financial stability and
enhancing prudential management. Thus, the
Additional Supervisory Requirements for
Systemically Important Banks (for Trial
Implementation), a critical regime issued by the
China Banking Regulatory Commission (CBRC), is
explained in detail from 2021 onwards. Influential
banks must meet additional capital requirements of
0.25%,0.5%,0.75%,1%, and 1.5%. The People's
Bank of China (PBOC) and the State Financial
Supervision and Administration Bureau (SFSAB)
have conducted a systemic importance evaluation of
the 30 selected banks, which is published annually
and provides additional supervision of this category
of banks, internal capital constraints mechanisms,
enhanced liquidity, significant risk exposures, and
risk statement summaries, [2]. At the same time,
developing a recovery and treatment plan for the
treatability assessment is necessary.2023 On
September 22, 2023, the latest list for the current
year was published, with a total of 20, whose
combined assets accounted for 61% of the total
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assets of China's banking sector (the banking sector
accounted for RMB 379.39 trillion of the total assets
of the financial sector institutions as published by
the People's Bank of China (PBoC) at the end of
2022), and of which, except for GFB, all are A-
share-listed Banks.
Due to its large scale, it has a high degree of
structural complexity and close connection with
other financial institutions. It is essential in the
whole financial system, and if there is a severe risk
resulting in the decline of the enterprise's viability, it
will negatively impact the whole financial system
and the real economy, [3]. Therefore, an in-depth
study of China's banking industry is essential and of
great practical significance for its stability and safe
development. The continuous strengthening of the
state's supervision of bank capital is ultimately
reflected in the capital adequacy ratio of banks,
which increases capital or improves the quality of
assets and reduces the proportion of risky assets.
However, it is difficult to achieve the goal of
increasing capital in the short term, so systemically
important banks tend to make adjustments to their
operating structure by cutting high-risk interest
business and improving low-risk non-interest
business to realize the reduction of risky assets and
proportion, [4]. Current research on systemically
important banks by scholars mainly from the
perspective of regulation on how to prevent the risk
of "too big to fail" research, respectively, from the
quantile regression based on the static Covar model
for the identification of systemically important
banks, the identification of systemically important
banks in China to conduct a comparative study,
some scholars on the identification of systemically
important banks in China to conduct an empirical
study, and some scholars on the identification of
systemically important banks in China to conduct a
comparative study, [5]. Some scholars have
empirically analyzed the identification of
systemically important banks in China and
concluded that the entropy value method can
identify several large state-owned commercial banks
and some joint-stock commercial banks as the
current systemically essential banks in China, [6].
However, up to now, there is little research in the
academic community on whether there is a gap
between the operational performance of
systemically important banks and that of non-
systemically important banks after they are subject
to additional regulatory requirements. Therefore,
there is a lack of research on the question, "Is the
operating performance of the 20 systemically
important banks in China significantly different
from that of the 23 listed non-systemically
important banks?" There is some value in
researching the question.
2 Background of the Study
In recent years, along with the deepening of the
reform of the financial system, the emergence of a
series of issues, such as "de-mediatization" and
"marketization of interest rates", has had a more
significant impact on China's commercial banks.
After the last global economic crisis, the concept of
"systematic banking" has become well-known and
more common in recent years. Banks are a category
of financial institutions that are large, complex, and
vulnerable to external risks, [7]. Once they fail, they
may have a domino effect on the entire financial
system, causing a significant impact on the real
economy and even triggering an economic crisis.
However, the overall value of a bank cannot be fully
reflected in the size of individual banks and the
complexity of their business. However, the current
financial system in China still has many problems,
and some departments' internal control mechanisms
are incompatible with the market economy's
development, making its development not sound
enough, [8]. In this context, to achieve "maximum
efficiency", the financial holding company adopts
the operation mode, which can expand the scope of
operation, improve operational efficiency, and
prevent the spread of risk. Using net interest rate,
input-output ratio, operating income growth rate,
and other indicators can comprehensively reflect the
company's profitability and solvency. Modern
scholars use "profitability", "operation", "debt
service", and "future development" to evaluate the
enterprise, [9]. Evaluation. Mutual contagion
between real and financial risks reduces the trust of
market participants in the financial system and, in
severe cases, may lead to bank runs or panic selling
of assets. At the same time, the rapid spread,
diffusion, and outbreak of systemic financial risks
will be accelerated through various related
networks. The performance evaluation system is a
specific application in the banking business, [10].
