I
THE IMPACT OF NET INTEREST SPREAD, NET
INTEREST INCOME RATIO AND COST TO INCOME
RATIO ON BANK PERFORMANCE IN CHINA’S
COMMERCIAL BANK
By
Li Yixuan
ID NO:014201400168
A Skripsi Presented to the
Faculty of Business President University
In Partial Fulfillment of The Requirements For
Bachelor Degree in Economics Major in Management
May 2018
I
II
DECLARATION OF ORIGINALITY
I declare that this skripsi , entitled “The Impact of Net Interest Spread,
Net Interest Income Ratio and Cost To Income Ratio on Bank
Performance in China’s Commercial Bank ” is , to the best of my
knowledge and beliefs , an original piece of work that has not been
submitted , either in whole or in part , to another university to obtain a
degree .
Cikarang , Indonesia , May 2018
Li Yixuan
014201400168
III
ACKNOWLEDGEMENT
First of all, I would like to thank the President University for giving me the
opportunity to study here and study my bachelor's degree. The successful completion
of this thesis is inseparable from the care and help of our teachers, classmates and
friends. I am here to express their thanks to the center.
1. Thanks to my adviser Mr. Purwanto, this paper was completed under the guidance
of Associate adviser Mr. Purwanto . adviser Mr. Purwanto profound professional
knowledge, rigorous academic attitude, and excellence in work style have made a
great impact on me.
2. Thanks to my family, I know that without their support there would be no me
today. I also know that I can never fully repay their love, so I hope to successfully
complete my studies and strive for greater success and give them a little comfort.
3. Thanks to my classmates, every detail and every data in this paper is inseparable
from your careful guidance. Next we must continue to work hard in our lives. I
sincerely wish you all the best. Let us work together and cherish it.
4. Thank all the teachers, classmates, and friends who helped me. I thank all those
who have cared and helped me. Without their concern for me, there is no achievement
for me today.
Cikarang , Indonesia , 2018
Li Yixuan
IV
ABSTRACT
In recent years, with the acceleration of interest rate liberalization and the
development of online banking, bank revenue agencies face great challenges, and
Chinese commercial banks are not profitable.
In order to propose suggestions for optimizing the income structure of commercial
banks, this paper focuses on the impact of changes in income structure on the
performance of commercial banks. Based on the analysis of previous research results,
this paper first summarizes the theory of the impact of income structure on business
performance, and then combines theory and practice, analyzes the status quo and
changes of the income structure of China's five listed commercial banks. The second
is the use of spss for empirical analysis of five commercial second-hand data from
2011 to 2016. An empirical study of the impact of China's commercial bank revenue
agencies on their operating performance. The empirical results show that, in general,
the net interest margin income of commercial banks has a positive impact on business
performance, non-interest income also has a great impact on business performance,
and cost income also has a positive impact on commercial banks. Finally, according to
the theoretical and empirical analysis results, suggestions are made for the
optimization of income agencies, such as the rational development direction of
commercial banks, the development of non-interest income, and the strengthening of
internal bank management.
Key words : Performance of commercial banks , Net interest spread ,
Net interest income ratio , Cost to income ratio.
V
TABLE OF CONTENTS
PANEL OF EXAMINERS ..............................................................错误!未定义书签。
APPROVAL SHEET .......................................................................错误!未定义书签。
DECLARATION OF ORIGINALITY ........................................................................... I
ACKNOWLEDGEMENT ........................................................................................... III
ABSTRACT ................................................................................................................. IV
TABLE OF CONTENTS .............................................................................................. V
LIST OF FIGURES ................................................................................................... VII
LIST OF TABLE....................................................................................................... VIII
CHAPTER I ................................................................................................................... 1
INTRODUCTION ......................................................................................................... 1
1.1 Research background ....................................................................................... 1
1.2 Problem Identification ..................................................................................... 3
1.3 Statement of Problem ....................................................................................... 4
1.4 Research Objective .......................................................................................... 4
1.5 Research Limitation ......................................................................................... 5
1.6 Significance of the Study ................................................................................. 5
1.7 Definition of Term ........................................................................................... 6
1.8 Thesis organization .......................................................................................... 8
CHAPTER II ................................................................................................................ 10
LITERATURE REVIEW ............................................................................................. 10
2.1 Theoretical Research ...................................................................................... 10
2.1.1 Return On Assets (ROA) .............................................................. 11
2.1.2 Net Income Spread (NIS).................................................................... 12
2.1.3 Net Interest Income Ratio (NIIR) ....................................................... 13
2.1.4 Cost To Income Ratio ( CIR ) ............................................................. 13
2.2 Previous Research ....................................................................................... 14
2.3 Research Gap ................................................................................................. 17
2.4 Theoretical Framework .................................................................................. 19
2.5 Hypothesis...................................................................................................... 20
CHAPTER III .............................................................................................................. 21
METHODOLOGY ...................................................................................................... 21
3.1 Research Method ........................................................................................... 21
3.1.1 Research Framework .......................................................................... 22
3.2 Hypotheses ..................................................................................................... 23
3.3 Sampling Design ............................................................................................ 24
3.3.1 Population ........................................................................................... 25
3.3.2 Size of Sample .................................................................................... 26
3.4 Research Instrument....................................................................................... 26
3.4.1 Data Collection Instrument ................................................................. 26
3.4.2 Data Analysis Instrument .................................................................... 27
3.5 Operational Definition ................................................................................... 27
3.6 Data Analysis Method ................................................................................. 29
VI
3.6.1 Descriptive Statistic Analysis ............................................................. 29
3.6.2 Classical Assumption Test .................................................................. 30
3.7 Multiple Regression Analysis ........................................................................ 31
3.7.1Hypotheses Testing .............................................................................. 32
CHAPTER IV .............................................................................................................. 34
ANALYSIS AND INTERPRETATION ...................................................................... 34
4.1 Company’s Profile ......................................................................................... 34
4.1.1 HUA XIA BANK ( HXB ) .................................................................. 34
4.1.2 PING AN BANK ( PAB ) ................................................................... 35
4.1.3 CHINA MIN SHENG BANK CORP ( CMBC ) ................................ 36
4.1.4 CHINA MERCHANTS BANK ( CMB )............................................ 37
4.1.5 CHINA CITIC BANK ( ECITIC ) ...................................................... 37
4.2 SPSS 23.0 ....................................................................................................... 38
4.2.1 Descriptive Statistics ........................................................................... 39
4.3 Classical Assumption Test ............................................................................. 40
4.3.1Normality Test ...................................................................................... 40
4.3.2 Heteroscedasticity Test ....................................................................... 42
4.3.3 Autocorrelation Test ............................................................................ 43
4.3.4 Multicollinearity Test .......................................................................... 44
4.4 Hypotheses Testing ........................................................................................ 45
4.4.1 T -Test ................................................................................................. 45
4.4.2 F -Test ................................................................................................. 47
4.4.3 Coefficient of determination ............................................................... 48
4.5 Interpretation of Results ................................................................................. 49
CHAPTER V ............................................................................................................... 53
CONCLUSION AND RECOMMENDATIONS ......................................................... 53
5.1 Conclusions .................................................................................................... 53
5.2 Recommendations .......................................................................................... 54
REFERENCES ............................................................................................................ 55
APPENDIX .................................................................................................................. 58
APPENDIX I ............................................................................................................... 65
RAW DATA ................................................................................................................. 65
VII
LIST OF FIGURES
Figure 2.1 The theoretical framework.......................................................................... 19
Figure 4.1 Histogram ................................................................................................ 41
Figure 4.2 P-plot diagram ......................................................................................... 42
Figure 4.3 Herteroscedascity test .............................................................................. 43
VIII
LIST OF TABLE
Table 1.2 Table of fefinition ........................................................................................... 6
Table 3.1 Research framework .................................................................................... 23
Table 3.2 Listed Commercial Banks ............................................................................ 25
Table 3.3 Operational Definition ................................................................................. 27
Table 4.1 Descriptive statistics .................................................................................... 39
Table 4.2 Autocorrelation test ...................................................................................... 44
Table 4.3 Multicollinearity test .................................................................................... 44
Table 4.4 T- Test .......................................................................................................... 45
Table 4.5 Anova table .................................................................................................. 47
Table 4.6 Coeffiecient of determination ...................................................................... 48
1
CHAPTER I
INTRODUCTION
1.1 Research background
The banking industry in China is ranked among the best in the world banking.