In China, the regular operation of the financial
system has a vital significance to the development
of the macro-economy. The operation and
management of the financial system and the
operational efficiency of the development of the
whole society impact it. With the integration of the
world economy, the number of financial banks in
our country is also increasing. Nowadays, several
banking institutions coexist in the pattern of mutual
competition. The traditional "indicator method"
based on individual correlation is no longer suitable,
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and the network analysis method based on global
correlation is better adapted to the current regulatory
needs, [11]. However, with the changes in financial
patterns, the global network constructed solely on
micro-financial data can no longer truly reflect the
complex network within the financial system, and
its "time-varying" characteristics are pronounced
here. At the same time, insurance companies,
finance companies, leasing companies, stock
markets, investment funds, etc., and even those non-
financial institutions that can provide financing
intermediary, payment, and settlement services for
banks will compete with banks. Therefore, a gap
comparison between the operating performance of
systemically important banks and that of non-
systemically important banks is a practical
necessity.
3 Research Methodology
3.1 Systemically Important Bank (SIB)
Identification
The failure of the core financial institutions (SIFS)
in this system would significantly impact the
financial system as a whole and severely negatively
impact the real economy. SIFIS can be divided into
two types: one is a large, highly indebted institution
whose asset losses will spread to the entire financial
system and have a severe negative impact on the
overall economy; the other scenario is that the
failure of certain large, more closely interconnected
financial institutions will have a domino effect on
all parties to the trade. SIBs generally refer to banks
with significant operations, high business
complexity, low substitutability, and strong linkages
with other financial institutions. Currently,
academic methods for identifying systemically
important banks (SIBs) are divided into two main
categories: network analysis method and composite
index method. The network analysis method is
based on the inter-bank debt network model and
identifies systemically essential banks in terms of
loss-shock contagion risk measurement and network
topology properties, [12]. The loss-shock contagion
risk measure is based on the magnitude of the
contagion risk caused by the loss of assets of other
banks in the system due to the failure of a bank as a
measure of the bank's systemic importance. The
composite index method identifies systemically
essential banks through the selection of indicators
and the determination of weights and is a standard
method used by international and domestic
regulators to identify SIBs. The FSB has published
an annual list of G-SIBs since 2011, which scores
banks on systemic importance based on five core
tier-1 indicators: complexity,
substitutability/financial infrastructure, cross-border
operations, connectedness, and size. The FSB has
also published a list of banks that are "excessively
connected" to the rest of the system, including banks
that are not "excessively connected".
"Overconnectedness" may be the most direct factor
contributing to financial risks in China's financial
system, closely related to economic development.
The domestic central bank and CBIRC refer to the
FSB and Basel Committee's SIBs scoring criteria to
develop the Assessment Methodology, which scores
30 domestic banks on four dimensions, namely,
complexity, substitutability, size, and
connectedness, and ultimately identifies 19 banks as
China's systemically important banks (D-SIBs).
There are three main methods for its indicators:
network analysis method, collaborative risk
modeling method, and indicator method. Then,
based on a stable external financing environment
and internal risk supervision perspective, the
relationship between direct and indirect financing
caused by public risk factors was studied. Stock
prices measured the systemic importance of banks at
different times, mainly due to the varying degrees of
impact of credit risk between financial institutions at
different levels, [13]. There is usually a
corresponding reaction in stock prices. The
transmission path of risk should be a convergent
structure, with "diffusion" and "absorption" at its
core; on the contrary, if the risk transmission path is
of the discrete type of "diffusion", the financial
system is unstable. On the contrary, if the risk
transmission path is discrete "diffusion", the
financial system is unstable. However, most existing
studies have been conducted from the perspective of
leverage, asset size, and maturity mismatch.
3.2 Bank Operational Performance
Revised total asset return indicators (capital
preservation and appreciation rate, sales
profitability, cost, and expense profitability) and
adjusted current asset turnover indicators.