However, as for the overall development of the bank and comprehensive
competition, and look at the existing gaps in China's commercial banks,
especially the differences in the structure of fiscal revenus . The foreign
advanced commercial banks have basically achieved a multiplicity of income
structure, with net interest income and interest income sharing the same
development, especially the net-interest income accounts for the greater
proportion in profit income, further more than the existing commercial banks in
China (Xu,2010) . For example, the development of the non-profit interest
business of Hua xia Bank was very significant, and more than 40% of its income
was received from net-interest income. Although our country’s commercial
banks are very profitable, they are mainly due to higher deposit and loan interest
rates under interest rate control, and interest income accounts for a considerable
part, which is in a long contrast to foreign advanced commercial banks.
Therefore, the income structure of China's commercial banks needs to be
optimized. The background of current income structure adjustments, and studies
the relationship between the income structure of China's commercial banks and
business performance, and further optimizes the income structure of commercial
banks.
The entire interest rate marketization in China has been accompanied by an
increase in the cost of debt of commercial banks and the chances for banks to
obtain low debt have become less and less. The inhalation of deposits is the most
fundamental condition for the issuance of loans. Only the issuance of loan banks
can make profits. Therefore, in the case that deposits are difficult to obtain,
2
banks are concerned about net-interest income business(Wei,2015).
There are usually two ways of understanding the marketization of interest rates.
One is that the central bank adjusts the macro-economy through the money
market on the basis of the benchmark interest rate. The other is that the financial
institutions change according to their own needs in the financial market of the
financial situation, to automatically determine the price and the flow wtih the
allocation of funds where are needed the most (Zhou,2016).
Based on Sun (2016) , the changes in the bank’s liability costs from the
perspective of the composition of bank liabilities and changes in the structure of
liabilities during the process of interest rate liberalization. The following will be
from the composition of the bank's debt and interest rates in the process of
changing the debt structure point of view Departure, to explore the changes in
the cost of bank liabilities, the composition of the bank debt mainly includes the
following sections:
1. Deposited funds, the interest rate of this part of the debt is regulated, followed
by the bank’s financial products, is the interest rate.
2. Disguised marketization; once again for the same industry liabilities, this part
of the debt is obtained in the interbank lending market.
3. The interest rate is also market-oriented, and finally the bondsissued, this part
of the source of the cost of debt is high, but the proportion is the lowest, only
2%, the entire cost of interest-bearing debt has little effect. In fact, discussed
here.
4. The debt cost of a bank is a combination of the cost of four sources of
liabilities, the cost of the portfolio is from a single source of debt the structure
of the interest rate and the source of liabilities are determined.
3
The proportion of this debt increased, and the proportion of low-cost liabilities
decreased. Rising interest rates mean commercial bank costs rise of domestic
banks that rely heavily on interest income is a hard hit , so in this under the
circumstance, commercial banks looking for a new outlet have become
inevitable, but they have begun to attach importance to non-interest income
business and increase investment becomes a new method for banks to improve
their performance(Wei,2015).
1.2 Problem Identification
Based on Wang (2008), in recent years, the marketization of interest rates in
China and internet finance pose a great threat to the development of commercial
banks. In summary, the impact of internet finance on commercial banks is not
only reflected in the deposit structure of banks.As a threat, the cost of deposits
has greatly increased, and it has changed people’s dependence on banks in
payments.The key link in the loan review is the construction of a credit system
that poses a threat to commercial banks. So, in this under the current situation,
the development of net-interest income business of banks is particularly
important, and the bank’s electronic and network.The operational efficiency of
the channel, quickly tapping the needs of customers through big data and fully
integrating customer financial management, financing and settlement
requirements, providing customers with a package of financial services,
increasing banks in net-interest business.Face the income, and then improve the
bank's performance.
The rising cost of bank's interest business, also the limited source of deposits,
whether banks can find new development space in addition to interest income
business, become the key to the future development of the bank, in terms of
net-interest income business There is still a big gap between China's commercial
banks and commercial banks in developed countries, and China’s commercial
4
banks account for a relatively low proportion. Therefore, the purpose of this
paper is to investigate whether domestic banks’ net-interest income business can
provide more room for the improvement of commercial bank performance
(Chi,2016) .
1.3 Statement of Problem
Based on the problem identification, the researcher wants to find therelationship
between net income spread and bank performance,andrelationship between bank
net-interest income ratio and bank performance is positively related, and the
relationship between cost to income ratio and bank performance, are these three
components constituted by other factors?
Therefore, the statement of problem was listed as below:
a) Is there a significance influence of Net Interest Spread(NIS) toward return on
assets of china commercial banks?
b) Is there a significance influence of Net Interest Income Ratio (NIIR) toward
return on assets of china commercial banks?
c) Is there a significance influence of Cost to Income Ratio (CIR) toward return
on assets of china commercial banks?
d) Is there a significance simultaneous influence of net interest spread , net
interest income ratio , cost to income ratio towards ,Return on assets of china
commercial banks?
1.4 Research Objective
This research will discuss and get the answer about the Impact of China
Commercial Banks' Income Institutions on Bank Management
Performance.Moreover, the data is from the year 2011-2016 yearly, the total is 5
years data. During this research, the research objective is tending to:
a) To analyze the influence of Net Interest Spread(NIS) towards retuen on assets
5
in China commercial banks.
b) To analyze the influence of Net Interest Income Ratio(NIIR) towards retuen
on assets in China commercial banks.
c) To analyze the influence of Cost to Income Ratio(CIR) towards retuen on
assets in China commercial banks.
d) To analyze the simultaneous influence of net interest spread , net interest
income ratio , cost to income ratio towards retuen on assets in China
commercial banks.
1.5 Research Limitation
Scope and Limitation of this research are:
a) The shortcoming of this paper is that due to the difficulty of obtaining the
data of more banks, there is a big flaw in the number of samples and the
regression analysis can not be done more fully.
b) This article studies China Commercial Bank and studies based on quarterly
data from 2011 to 2016.
1.6 Significance of the Study
The researchers hope that the program has what is important:
1. Student:
This article is about the study of the impact of China's commercial bank
income agencies on bank operating performance. It can help students who
want to understand the performance of China's commercial banks. Can
provide information during their study.
2. Bank researchers:
This article will help them understand the relationship between commercial
banks' revenue business and performance.
3. Researchers:
6
Through the research of the researcher, we can understand the valuable
reference for the type and quantity of business.
1.7 Definition of Term
Table 1.2 Table of fefinition
Name of Term Definition
Commercial Bank
Commercial Bank is a type of bank, whose
responsibility is to undertake credit-mediated
financial institutions through deposits, loans,
exchanges, savings and other businesses. The main
business scope is to absorb public deposits, issue
loans and handle discounted bills. General
commercial banks do not have the right to issue
money. The traditional commercial banks' business
mainly focuses on deposits and loans.( Duan , 2011)
Return on Assets
Return on Assets (ROA), also known as Return on
Assets (ROA), is a measure of how much net profit is
created per unit of assets. A useful indicator for
assessing a company's profitability relative to its total
asset value. The return on assets is a comprehensive
reflection of the effects of the use of corporate assets.
It is also an important indicator for measuring profits
made by companies using the total amount of claims
and their owners' equity.(Hao ,2016)
7
Net interest spread
The net interest spread is the difference between the
average interest rate of interest-earning assets and the
average cost of interest-bearing liabilities.The net
interest spread is a measure of bank operating
efficiency and reflects the relationship between bank
interest income, interest costs, and total assets. It
reflects the efficiency of the use of funds by banks to
use their existing assets to conduct business, and also
reflects the level of bank operating efficiency and the
ability to conduct business at a certain
level.(Xu ,2010)
Net interest income ratio
The use of net-interest income ratio as a relative
indicator to represent the net-interest income variable
is intended to exclude the influence of other variables
in the variable selection and is more credible than the
absolute index.(Jiang,2012)
Cost to income ratio
The cost-to-income ratio is the ratio of bank operating
expenses to operating revenue. It reflects how much
cost each unit of the bank needs to pay. The lower the
ratio, the lower the cost of bank unit revenue and the
stronger the bank's ability to obtain income.
Therefore, the cost-to-income ratio is an important
measure of the profitability of a bank.(Li,2009)
Source : Adjusted by researcher, 2018
8
1.8 Thesis organization
This paper mainly studies the relationship between bank net-interest income and
bank performance, which is the first to study the bank currently facing
The development background and the definition and interpretation of the
net-interest income business of domestic commercial banks,
Based on this, based on the regression analysis of 34 annual reports of
commercial banks, to study the net-interest income is whether to promote bank
performance. The full text is divided into five chapters:
CHAPTER I
The main content of the first chapter is to first explain the background of this
study, that is, to speed up the process of interest rate marketization.
The rapid development of Internet finance and the trend of integrated banking
operations are obvious, and the content of previous research Summarize and
explain the innovations and deficiencies of this paper.
CHAPTER II
The second chapter mainly introduces the theoretic reference for this study, As
well as the new connotation of the net-interest income of the banks studied in
this paper.
CHAPTER III
The third chapter mainly explains the selection and source of variables based on
two aspects, proposes hypothetical problems, and proposes verification methods
to demonstrate and explain.
CHAPTER IV
The fourth chapter conducts empirical analysis, measures the bank’s
performance, selects indicators, and line description, describes the samples and
9
data sources for empirical analysis, and selects measurement methods that are
consistent with the model construction method and software for regression
estimation, further analysis of net-interest income and bank performance through
regression results the relationship between.
CHAPTER V
The fifth chapter proposes countermeasures for the development of net-interest
income in banks according to the regression results in Chapter IV.
10
CHAPTER II
LITERATURE REVIEW
2.1 Theoretical Research
The influence of commercial bank revenue agencies on the performance of bank
operations does indeed exist. The earliest point of view was the hypothesis of
supervision and hypothesis that Berger and Udell (1993) represented. The theory
believes that the banking industry must achieve long-term and stable
development, and effective supervision and control is indispensable. However,
unlike the interest business, commercial banks do not make provisions for
net-interest business and do not use bank’s own funds. The rich variety of
business features makes it more difficult for banks to monitor. Therefore, the
safety of commercial banks will increase due to net-interest income (Wang,
2013).
Based on He (2014) ,the second research object, whether it is a domestic or a
foreign commercial bank, has different development history of different banks,
Business scale and types are different, which resulted in differences in income
structure of commercial banks, finally, research methods, some scholars from the
theoretical point of view to the qualitative analysis, there are some scholars are
combined with the actual development of the current through the establishment
of data mathematical models to analyze the differences in research methods will
also affect the final results of the research.
In this article will make reasonable considerations in the research period,
research content, and research methods. In the final chapter, the research
methodology of theoretical analysis and empirical analysis was used to deeply
discuss the impact of the structure of China's commercial banking income
11
structure on its operational performance, and provide suggestions and opinions
for optimizing the commercial bank's income structure.
There are also investment gains on commissions and investments in securities
accounts provided in the capital market; and other net-interest income.
Therefore, in order to better reflect the results, this article uses Return on assets
for Y . Net interest spread, Net interest income ratio, Cost in income ratio as the
for X.
2.1.1 Return On Assets (ROA)
Return on asset (ROA ) is the ratio of the company's net profit to the average
total assets over a certain period of time. The higher the return on asset, the
stronger the profitability of the company's use of all assets; the lower the return
on asset, the weaker the profitability of the company's use of all assets. Return on
asset is proportional to net profit and inversely proportional to the average total
assets. Return on asset is the most important indicator of the owner's equity
profitability. It is highly comprehensive, and return on asset depends on the sales
net profit rate and the asset turnover rate (Yan ,2013 ).
Based on Liu (2012) , return on asset is mainly used to measure the ability of
companies to use assets to obtain profits, reflects the efficiency of the use of the
company's total assets, and indicates that the number of net profits the company
can obtain per unit of assets. The higher the ratio, the stronger the profitability of
all assets of the company. This index is proportional to the net profit rate and
inversely proportional to the average total assets component.
This paper uses total return on assets as a measure of bank profitability in
addition to the previous article. There are other reasons for using it. For example,
some commercial banks have negative equity in certain years, so the return on
assets the quality of the rate will be greatly affected, and the return on assets of
12
commercial banks will not appear this problem, because the total assets can not
be negative, so the return on assets, that is, the quality of ROA can be well
guaranteed.
Annual Net Income
Return On Asset= %100
Total Assets
(Eq. 1)
2.1.2 Net Income Spread (NIS)
In China's commercial banks, the net income spread as a comprehensive
indicator to measure the level of cost and income is of great significance to the
operation and management of commercial banks.The net interest spread is a
measure of bank operating efficiency and reflects the relationship between bank
interest income, interest costs, and total assets. It reflects the efficiency of the use
of funds by banks to use their existing assets to conduct business, and also
reflects the level of bank operating efficiency and the ability to conduct business
at a certain level(Lou ,2008).
In the study of this paper, the researchers found that with the gradual depending
of the financial marketization reform and the depending of the degree of
openness to the outside world, major domestic commercial banks have
successively completed or started the process of listing reform, and the overall
profitability and competitiveness of China's banking industry have increased
substantially.
(Eq. 2)
Net interest income
Net Income spread= %100
Total assets
13
2.1.3 Net Interest Income Ratio (NIIR)
The use of net interest income ratio as a relative indicator to represent the
net-interest income variable is intended to The impact of other variables, such as
total operating income, total asset size, and total equity scale, was removed from
the variable selection. More credible than absolute indicators.Introducing the
proportion of net interest income ratio into the efficiency impact relationship
analysis model means to reveal the impact of income structure on the income
efficiency of China commercial banks(Deng,2006).
The impact of net interest income ratio structure on the operating performance of
commercial banks in China is still not clear. In the China periodical database
from January 1994 to March 2005, such valuable documents could not be
retrieved. If this relationship can be determined, the Bank of China can adjust its
output structure in a targeted manner and promote efficiency(Wang ,2008).
(Eq. 3)
2.1.4 Cost To Income Ratio ( CIR )
Chi (2006) Research on the “Relationship between Income Structure and Income
Efficiency of Commercial Banks in China” The cost-to-income ratio is the ratio
of the bank's total operating cost to the total operating revenue, and reflects the
level of total cost consumed by the bank when it conducts business, which to a
certain extent reflects the efficiency of bank operations and the utility of
operating costs.
Net Interest Income
Net interest income ratio = %100
Every income
14
In this paper, the business performance of commercial banks is not only
dependent on income, but also has an inseparable relationship with its cost (Wei,
2010).
In simple terms, business performance is the difference between income and cost.
The cost-to-income ratio reflects the level of bank operating performance, and
the general cost-to-income ratio is inversely proportional to the bank's operating
performance. Therefore, in order to improve their own operating performance
and operating efficiency, banks should control costs, minimize costs, and have
the best results.