In addition, enterprise performance evaluation is
based on indicators such as asset operation status,
financial efficiency status, development ability
status, and solvency status of the enterprise. Among
these indicators, the Financial Efficiency Index is an
important indicator that reflects a company's
profitability, [14]. In addition, the Asset Operating
Condition Index reflects the ability of a company to
earn profits from all assets and is an essential
financial management tool, [15]. The solvency
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index measures a company's ability to repay its
debts when they mature. The development capacity
index is a measure of a firm's potential to expand the
scale of its operations based on its development,
such as operating income growth rate, capital
preservation and appreciation rate, capital
accumulation rate, operating profit, total assets,
technology investment ratio, three-year capital, and
three-year operating income. Whereas there are
several rating methods for evaluating bank
performance, the Wall Score method is a process of
calculating the score by using the weights assigned
to seven financial indicators, such as quick ratio,
current ratio, equity ratio, etc., to obtain the
corresponding standardized ratio values, and then
comparing the actual ratio values with the
standardized ratios to arrive at the relative ratios, to
multiply the relative ratios by the weights they are
assigned in turn to obtain the overall score, [16]. In
essence, many financial indicators are linearly
linked, and the resulting values are used to measure
the enterprise's financial position. DuPont analysis:
from the perspective of return on equity, the return
on net assets is broken down item by item into the
multiplication of multiple financial ratios, which
breaks down the roles of the various influencing
factors, prompting corporate managers to gain an in-
depth understanding of the factors affecting the
return on net assets, and to analyze the correlation
among the factors; however, this method is not
suitable for evaluating commercial banks from a
financial perspective, [17]. However, this method
assesses commercial bank performance from a
financial perspective and does not comprehensively
reflect the effect. Balanced Scorecard: Compared
with the DuPont analysis method, which focuses too
much on short-term financial data, this method
breaks down the core objectives of the enterprise
and conducts inter-temporal assessment, which is
conducive to the rationalization of the relationship
between long-term and near-term goals, operational
efficiency indicators and non-operational efficiency
indicators.
3.3 AHP_DEA Business Performance Model
for Financial Banks
The APH_DEA model classifies the financial banks'
operation performance into three levels: based on
the analysis method of APH_DEA, the operation
status of China's financial banks is analyzed and
evaluated from three dimensions. The first level is
the decision-making level, which evaluates the
business performance of commercial banks; the
second level is the management level, which
evaluates the business performance of commercial
banks in three dimensions from the viewpoint of
safety, liquidity, and profit; and the third level is the
specific indexes, such as capital adequacy ratio,
non-performing loan ratio, borrowing ratio, liquidity
ratio, and deposit-to-lending ratio, [18]. Liquidity
criteria can be used to select financial indicators
such as non-performing loan ratio, borrowed funds
ratio, current ratio, liquidity ratio, deposit-to-loan
ratio, interest recovery ratio, return on capital, etc...
In contrast, profitability is measured by non-
performing loan ratio, current ratio, deposit-to-loan
ratio, interest recovery ratio, return on capital, etc.,
and these indicators intersect.
Select the BCC model in DEA and use
mathematical programming to calculate the relative
efficiency between the evaluated organizations. This
involves comparing the evaluated organizations
with the reference decision-making unit to obtain
relative efficiency, [19].
Applicable to the measurement of relative
efficiency values at different levels of
compensation, the APH-DEA modeling process is
shown in Fig. 2. The first step is to develop a
research hypothesis that systemically important
banks have significant assets. Systemic-importance
banks with important business operations face the
challenge of maintaining high operational
efficiency. However, investments in technology and
process optimization by systemically important
banks can help manage operational costs. In China,
due to the needs of China's economic system
reform, research on this topic is still in its early
stages. Due to its more direct operation and strict
regulation. Based on the above analysis, a research
hypothesis is proposed that there is no significant
difference in operational performance between
systemically important banks and non-systemically
important banks.
Research Methodology Using an independent
samples t-test is an effective statistical method for
comparing the differences between two groups of
data in quantitative research, and it is appropriate to
use an independent samples t-test to discuss the
operational performance between systemically
essential banks and non-systemically important
banks in China by comparing whether there is a
statistically significant difference between the two
groups of data in terms of the mean value, [20]. It is
mainly supplemented by databases such as Wind
and CSMAR and relevant data disclosed in each
bank's annual reports. Statistical analysis was done
using SPSS statistical software. In this paper, four
indicators, namely, earnings per share, return on
total assets, return on net assets, and growth rate of
net profit, were selected to evaluate the company's
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operating performance, and two indicators, namely,
non-performing loan ratio, and provision coverage
ratio, were used for risk management. Systemically
essential and non-systemically important banks,
totaling 43 banks, were divided into two groups.
Forty-three Chinese banks were selected as
samples to construct panel data for 2021-2023. The
sample selection is divided into a sample group and
a control group. Sample group: 20 Chinese
systemically essential banks were selected as the
sample group in 2023, including 6 state-owned
commercial banks, 9 joint-stock commercial banks,
and 5 urban commercial banks. According to the
systemic importance score from low to high, they
are China Everbright Bank, China Minsheng Bank,
Ping An Bank, Huaxia Bank, Bank of Ningbo, Bank
of Jiangsu, Bank of China, Guangfa Bank, Bank of
Shanghai, Bank of Nanjing, Bank of Beijing, China
CITIC Bank, Pudong Development Bank, Postal
Savings Bank of China, Bank of Communications,
China Merchants Bank, Industrial Bank of China,
Industrial and Commercial Bank of China, Bank of
China, China Construction Bank, Agricultural Bank
of China.