Operating expense
cost-to-income ratio = ×100
operating revenue
(Eq. 4)
2.2 Previous Research
The previous thesis research basically studied the impact of net-interest income
on the bank's performance, but it rarely studied the effect of the bank's income
agency on the bank's performance. The main innovation in this paper is the
selection of model variables. This paper selects innovations in analyzing
variables. This article will select return on assets as the explanatory variable, and
net income spread , net interest income ratio, cost to income ratio will be used as
explanatory variables. Through these four variables, the impact of these different
components on the performance of commercial banks was analyzed.
In terms of data selection, based on the accuracy, completeness of the data, and
the relative unity of the indicators, the monthly data from 2011 to 2016 were
selected for empirical analysis, and the accuracy and comparability were
relatively high. In order to match the selection of the data with the current
domestic bank pattern, in the bank's choice, the four-year total data of the five
15
banks Hua Xia Bank ,Ping An Bank ,Min Sheng Bank , China Merchants Bank ,
China Citic Bank will be selected for monthly analysis. Analyze the relationship
between them by analyzing the results of the regression.
In previous studies, the effect of bank revenue agencies on business performance.
The researchers completed the following tasks:
1.Zheng and Hong. (2007) conducted the research“ financial data of 14 listed
commercial banks in China from 1996 to 2000, a panel model was
established” Using quantitative analysis methods, results are obtained through
model analysis.it was concluded that the increase in the scale of net-interest
income would reduce the profitability of commercial banks and improve the
Business risk.
2. Yao (2001) conducted the research“used 10 years of data from 1 to 4 listed
commercial banks in 2000 as a sample, and ROA as an explanatory variable
of bank performance to study commercial bank and bank performance” The
relationship between net-interest income does not significantly improve bank
performance and is not conducive to the stable operation of commercial banks.
3. Sheng and Wang (2014) conducted the research “conducted empirical studies
on domestic commercial banks”selected 14 domestic commercial banks'
financial data from 2003 to 2007, used ROA and ROE as explanatory
variables, and accounted for net-interest income and total assets. The
logarithm, as an explanatory variable, was subjected to a regression analysis,
which proved that with the increase in the proportion of non-interest income of
commercial banks, the performance of banks will also increase.
4. Li and Yan (2012) conducted the research “studied the financial data of 16
domestic listed banks from 2005 to 2012” used the total return on assets as
16
an explanatory variable. The explanatory variable was the proportion of
net-interest income, and it was the first in the model. Taking into account the
control variables, and using the operating expense ratio and total asset size as
the control variables for regression analysis, the analysis shows that the
increase in net-interest income can improve the bank's performance.
5. The study by Stiroh (2004) conducted the research “Empirical Research on
Community Banks” The selected sample data was the community bank data
from 1984 to 2000, and conducted regression analysis. In the regression model,
risk-adjusted return on assets was used as the explanatory variable. It was
found that the increase in net-interest income increased the risk-adjusted net
asset rate of banks. On this basis, he also adopted the holding company of the
Bank of America as the research object and selected the data from the Bank of
America Holding Corporation from 1997 to 2004 for regression analysis. It is
found that those companies that do not rely on non-interest business rely
heavily on the benefits of poor performance and low performance.
6. The study by Wang and Zhou (2008) conducted the research “analyzed the
financial data of 12 domestic listed commercial banks in China” In order to
increase the sample size using the financial data from 1999 to 2006, the capital
ratio was added to the model in the construction of the model for regression
analysis. The study concluded that the bank's net-interest income is
significantly negatively related to the bank's performance. The article analyzes
the ways in which net-interest income affects bank performance during the
process of expounding.
7. The study of Tian (2013) “Net interest spread impact on business
performance” used the financial data of the domestic 16 commercial banks
from 1998 to 2012 to introduce more financial data in bank statements and
established a bank’s net profit as an explanatory variable, payable employee
17
compensation, and net fixed assets. The bank's cost-to-income ratio,
net-interest income ratio, and bank's non-performing loan ratio were modeled
as explanatory variables. The final conclusion was that the bank’s
performance varies with banks’ net-interest-income and income-to-subsidiary
business ratios , and the bank's net-interest income business can not become
the dependence of the bank's long-term development.
2.3 Research Gap
With regard to the influence of the bank's income agencies on the performance of
commercial banks, the research work of foreign scholars is rather profound. Due
to the late start of China's non-profit interests business, the researchers in the
study are not mature enough to study this issue. Although scholars have studied
the influence of income agencies on bank performance in recent years, domestic
scholars have not yet reached a unified Wang (2008). Research on the “Impact of
Net-interest Income and Its Composition on the Performance of China's Listed
Banks” Regarding the meaning of financial innovation, there is no unified
explanation in China and abroad. Generally speaking, financial innovation has a
broad and narrow sense. Broadly speaking, financial innovation refers to the
development and change of commercial banks in the financial, financial, and
financial markets, and other macroscopic and microscopic fields. The narrow
sense of financial innovation refers to the fact that in the 1990s, the western
developed countries implemented financial innovation, followed by innovations
involving financial instruments, financial services, financial institutions, and
management systems. Because the angles of analysis are different, many
innovation theories have been formed.
In past studies, there are some factors that affect the performance of a bank’s
operations.
18
1) Yan (2013) Research on the“Net-interest Income Affects the Performance of
Commercial Banks in China’’ The first level is from the macro level. Financial
innovation at this level actually speaks of the fact that the entire human financial
history is a process of continuous financial innovation. Every great development
in the history of financial development.They are all driven by financial
innovations. Such financial innovations span a relatively long period of time and
spread across a wide range.
2) Zhang (2010) Research on the “Commercial bank risk: scale and non-interest
income. financial Research” The second level of financial innovation is from the
perspective of financial institutions. The process of financial institution function
change, cost reduction and risk transfer is the process of financial innovation.
The process of banks building a more efficient and more comprehensive
institution It is the process of financial innovation.
3) Liu (2012) Research on the “Relevance of Business Diversification,
Operational Performance and Risk of China's Commercial Banks - Research on
International Finance” The third level of financial innovation refers to the
innovation of financial instruments. Such financial innovation is based on
micro-specific financial products and services. Such products basically include
credit re-innovation and risk transfer innovation. Innovations in creditor's rights
and equity creation, such innovative products as bill issuance facilities, currency
swaps, convertible bonds, etc.
For commercial banks, commercial banks carry out net-interest business on the
basis of the original traditional credit business, which is also a manifestation of
financial innovation, by the analysis of constraint-induced innovation theory,
changes in the market environment have caused the profitability of commercial
banks to be seriously affected. Under the circumstances of sending seed,
commercial banks have to expand net-interest business to earn more profits;
19
According to the theory of transaction costs, net-interest business of commercial
banks can increase the unit cost expenditure effect and reduce the overall
transaction costs of banks (Zhou ,2016).
2.4 Theoretical Framework
This paper examines the impact of planned collection of data on the impact of
China's commercial bank revenue agencies on business performance. In this
study, researchers will carefully provide the final results. According to this
research topic, researchers will use quantitative methods to analyze data, and
dependent variables and independent variables are shown below the chart:
X1
X2 Y
X3
Figure 2.1 The theoretical framework
Source : Adjusted by previous research, 2017
Independent variable Dependent variable
Net income spread
Net interest income ratio
Cost to income ratio
Return on asset
20
In the text dependent variable is return on assets ( ROA ) for Y. Independent
variable have there , are respectively is Net interest spread ( NIS ) for X1,
Net interest income ratio ( NIIR ) for X2,Cost to income ratio (CIR ) for X3.
2.5 Hypothesis
The hypothesis is generated via a number of means, but is usually the result of
a process of inductive reasoning where observations lead to the formation of a
theory. This research is used to analyze the impact of net interest spread, net
interest income ratio, and cost to income ratio on the operation of commercial
banks.
The following hypotheses:
1. Hypothesis 1: There a significance influence of net interest spread
toward return on assets of china commercial banks.