Control group: 23 non-systemically important
listed banks in China are selected as the control
group in FY2023: Bank of Lanzhou, Bank of
Jiangyin, Zhangjiagang Bank, Bank of Zhengzhou,
Bank of Qingdao, Qingdao Agricultural and
Commercial Bank, Bank of Suzhou, Bank of Wuxi,
Bank of Hangzhou, Bank of Xi'an, Yu Agricultural
and Commercial Bank, Bank of Changshu, Bank of
Xiamen, Ruifeng Bank, Changsha Bank, Bank of
Qilu, Shanghai Agricultural and Commercial Bank,
Bank of Chengdu, Zijin Bank, Zheshang Bank,
Bank of Chongqing, Guiyang Bank, and Sunon
Commercial Bank.
Data collection of earnings per share, return on
total assets, return on net assets, net profit growth
rate, and non-performing loan ratio, provision
coverage ratio in the last three years, based on
which carry out the statistical analysis of the total
distance, minimum, maximum, mean, standard
deviation, variance, etc. in different years, carry out
the statistical analysis of the test of normality and
chi-squareness to find abnormal data and deal with
them accordingly. Carrying out independent
samples t-tests to objectively present the results of
the t-statistics and P-values of the tests; if the P-
value is less than the chosen level of significance
(e.g., 0.05), it means that the difference between the
means of the two groups is statistically significant;
otherwise, it means that there is no significant
difference.
This article will include descriptive statistical
analysis, inferential statistical analysis, and
correlation analysis, calculated correlation types
(such as Pearson, Spearman), and whether they are
used to evaluate the strength and direction of
relationships. A threshold can be specified to define
weak/strong correlation. Providing transparency in
this way makes statistical analysis more robust and
replicable.
4 Conclusion
4.1 Trends and Related Gaps in the
Performance of Systemically and Non-
Systemically Important Banks
4.1.1 Descriptive Statistics
In order to understand the differences between the
sample group and the control group in each research
variable, descriptive statistics were conducted on the
raw data, and statistical software SPSS was used for
analysis. It was observed that there was a significant
difference in earnings per share between
systemically important banks and non-systemically
important banks.
In terms of the correlation between EPS, ROE,
NPL, and NPL, the average earnings per share of
systemically important banks in the past three years
were 1.32, 1.50, and 1.64, respectively, while the
average earnings per share of non-systemically
important banks were 0.78, 0.86, and 0.95,
respectively. This seems to indicate that "the larger
the bank, the more profitable it is." However, in-
depth analysis reveals that although the nature of the
bank's business is the same, due to differences in the
number of common shares, the bank's profits are not
as good as those of other banks. Therefore, this
single indicator cannot directly prove the significant
difference in the operating performance of these two
groups of banks but needs to be combined with
other indicators for comprehensive observation.
The descriptive statistics of EPS are shown in
Table 1.
The Return on Equity (ROE) for the last three
years shows that the ROE of systemically important
banks is 10.26%, 10.47%, and 10.35%, while that of
non-systemically important banks is 10.16%,
10.27% and 10.33% respectively. There is no
significant difference between the two groups of
banks on this indicator, so it is impossible to
conclude that there is a significant difference
between systemically important and non-
systemically influential banks in terms of
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performance. ROE descriptive statistics are shown
in Table 2.
Table 1. EPS descriptive statistics
Variable # year
V1
N
Std. Deviation
Std. Error
Mean
EPS#2022
1
20
1.2479
0.2790
0
23
0.5739
0.1197
EPS#2021
1
20
1.8264
0.2421
0
23
0.4569
0.0952
EPS#2020
1
20
0.8627
0.1929
0
23
0.4137
0.0863
Table 2. ROE descriptive statistics
Variable # year
V1
N
Mean
Std. Deviation
Std. Error Mean
ROE#2022
1
20
10.3519
2.5350
0.5668
0
23
10.3333
2.6479
0.5521
ROE#2021
1
20
10.4711
2.3403
0.5233
0
23
10.2753
2.0367
0.4247
ROE#2020
1
20
10.2680
2.1712
0.4855
0
23
10.1692
1.9908
0.4151
Table 3. NPL provision coverage descriptive statistics
Variable # year
V1
N
Mean
Std. Deviation
Std. Error Mean
NPL Provision
coverage#2020
1
20
245.29
108.96
24.36
0
23
288.45
88.05
18.36
NPL Provision
coverage#2021
1
20
264.71
113.82
25.45
0
23
327.49
119.79
24.97
NPL Provision
coverage##2022
1
20
263.41
106.06
23.71
0
23
351.08
140.62
29.32
Table 4. NPL Ratio descriptive statistics
Variable # year
V1
N
Mean
Std. Deviation
Std. Error Mean
NPL Ratio#2022
1
20
1.256
0.2848
0.0637
0
23
1.181
0.3721
0.0776
NPL Ratio#2021
1
20
1.280
0.2951
0.0660
0
23
1.243
0.2953
0.0616
NPL Ratio#2020
1
20
1.397
0.3094
0.0692
0
23
1.357
0.2802
0.0584
In terms of risk control indicators, the non-
performing loan (NPL) ratios of systemically
important banks were 1.39%, 1.28%, and 1.25% in
the last three years, while those of non-systemically
important banks were 1.35%, 1.24%, and 1.18%,
respectively, and there is no significant difference in
this indicator either. Finally, in terms of provision
coverage ratio, the provision coverage ratio of
systemically essential banks was 245.28%,
264.71%, and 263.41% in the last three years, while
it was 288.44%, 327.49%, and 351.07% for non-
systemically important banks, respectively. Within
the first two years of data analysis, this indicator did
not show a significant difference, but in the last
year, 2022, the statistics showed a significant
difference. There are two main reasons for this
difference: first, since the 2020 Chinese government
work report, the central government has been
committed to promoting financial institutions to
reduce financing costs and promote the development
of the real economy, especially requiring large
banks to support the development of small and
micro enterprises, and large banks have developed
small and micro enterprises with better business
conditions into their available customers through
lower interest rates, which has led to small and
medium-sized commercial banks facing higher asset
risk, requiring more provision coverage to cope with
it; on the other hand, the impact of the epidemic on
the real economy is gradually showing up,
especially the value of a property, which is the
primary collateral of banks, is declining, which
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makes it necessary for small and medium-sized
banks, which bear more significant business risks, to
have adequate provision coverage to cope with the
situation. The NPL Provision coverage descriptive
statistics are shown in Table 3, and the NPL Ratio
descriptive statistics are shown in Table 4.
From this analysis, it can be concluded that
although systemically essential and non-
systemically important banks show significant
differences in specific financial indicators, these
differences do not directly indicate that they are
significantly different in terms of overall operating
performance and level of risk control.
4.1.2 Trends and Differences
Bank capital is the funds invested by the
shareholders, that is, the funds held by the
shareholders, including capital, surplus, surplus
reserves, and undistributed profits. The primary
funding sources for a bank are debt, owner's equity,
and proprietary equity. Therefore, debt financing is
a method that can reduce a bank's overall cost of
funds and increase profits. However, as a bank's
debt ratio increases, its financial leverage and
operational risk increase. With too much financial
leverage, a bank may fail due to its inability to repay
its debts, and while debt capital can reduce the cost
of capital, it can also increase a bank's operational
risk. Equity investment is risk-free, but it will
increase the bank's capital cost. When the debt ratio
changes, the risk faced by the bank also changes.
The cost of financing also changes, which affects
the operational performance of the bank. EPS is an
essential measure of the value of an investment and
a basis for analyzing the value per share, which is an
important indicator that provides a comprehensive
picture of the profitability of systemically and non-
systemically essential banks, which reflects over a
period, a portion of a bank's net profit, which is the
after-tax profit it generates, and many investors pay
close attention to earnings per share when
examining a bank's financial situation. Earnings per
share is a bank's net assets divided by its total
equity, is a measure of current earnings per share
and annualized return, and is the most intuitive way
to measure a bank's operating conditions. ROE is
average net income divided by shareholders' equity,
and this indicator reflects the level of return on
shareholders' equity, providing a measure of the
efficiency of a company's use of its capital, with
higher indices indicating a higher return on
investment. This indicator reflects the ability of
one's capital to earn net income.
NPL is an important indicator for assessing the
safety of assets; a higher NPL ratio indicates that
non-performing loans account for a more significant
proportion of total loans, and a lower NPL ratio
indicates that the financial institution is safer. NPL
Provision Coverage From a practical point of view,
the ratio of the allowance for doubtful loans to the
allowance for bad loans issued by a bank determines
whether a bank has adequate credit provisioning.