2. Hypothesis 2: There a significance influence of net interest income ratio
toward return on assets of china commercial banks.
3. Hypothesis 3: There a significance influence of cost to income ratio toward
return on assets of china commercial banks.
4. Hypothesis 4: There a significance simultaneous influence of net interest
spread , net interest income ratio , cost to income ratio
towards ,Return on assets of China commercial banks.
21
CHAPTER III
METHODOLOGY
3.1 Research Method
Quantitative Research is used to quantify the problem by way of generating
numerical data or data that can be transformed into usable statistics. It is used to
quantify attitudes, opinions, behaviors, and other defined variables – and
generalize results from a larger sample population.
Quantitative Research uses measurable data to formulate facts and uncover
patterns in research. Quantitative data collection methods are much more
structured than Qualitative data collection methods. Quantitative data collection
methods include various forms of surveys – online surveys, paper surveys,
mobile surveys and kiosk surveys, face-to-face interviews, telephone interviews,
longitudinal studies, website interceptors, online polls, and systematic
observations (Duan, 2011).
Quantitative studies use data that is constructed in digital form or that can be
immediately converted to numbers. This research methodology involves the use
of statistical and mathematical tools to analyze frequency data.
It uses data to prove or disprove a concept or hypothesis. Quantitative research is
based on the numbers of people with calculative methods and approaches.
Formulas are the commonly tools that used to analyze the data. The result are
usually the difference between qualitative methods by statistics, tables and charts.
For example, it uses statistical tools, mean ratings, correlations, regression
analysis the data (Wang,2013).
In this research, a quantitative approach was used as an appropriate method to
address the research goals and use data and statistics.In this research, the
22
researcher uses quantitative method with secondary data as the type of data.
Secondary data is the data that has been already collected by and readily
available from other sources. Such data are cheaper and more quickly obtainable
than the primary data and also may be available when primary data cannot be
obtained at all (Xu, 2010).
This researcher is used statistical data. The data was manipulated, summed up
and reduce to provide the necessary information to answer research questions
and research issues. Researchers start to analysis data when all of data was
collected, the research was looked deep into the general factors causes of
non-performing loans in china commercial banks, for which data are taken from
the financial statement of Bank of china , Hua Xia Bank , Ping An Bank , Min
Sheng Bank , China Merchants , China Citic Bank .
3.1.1 Research Framework
The main purpose of the study is whether the development of banks' net-interest
income can provide more room for the development of domestic commercial
banks and clearly define the net-interest income business. The study of the entire
topic is of great significance.
23
Table 3.1 Research framework
Source: Adjusted by Researcher, 2017
3.2 Hypotheses
A research hypothesis is the statement created by researchers when they
speculate upon the outcome of a research or experiment .
Ho1:β1=0 There net interest spread has no significant effect on return on assets.
Ha1:β1≠0 There net interest spread has significant effect on return on assets.
Journal and Literature Review
Construct the Hypothesis
Data Computing by Statistic Tool
Data Anaysis and Interpret results
Analysis
with SPSS
Conclusion and Recommendation
Formulate the Problem
24
Ho2:β2=0 There increase in bank net interest income ratio has no significant
effect on return on assets.
Ha2: β2≠0 There increase in bank net interest income ratio has a significant
effect on return on assets.
Ho3:β3=0 There cost to income ratio has no significant effect on return on
assets.
Ha3: β3≠0 There cost to income ratio has significant effect on return on assets.
Ho4:β4=0 There simultaneous of net interest spread, net interest income ratio,
cost to income ratio has no significant effect on return on assets.
Ha4:β4≠0 There simultaneous of net interest spread, net interest income ratio,
cost to income ratio has significant effect on return on assets..
3.3 Sampling Design
Sampling design is part of statistical methodology and involves a part of
population. The researcher can use the statistical analyze to generalize the entire
population if a sampling is done correctly. Probability and non-probability
sampling are two main types of sampling design (Yan, 2013).
Non-probability sampling is based on the subjective experience of the
investigators to select from the overall sample sampling methods that are judged
as the most representative units (Deng ,2006). It can reflect the characteristics of
population, and it is a fast, easy and economical way to collect data. This method
can be used when the researcher has a good general understanding of the
population, or when the overall population is too large, complex, and
probabilistic and has difficulty to use probability sampling. Therefore, in this
research, the researcher uses non-probability sampling. There are twelve national
joint-stock commercial banks in China. This paper will select five of them to
25
conduct analysis and demonstrate . The selected samples for this research are
explained as follows:
1. Commercial banks listed on the Shanghai Stock Exchange.
2. Five Chinese banks . 2011-2016 every half year. Annually publish its own
financial report.
According to the above criteria. This article studies sample selection:
Return on assets for dependent variable . Use net net income spread ,net interest
income ratio , cost to income ratio for independent variable.
3.3.1 Population
The population is a generalized area consisting of objects / subjects that have
certain traits and characteristics that are determined by the resarcher to study
and then got the conclusions (Jiang, 2012).
Table 3.2 Listed Commercial Banks
TOTAL
120
Data
Source: Adjusted by researcher based on the Sina finance website (2017)
NO NAME ABBREVIATION ESTABLISHED
DATE
1 PING AN BANK PAB 1995
2 HUA XIA BANK HXB 1992
3 MIN SHENG BANK CMBC 1996
4 CHIN MERCHANTS BANK CMB 1987
5 CHINA CITIC BANK ECITIC 1987
26
3.3.2 Size of Sample
Given the fact that the scale of development of China's commercial banks in the
country is relatively large in the overall banking industry, this section mainly
examines the revenue structure of five China-based commercial banks in
2011-2016, which in turn reflects the development status of the current
state-owned banking industry. There are four variables in this study return on
assets , net interest spread , net interest income ratio , cost to income ratio .
3.4 Research Instrument
3.4.1 Data Collection Instrument
Research Instrument is the tool used to answer research questions which
described in the previous chapter. The researcher's intent is to gather information
from a variety of sources.
According to previous literature, researchcan know. There are two types of data.
One is the main data, and the other is the secondary data.
Raw data refers to the straightforward acquisition through interviews, inquiries,
questionnaires, and measurement. The collection of first-hand data can solve the
pending problems. The original data is collected by the company and belongs to
the company, so it is easy to keep confidential.
Secondary data has the advantages of being quick, low-cost, easily accessible,
and capable of laying the foundation for further raw data collection. It is usually
easier to obtain. The purpose of using secondary data is very easy to access,
convenient, and will save time and cost. Sources of secondary data are usually
official statistics of government agencies, technical reports, academic journals,
literature reviews, and online data (Jiang,2012).
27
3.4.2 Data Analysis Instrument
SPSS 23.0 was used to study statistics in this study:
1. The analysis results of SPSS for Windows are clear and intuitive.
2. SPSS version 23.0 helps researchers numerically measure the data collected
and entered, and helps researchers analyze the data from the conclusions. It
also helps researchers to identify the impact/relation/correlation between
independence and dependent variables.
3.5 Operational Definition
An operational definition, when applied to data collection, is a clear, concise
detailed definition of a measure. The need for operational definitions is
fundamental when collecting all types of data . Operational Definition Identifies
one or more specific, observable events or conditions such that any other
researcher can independently measure and/or test for them (Zhou, 2016).
Table 3.3 Operational Definition
No. Variable
s
Definition Measur
ing
Scale
Formula
1 ROA Commercial banks have
negative assets in certain
years, and the quality of
asset returns will be
greatly affected.
Ratio
Annual net income
%100
Total assets
28
2 NIS The net interest spread of
returnas a comprehensive
indicator to measure the
level of cost and income
is of great significance to
the operation and
management of
commercial banks.
Ratio
Net interest income
%100
Total assets
3 NIIR The proportion of net
interest income ratio into
the efficiency impact
analysis model means to
reveal the impact of
income structure on the
income efficiency of
China commercial banks.