This indicator reflects the risk level of bank credit
and the socio-economic environment and credit
conditions at the macro level. According to the
"Risk Rating System of Joint-Stock Commercial
Banks (Provisional)", the provision coverage ratio is
the ratio of actual lousy debt provisions to non-
performing assets, and the ideal condition of this
indicator is 100%. The trend of the performance of
EPS, ROE, NPL, and NPL Provision Coverage is
shown in Fig. 1.
Fig. 1: EPS, ROE, NPL, NPL provision coverage
performance trends
The correlation difference judgment matrix
obtained the weighting of the importance of each
program by comparing and calculating the relative
importance of two pairs of factors for each element
in each level and then obtained the guidelines for
the preferred program. Then, the relative importance
of each influential factor was used to analyze each
indicator's weight. At both the macro and micro
levels, it will affect not only the business
performance of the system and non-system vital
banks but also the business performance of the
financial industry in which the system's critical
financial institutions are located, as well as the
business performance of banks in different systems
and different systems. The primary and secondary
indicators of the regional financial system are the
same as the traditional statistical framework of D-
SIBs. The indicators of external claims and external
debts are added at the level of cross-administrative
activities to collect data on the structural
characteristics of the region, to comprehensively
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reflect the relationship between internal and external
claims and debts in the region, and to evaluate the
impact of the risk and its scope of propagation. In
other aspects, information collection on their
regional characteristics is emphasized while meeting
the overall information requirements. The APH
model has only one level, and there is no need to
check the compatibility of the judgment matrix and
the eigenvectors located in which position also
reflect the superior and inferior values of the
performance of the commercial bank as a way to
evaluate these commercial banks more precisely. To
determine the conditions under which the matrix can
realize the compatibility test, the order of a
judgment matrix can be expressed in terms of a
specific numerical value, and under certain
conditions, the judgment matrix satisfies the
following conditions, or else it needs to be corrected
appropriately. Combining the results of the model
processing derived from the method with the
method can make up for the shortcomings of the
model in different commercial banks in the
performance evaluation to understand the
differences between systemically important and
non-systemically influential bank business
performance, the relevant difference judgment
matrix, as shown in Fig. 2.
Fig. 2: Relevant difference judgment matrix
4.2 Analysis of Consistent Trend Tests of
Financial Business Performance of
Rotating Components in Years
The interconnection between banks is one of the
main ways to realize the risk contagion channel and
the inter-bank network is the most intuitive
manifestation of the inter-bank connection.
Traditional complex networks mainly include two
categories: regular and random networks, while
completely regular and random networks are not
standard in practical applications. The construction
of scale-free networks follows two mechanisms: one
is the growth mechanism, i.e., new nodes join the
network sequentially, which makes the network size
increase; the other is that, on this basis, the newly
joined nodes tend to establish connections with
high-value nodes. The interbank lending network is
a critical link in the study of risk contagion and
systemic risk among banks, and analyzing its
structural characteristics is the key to in-depth
exploring the risk transmission path and systemic
risk of the interbank lending network. The power
law index of out-degree value shows an overall
increasing trend. The power law index of in-degree
value shows an overall decreasing trend, indicating
that the out-degree value of high out-degree/in-
degree banks accounts for a decreasing proportion
of the overall degree value.
In contrast, the in-degree value accounts for a
higher proportion of the overall degree value, which
suggests that China's large banks are gradually
fading out their role as capital outflows in the
interbank market and gradually strengthening their
role as capital inflows. At the same time, meeting
the requirements of aggregate data, the author
focuses on the collection of regional characteristic
data, classify and collect the data within and outside
the region, especially the assets, liabilities, and other
related indexes within the financial system, and
make a detailed portrayal of the transaction links
between systemically essential banks and various
financial institutions within the region, to portray
better the flow and application of funds of the banks
in the inter-financial institutions. Thus, the entire
transaction structure of banks in the financial system
is better portrayed, and the association and
dependence of banks in the financial system are
comprehensively reflected. When the liquidity
supply side suffers an external asset shock, the
liquidity contraction due to asset losses will make it
reduce its short-term funding support to debtor
banks through the interbank network, which in turn
will make its debtor banks similarly fall into the
plight of illiquidity, leading to the failure of more
banks in the entire banking system, and causing a
more severe impact on the systemic risk, as shown
in Fig. 3 of the Consistency of Trends in Financial
Operating Performance Test (I), which shows that.
Fig. 3: Consistent trend test for financial business
performance (I)
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From the point of view of returns, it is not the
case that higher capital adequacy ratios are better
because lower capital adequacy ratios imply less
capital support and higher financing costs between
systemically important and non-systemically
essential banks. Therefore, financial banks have an
incentive to lower their capital adequacy ratios.
However, lower capital adequacy ratios can reduce
the ability to control risk and thus test a bank's
creditworthiness. The higher the capital ratios of a
bank that is important in the overall financial
system, the more resilient it is against defaulted
assets and the less capital risk it takes. If the
increase in capital adequacy is not due to the bank's
risk, an increase in capital adequacy will decrease
the ROE. Non-performing loans are abnormal or
problematic loans. A loan agreement in which the
borrower cannot repay the principal and interest on
the original commercial bank loan on time.
Although local government financing is
manageable, potential risks are still monitored. The
systemic financial risk triggered by stock futures
trading will be transmitted to other markets through
other channels, such as insurance and securities, in
addition to the direct effect on commercial banks.
The impact of other financial sectors on commercial
banks is mainly through more than indirect effects
on commercial banks.
Assessment of the structure and effectiveness of
bank capital utilization. By collecting the
information of essential counterparties in the capital
utilization of central banks, the author evaluates the
risk exposure between subjects, the maturity
mismatch structure of capital flows, and the
efficiency and safety of capital utilization to
improve banks' operation and management ability
and competitiveness. On this basis, a monitoring
mechanism is constructed based on the trend of
banks' cross-market and cross-industry capital
flows, their correlation characteristics, and risk
contagion paths. The main control variables are
gradually introduced into the model, in which those
variables that test the variables and help to reduce
the model setting errors are also kept. Then, each of
these variables is introduced into the explanatory
variables, the model's parameters are analyzed, the
optimal model is selected, and the financial business
performance consistency trend test (II), as shown in
Fig. 4.
In this analysis, Mann-Kendall and Spearman's
rho tests were used to evaluate trends in financial
performance metrics over time. The Mann-Kendall
test is a non-parametric method for trend detection
that evaluates the statistical significance of
monotonic trends in time series data. Spearman's rho
is a non-parametric measure of rank correlation that
can indicate how well the relationship between two
variables can be described using a monotonic
function. Both tests were performed at the 5%
significance level to identify consistent trends across
various profitability, asset quality, and liquidity
metrics for banks over the period studied. The
results provide evidence for trends in the financial
soundness of the banking sector.
Fig. 4: Consistent trend test for financial business
performance (II)
4.3 Comparative Analysis based on
Asymmetric Characterization of
Systemic and Non-Systemic Importance
Performance
Determining the regulatory capital requirements
faced by each banking system according to its level
of importance enables the smooth functioning of the
financial system while ensuring the sound operation
of individual banks. In this context, the degree of
importance of each commercial banking system is
directly related to the capital regulation it faces,
which in turn affects the need for capital and thus
leads to capital adjustments. By examining the
interaction term of the capital ratio gap, where the
amount of change is regressed only on the value of
the gap, the values reflect the rate of adjustment of
the three capital ratios, with both regulatory capital
ratios adjusting faster than the rate of adjustment of
leverage. Systemically important banks are subject
to more significant constraints than systemically
essential banks in general, which can increase the
cost of adjusting their capital structure, affecting the
adjustment of funding. In the negative leverage gap,
the positive gap in core capital adequacy, and the
asset class index, a more significant negative
leverage gap is associated with a more extensive
asset class index; conversely, it is slower when the
negative leverage gap is more extensive. However,
the negative core capital ratio gap increases when
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risk-weighted growth accelerates, suggesting that
the more influential banks hold riskier assets when
regulatory capital levels increase. The composition
of liabilities includes the growth rates of total, long-
term, and short-term liabilities. In both cases, the
higher the level of undercapitalization for a
systemically important bank, the slower the growth
rate of its liabilities, the more overcapitalized it will
be, and the higher the growth rate of its liabilities.
Based on the comparative analysis of the
asymmetric characteristics of systemic and non-
systemic importance performance (I), as shown in
Fig. 5.
Fig. 5: Comparative analysis based on asymmetric
characterization of systemic and non-systemic
importance performance (I)
The leverage adjustment rate for financial banks
is lower than the statutory capitalization, and the
adjustment rate for all three capitalization categories
is slower than that for non-systemically important
banks; therefore, the equity versus asset approach is
used more often for systemically important banks
and the asset versus liability approach is used more
often for non-systemically essential banks in the
capital regulation. The adjustment rate of all three
capital adequacy ratios is slower than that of non-
systemically important banks; therefore, the equity
vs asset approach is used for systemically important
banks, and the asset vs liability approach is used for
systemically essential banks in capital regulation.