Ratio
Net interest income
%100
Every income
4 CIR The cost-to-income ratio
is the ratio of the bank's
total operating cost to the
total operating revenue,
and reflects the level of
total cost consumed by
the bank when it
conducts business.
Ratio
Operating expense
%100
Operating revenue
Sources: Adjusted by Researcher, 2017 based on Liang Qiu xia (2012)
29
3.6 Data Analysis Method
3.6.1 Descriptive Statistic Analysis
Descriptive statistical analysis of these results is a general description and
therefore cannot be used for conclusions.
It is used to describe and summarize data information for each variable,
including calculating the mean, maximum, minimum, and standard deviation of
each variable(Zhou, 2016).
a. Mean
Average formula :
b. Standard deviation
30
3.6.2 Classical Assumption Test
1.Normality test
The test is done by looking at the spread of data on the diagonal graph. If the
distribution is normally distributed, the line representing the actual data
distribution immediately follows the diagonal, which means that the regression
model conforms to normal assumptions (Hao, 2016).
Normality is used to test whether the regression model used to test independent
variables and independent variables has a normal distribution. The best
regression model is normal distribution or near normal distribution. Normal tests
typically use test chart histograms and P-P graphs to test regression model
residuals.
2. Heteroscedasticity Test
The heteroscedasticity test is designed to test whether there is inequality in the
regression model of the residuals (Liu, 2012).
A good regression model is happen to homoskedasticty while the bad model is
happen to heteroscedasticity.
3. Autocorrelation Test
Autocorrelation means that there is a correlation between the expected values of
the random error term. There are autocorrelation or sequence correlations
between random error terms, which were proposed in 1972.
The Durbin Watson test reports a test statistic, with a value from 0 to 4, where:
2 is no autocorrelation.
0 to <2 is positive autocorrelation .
>2 to 4 is negative autocorrelation .
A rule of thumb is that test statistic values in the range of 1.5 to 2.5 are relatively
normal. Values outside of this range could be cause for concern (Stephanie,
2017).
31
4. Multicollinearity Test
The multicollinearity test was designed to test whether the regression model
found correlations between independent variables (Chi, 2016). 1) Absolute
indicators are converted to relative indicators; 2) Nominal data is converted to
actual data.
From the reference literature, if the tolerance is <0.2 or VIF> 10, there may be
multiple collinearity problems between the independent variables.
3.7 Multiple Regression Analysis
Multiple regression analysis conducted to find out how far the dependent
variable can affected to the independent variable. In multiple regressions analysis,
there is one independent variable and more than one dependent variables (Duan,
2011).
The multiple regression formula used in this research to assist and support the
results between the dependent variable and four independent variables the
equation as follows:
Where:
Y = Bank return on assets
β0 = the slope (beta coefficient) which is constant
β1 = the slope (beta coefficient) of Net income spread
X 1 = Net income spread
β2 = the slope (beta coefficient) of Net interest income ratio
X 2 = Net interest income ratio
β3 = the slope (beta coefficient) of Cost to income ratio
X 3 = Cost to income ratio
ɛ = random error
32
3.7.1Hypotheses Testing
1. T-Test
T-test is used to determine the partial relationship between each independent
variable (coefficient) and dependent variable. The null hypothesis is that the
coefficient of X is 0. The test result (the significance level α used is 0.05) of the
research is to reject or accept the null hypothesis (H0), while the alternative
hypothesis (HA) is contrary to the null hypothesis assumptions. Therefore, the
assumptions are as follows:
1. H 01: β 1 =0 or if significant value> α, do not reject H 0.
(There increase in bank net interest spread has no significant effect on return
on assets.)
H A1 : β1≠0 or if significant value<α, reject H 0
(There increase in bank net interest spread ratio has a significant effect on return
on assets.)
2. H 02: β 2 =0 or if significant value> α, do not reject H 0
(There net interest income ratio has no significant effect on return on assets.)
H A2 : β2≠0 or if significant value<α, reject H 0
(There net interest income ratio has a significant effect on return on assets.)
3. H 03: β 3 =0 or if significant value> α, do not reject H 0
(There cost to income ratio has no significant effect on return on assets.)
H A3 : β3≠0 or if significant value<α, reject H 0
(There cost to income ratio has a significant effect on return on assets.)
33
2. F-Test
Simultaneour teseting using the F-test, also called simultaneous significance
testing. It is usually used to analyze statistical models that use more than one
parameter to determine whether all or some of the parameters in the model are
suitable for estimating the population.
The step to be taken in testing is construct the null hypothesis and alternative
hypothesis (HA).
H04: β=0 or if significant value>α,there is no simultanous significant influence
of the independent variable towards on the retuen on assets.
HA4: βi≠0 or if significant value<α,there is simultanous significant influence
of the independent variable towards on the retuen on assets.
With the conditions:
If the probability> 0.05 and then the H 04 is accepted and HA4 is rejected.
If the probability< 0.05 and then the H 04` is rejected and HA4 is accepted.
3. Coefficient of determination (R²)
The coefficient of determination is a measure used in statistical analysis that
assesses how well a model explains and predicts in the dependent variable. The
coefficient of determination is between zero and one. The coefficient of
determination, also commonly known as "R-squared," is used as a guideline to
measure the accuracy of the model.
34
CHAPTER IV
ANALYSIS AND INTERPRETATION
4.1 Company’s Profile
4.1.1 HUA XIA BANK ( HXB )
Hua Xia Bank was established in Beijing in October 1992.( Referred to as HXB).
In march 1995, it implemented shareholding system reform. It is a joint-stock
bank. In march 1995, the joint-stock reform was implemented; in september
2003, the company first issued public shares and was listed (stock 600015),
becoming the fifth listed bank in the country; in october 2005, it successfully
introduced deutsche bank as an international strategic investor; In october and
april 2011, two non-public offerings were successfully completed.
By the end of september 2013, the agency network had spread to 320 cities in
110 countries and regions on five continents, and established a settlement
network covering major trading areas in the world; total assets reached 155.819
billion yuan, and the overall profitability improved rapidly, resulting in a
significant improvement in asset quality. The business structure has been
significantly optimized and the operating efficiency has been improved rapidly,
maintaining a good momentum of development.
At the end of 2016, Hua Xia Bank's total assets were 2.356 trillion yuan and
operating income was 64.025 billion yuan. A total of 40 first-tier branches, 53
second-tier branches, 886 business offices were established, and the number of
employees exceeded 39,000 people.
35
In february 2017, in the british banker magazine's ranking of “Global Banking
Brands Top 500”, Hua Xia bank ranked 71st, ranking 15th in China-funded
banks, with a brand value of 3.473 billion US dollars, an increase of 23%
year-on-year. In July 2017, Huaxia bank ranked 67th in terms of Tier 1 capital
and 70th in total assets in the Top 1000 Global Bank List published by the British
Banker magazine,at the same time, Hua Xia bank ranked 103rd in the 2016
fortune 500 china.
4.1.2 PING AN BANK ( PAB )
Ping An Bank, which is also known as Ping An Bank Co., Ltd., has 34 branches
across China and a representative office in Hong Kong. As of the end of June
2013, the total assets of Ping An Bank amounted to RMB 1,892.998 billion, total
deposits amounted to RMB 1,137.361 billion, and total loans (including
discounted expenses) amounted to RMB 7,864.84 million.
Ping An, through its professional subsidiaries and departments, provides a full
range of personalized financial products and services for more than 70 million
customers of a single brand, such as insurance, banking and investment. Ping An
banking service is an important pillar of Ping An bank. Ping An bank has 28
branches and more than 400 outlets, has representative offices in Beijing and
Hong Kong, and has established agency relations with more than 600 banks in
overseas countries and regions.
Ping An Bank has launched dozens of new international business models such as
forfaiting, risk sharing, and acting on behalf of other banks. It has developed
nearly 500 agency agencies worldwide. As a primary dealer of the People's Bank
of China (PBOC) open market, the transaction volume of foreign currency
currency markets and bond markets has doubled year after year. It has ranked
36
among the top 20 member banks of the national inter-bank bond market and has
been rated as an outstanding transaction settlement unit. From July 1, 2009, Ping
An bank launched debit card global ATM withdrawals for personal customers,
free personal internet banking remittance, and non-customer fault personal
banking losses by Ping An Bank indemnity and other 3 e-banking service
commitments, service standards Set a new high in the industry.
4.1.3 CHINA MIN SHENG BANK CORP ( CMBC )
China Minsheng bank was officially established in Beijing on January 12, 1996.
It is China's first national joint-stock commercial bank that was mainly
established by private enterprises. Minsheng bank A shares were publicly listed
on the Shang Hai stock exchange on december 19, 2000, and Minsheng Bank H
shares were listed on the Hong Kong stock exchange on november 26, 2009.
In the ranking of the world's top 1,000 major banks released by the Banker
magazine in July 2017, China Minsheng Bank ranked 29th; in the ranking of
Fortune 500 companies released by the US “Fortune” magazine in July 2017,
China Minsheng Bank ranked 251th. China Minsheng Bank strengthened the
marketing planning and management of its customers in the same industry,
deepened the construction of its own strategic cooperation platform, continued to
enrich its asset management business products, explored in-depth comprehensive
custodial financial services, and continued to enhance its foreign exchange and
precious metals trading market competitiveness.
Minsheng Bank's first overseas branch, the Hong Kong Branch, officially opened
on march 30, 2012, marking a historic step in Minsheng’s internationalization
strategy. By the trial on february 8 to march 30, RMB 19 billion yuan in
renminbi deposits have been drawn up, and renminbi loans have also reached
RMB 5.5 billion.
37
4.1.4 CHINA MERCHANTS BANK ( CMB )
China Merchants Bank is the first state-owned joint-stock commercial bank in
China that is wholly owned by a corporate legal person. Founded on April 8,
1987, it was established by the China Merchants Group of Hong Kong-based
China Merchants Group Co., Ltd. It is one of the five largest banks in the
mainland of China and one of the eight lines and five guarantees of
China-funded financial stocks in Hong Kong.
On april 9, 2002, China Merchants Bank A shares were listed on the Shanghai
stock exchange. On September 8, 2006, China Merchants bank began public
offerings in Hong Kong, issued approximately 2.2 billion H shares, raised funds
of up to more than 20 billion Hong Kong dollars, and was listed on the Hong
Kong stock exchange on september 22.
China Merchants bank's development goal is to become the leading retail bank in
China. On april 2, 2015, China Merchants Group Co., Ltd. planned major events.
In order to ensure fair disclosure of information, protect the interests of investors,
and avoid abnormal fluctuations in the company's share price, the company’s
stock has been applied since the company opened on April 3, 2015. Suspended.
In August 2016, China Merchants Bank ranked 39th among the "2016 Top 500
China Enterprises". China Merchants Bank has 113 branches and 943
sub-branches in more than 110 cities in Mainland China, a branch in Hong Kong
(Hong Kong Branch), a New York branch and representative office in the United
States, and a Singapore branch in Singapore, in London and Taipei has a
representative office.
4.1.5 CHINA CITIC BANK ( ECITIC )
China CITIC Bank was established in 1987. It is one of the earliest newly
38
established commercial banks in China's reform and opening up, and the earliest
commercial bank in China to participate in the financing of domestic and foreign
financial markets. In april 2007, CITIC Bank was established on the Shanghai
Stock Exchange and Hong Kong. The A+H shares of the Stock Exchange are
listed simultaneously. CITIC Bank is one of China's national commercial banks
and is headquartered in Beijing.
CITIC Bank is the seventh largest bank in Mainland China. Its total assets are
more than 12,000 million Hong Kong dollars, and there are more than 16,000
employees and more than 540 branches. It is one of the six lines and three
guarantees of Hong Kong-funded China financial stocks.
As of the end of 2017, the Bank had 1,435 business outlets in 142 large and
medium-sized cities in the country. It also set up five affiliates, including CITIC
International Financial Holdings Limited, Xinyin (Hong Kong) Investment Co.,
Ltd., and CITIC Financial Leasing Co., Ltd. , Zhejiang Lin'an CITIC Village
Bank Co., Ltd. and CITIC Baixin Bank Co., Ltd. Among them, CITIC Bank
International (International) Co., Ltd., a subsidiary of CITIC International
Financial Holdings Co., Ltd., has 41 outlets in Hong Kong, Macau, New York,
Los Angeles, Singapore and Mainland China. CITIC Baixin Bank Co., Ltd.
initiated the establishment of the first domestic direct bank with independent
legal person status established by the Bank and Baidu company. In addition, the
Bank and the People’s Bank of Kazakhstan have signed an equity transaction
agreement, becoming the first joint-stock commercial bank in Kazakhstan to
acquire a bank.
4.2 SPSS 23.0
In this model the researcher will set Y is dependent variable return on assets,and
X is independent variable, which are X 1 is the net interest spread , X2=net
39
interest income ratio, X3= cost to income ratio, then here is the researcher’s
result analysis:
4.2.1 Descriptive Statistics
Table 4.1 Descriptive statistics
Descriptive Statistics
N Minimum Maximum Mean Std. Deviation Variance
ROA (Y) 120 2.78 4.89 3.83 .414 .323
NIS (X1) 120 1.74 3.29 2.55 .303 .092
NIIR(X2) 120 1.18 2.86 2.13 .238 .056
CIR(X3) 120 3.57 4.62 3.93 .842 .071
Valid N
(listwise)
120
Source: Constructed in SPSS 23.0
1. Y= Return on assets
As can be seen from Table 4.1, the mean value of return on assets (Y) is 3.83,
the minimum value is 2.87 , the maximum value is 4.89 , the standard
deviation value is 0.414 . That can conclude the stabdart devision of return on
asset is good .
2. X1=Net interest spread
As can be seen from Table 4.1, the mean value of net interest spread (X1) is
2.55 , the minimum value is 1.74 , the maximum value is 3.29 , the
standard deviation value is 0.303 . That can conclude the stabdart devision of
net interest spread is good .
40
3. X2=Net interest income ratio
As can be seen from Table 4.1, the mean value of net interest income ratio (X2)
is 2.13, the minimum value is 1.18 , the maximum value is 2.86 , the
standard deviation value is 0.238 . That can conclude the stabdart devision of
net interest income ratio is good .
4. X3=Cost to income ratio
As can be seen from Table 4.1, the mean value of cost to income ratio (X3) is
3.93 , the minimum value is 3.57 , the maximum value is 4.62 , the standard
deviation value is 0.842 . That can conclude the stabdart devision of Cost to
income ratio is good .
4.3 Classical Assumption Test
The classical hypothesis tests conducted in this study are as follows : normality
test, heteroscedasticity test,autocorrelation test, and multicollinearity test.
4.3.1Normality Test
The test of using observation data to judge whether the population is normally
distributed is called normality test. It is an important speciality test of goodness
of fit in statistical judgments.
The display the residual of the regression model as follows Figure 4.1:
41
Figure 4.1 Histogram
Source: Constructed in SPSS 23.0
From the figure 4.1 histogram, it can be seen that, based on the SPSS output,
most of the results are displayed between -1 and 1, and the graphical data is
normally distributed. The results show that the data is well distributed.
42
Figure 4.2 P-plot diagram
Source: Constructed in SPSS 23.0
From figure4.2 normalized P - P graph of standardized residuals . The results
show that in a normal distribution, the data spreads diagonally and diagonally,
and the regression model satisfies the assumption of normality.