The slowdown in the growth rate of total assets/total
liabilities is mainly due to the simultaneous decline
in the loan balance and the weighted growth of risky
assets, while the decline in the growth rate of long-
term liabilities is due to the slowdown in the growth
of long-term liabilities. Since banks account for a
substantial proportion of the overall system, each of
their changes can significantly impact the
macroeconomy, and therefore, to ensure their
stability and control their risk weights, financial
banks also actively adjust their risky assets.
Therefore, strengthening the management of risky
assets is an effective way to improve the regulatory
capital ratio. Based on maintaining specific asset
and debt adjustment ratios, they should
appropriately increase the proportion of equity
capital and develop capital adjustment tools in line
with their characteristics to improve the quality of
their funds. On this basis, the change and growth of
capital ratios have slowed, suggesting that banks
will spontaneously reduce their capital ratios to
appropriate target ratios in the context of regulating
overcapitalization. Based on the comparative
analysis of asymmetric characteristics of systemic
and non-systemic importance performance (II), as
shown in Fig. 6.
Fig. 6: Comparative analysis based on asymmetric
characterization of systemic and non-systemic
importance performance (ii)
For systemically and non-systemically important
banks, if their liquidity is too high, they will hold
too many assets of lower risk, lower yield, easy
liquidity, and other types, which may cause a
decline in profitability if they are over-represented.
The higher the index of a bank's systemic
importance, the more significant the gap between its
asset size, type of business, complexity, degree of
affiliation, and service capacity compared to our
systemically important banks. Nevertheless, some
banks may be included in the former category in the
long run because of the rapid growth of this
category and the potential room for advancement
that still exists. However, in the long run, some
banks may be included in the former category
because of this group of banks' rapid growth and
potential to grow. In compressing the coefficients,
samples from previous years are utilized as training
samples, and the sparsity of the data for each year is
analyzed to make the extracted information more
realistic. The Kalman filter corrects the sparse
structure of the signal by iterating to ensure that the
signal is correct. On this basis, a forecasting method
based on historical data is proposed. Therefore, the
author considers this type of bank with systemically
important potential. On this basis, the performance
of our financial banks is evaluated by applying the
improved effectiveness coefficient method.
Financial banks utilize payment systems for cross-
domain operations. A quadratic nonlinear
relationship between the average return on assets,
represented by profit, and risky indicators, such as
capital adequacy and liquidity ratios, is found by
measuring their risk and return. Based on the
comparative analysis of asymmetric characteristics
of systemically and non-systemically important
performance (III), as shown in Fig. 7.
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Fig. 7: Comparative analysis based on asymmetric
characterization of systemic and non-systemic
importance performance (iii)
5 Conclusion
The above comparative analysis of the operating
indicators of systemically important banks and non-
systemically important banks in the past three years
shows that there is a significant difference in
specific financial indicators between these two
groups of banks. However, further analysis shows
that there is no significant difference in overall
operating performance and risk control level
between the two groups. The reason may be that
additional regulatory requirements only have a
positive impact on risk control, while operational
performance depends more on the market behavior
of each banking institution itself. Based on this,
regulatory authorities will continue to closely
monitor the performance of systemically important
banks and guide non-systemically important banks
to strengthen their risk management practices in
future direction recommendations. For systemically
important banks: invest in technology and
innovation to improve operational efficiency and
maintain competitive advantage. For non-
systemically important banks: focus on segmented
markets, strengthen risk management practices, and
explore opportunities for cooperation with
systemically important banks. This study provides
insights into the operational performance gap
between systemically important banks and non-
systemically important banks in China. Further
research can delve into specific areas, such as the
impact of digital transformation, the addition of
financial technology, and the constantly changing
regulatory environment.
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Contribution of Individual Authors to the
Creation of a Scientific Article (Ghostwriting
Policy)
Daodi Yao wrote the main manuscript.
Sources of Funding for Research Presented in a
Scientific Article or Scientific Article Itself
No funding was received for conducting this study.
Conflict of Interest
The authors have no conflicts of interest to declare
that are relevant to the content of this article.
Creative Commons Attribution License 4.0
(Attribution 4.0 International, CC BY 4.0)
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Creative Commons Attribution License 4.0
https://creativecommons.org/licenses/by/4.0/deed.en
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