4.3.2 Heteroscedasticity Test
The heteroskedasticity test is an important assumption of the classical linear
regression model , the heteroscedasticity varies with the size of the independent
variable, the scatter plot can be used to simply determine whether there is
heteroscedasticity. If there is no heteroskedasticity in the model, the multiple
regression model is good.
Analyze the model to see the figure below:
43
Figure 4.3 Herteroscedascity test
Source: Constructed in SPSS 23.0
From figure 4.3 can see the scatter plot of heteroscedasticity from the SPSS
output. In the normal distribution of the graph, the data is distributed and there is
no patterning. The results show no heteroskedasticity. So the researchers'
regression model is effective.
4.3.3 Autocorrelation Test
Autocorrelation is based on time-series data analysis. Autocorrelation testing is
used to examine the correlation between data and time series. In this paper, the
autocorrelation test is shown in table:
44
Table 4.2 Autocorrelation test
Model Summaryb
a. Predictors: (Constant), X3, X2 , X1
b. Dependent Variable: Y
Source: Constructed in SPSS 23.0
From on that table 4.2 , the Durbin - Watson value is 1.520 , the value is between
-2 and 2, so the regression model proves to be good .
4.3.4 Multicollinearity Test
The multicollinearity test is a statistical analysis method that uses the regression
analysis in mathematical statistics to determine the interdependent quantitative
relationship between two or more variables.
Table 4.3 Multicollinearity test
a. Dependent Variable: Y
Source: Constructed in SPSS 23.0
Model R R Square Adjusted R
Square
Std. Error of
the Estimate
Durbin-Watson
1 .797a .635 .631 .52132 1.520
Model Collinearity Statistics
Tolerance VIF
1
(Constant)
NIS .872 1.147
NIIR .633 1.579
CIR .638 1.568
45
If the variance inflation factor (VIF) value is less than 10 and greater than 0.1,
there will be no collinearity problem in this study. The tolerances of the variables
1.147, 1.579 and 1.568 can be seen from the chart. The variable values are all
above 0.1 and less than 10. Therefore, the regression model in this study is good.
4.4 Hypotheses Testing
4.4.1 T -Test
The T-test identified the significance of the independent variables in the multiple
regression analysis for the dependent variables. The independent variables used
in this study are the Net interest spresd , Net interest income ratio , cost to
income ratio and the dependent variable is Return on assets .
After screening explanatory variables, we used SPSS statistical software to
construct a regression model for the explanatory variables and the selected
explanatory variables. To explore the factors affecting ROA, this paper selects
ROA as the dependent variable Y, selects NIS as X1, NIIR as X2, and CIR as X3.
As an independent variable, a multiple regression model is established:
Table 4.4 T- Test
Coefficientsa
a.Dependent Variable: Y
Source: Constructed in SPSS 23.0
Model Unstandardized
Coefficients
Standardized
Coefficients
t Sig. Collinearity Statistics
B Std. Error Beta Tolerance VIF
1
(Constant) 1.238 .347 3.569 .001
NIS .287 .085 .257 3.357 .001 .872 1.147
NIIR .213 .048 .799 8.879 .000 .633 1.579
CIR .329 .061 .450 5.018 .000 .638 1.568
46
The relevant data was entered into SPSS to obtain a multiple regression model.
So the linear regression equation is:
Y=1.238+0.287X1+0.213X2+0.329X3
From table 4.5, the regression coefficients between ROA and NIS, NIIR, and
CIR were 0.287, 0.213, and 0.329, respectively. At the 5% significance level, the
significance values were all less than 0.05, and the regression coefficients were
all positive. There is a positive positive effect.
The details as follows :
1. The T-test results show the impact of net interest spreads on the total interest
rate of assets.
From the T test , the cofficient value is 0.287 , the significant value is 0.001
that must be small than 0.05 or 5% , passed the T test . It shows that there is a
significant positive correlation between the net interest spread and the total
asset net rate. It shows that there is a significant positive correlation between
the net interest spread and the total asset net rate, which indicates that the net
interest spread has a significant positive effect on the total bank asset net rate .
2. The T-test results show the impact of net interest income ratio on the total
interest rate of assets.
From the T test , the cofficient value is 0.213 , the significant value is 0.000
that must be small than 0.05 or 5% , passed the T test . It shows that there is a
significant positive correlation between net interest income ratio and total
assets net interest rate, indicating that net interest income ratio has a
significant positive effect on the total bank asset net rate.
3.The effect of the T-test results cost-to-income ratio on the total interest rate of
assets.
47
From the T test , the cofficient value is 0.329 , the significant value is 0.000
that must be small than 0.05 or 5% , passed the T test . It shows that the
cost-to-income ratio is significantly positively related to the total asset net rate,
which indicates that the cost-to-income ratio has a significant positive effect
on the bank's total asset net rate.
4.4.2 F -Test
SPSS will automatically calculate the inspection test statistic observations and
the corresponding probability P value. If the probability P value is less than the
given significance level, the original hypothesis should be rejected, and the
partial regression coefficients are considered to be different at zero and the
interpreted variable y is The overall linear relationship of the explanatory
variable x is significant, and the linear model can be used to describe and
reflect the relationship between them; otherwise, it is not significant. In
addition, we can also judge the significance of the regression equation by
observing the size of the P value. When P value ≤, reject H0 and consider the
regression equation to be significant.
Specific table is as follows:
Table 4.5 Anova table
ANOVAa
Model Sum of Squares df Mean Square F Sig.
1
Regression 62.514 3 20.838 72.405 .000b
Residual 59.075 116 .570
Total 112.589 119
a. Predictors: (Constant), X3, X2 , X1
b. Dependent Variable: Y
Source: Constructed in SPSS 23.0
48
When P value >, H0 is accepted and the regression equation is considered to be
insignificant. In this model, we use SPSS statistical software for analysis of
variance to obtain the analysis of variance table. Obviously, P value Sig = 0.001
(approximate), and thus the regression equation has strong significance, that is,
all the independent variables have the dependent variable Y. Significantly
affected.
4.4.3 Coefficient of determination
The coefficient of determination is the measure of the fitness of the regression
method. It can also be used to indicate that the relationship between all variables
is also the variability of the percentage variable. Errors are related to each other
because the total error between the actual and residual values of the regression
error is between the mean value.
The squared R is the percentage variance of the explanatory variable, and
independent variables can simply be added by adding more variables. Therefore,
one of the important indicators to measure the effect of the model is to adjust the
R-squared.
Table 4.6 Coeffiecient of determination
Model R R Square Adjusted R Square Std. Error of the
Estimate
Durbin-Watson
1 .797a .635 .631 .52132 1.520
a. Predictors: (Constant), X3, X2 , X1
b. Dependent Variable: Y
Source: Constructed in SPSS 23.0
49
As a test of goodness of fit test. The value range is (0, 1). The closer to 1, the
better the fitting effect; and the closer to 0, the worse the fitting effect. As can be
seen from Table 4.6, the coefficient of determination R2=0.635, and the R2 value
of Adjusted R Square is 0.631.
It can be seen that the Adjusted R Square value can reflect the fitting effect, so
we can see that the regression effect of this model is significant with the
modified coefficient.
4.5 Interpretation of Results
1. Net interest spread impact on assets return on assets
This research first hypothesis shows that (There net interest spread has a
significant effect on return on assets.) From the T test , the cofficient value is
0.287 , the significant value is 0.001 that must be small than 0.05 or 5% ,
passed the T test . It shows that there is a significant positive correlation
between the net interest spread and the total asset net rate. It shows that there
is a significant positive correlation between the net interest spread and the
total asset net rate, which indicates that the net interest spread has a
significant positive effect on the total bank asset net rate .
According to (She,2015) Through the verification and analysis of the
coefficient of the net interest spread difference in this paper is 0. 315829,
which is positive, indicating that the increase of net interest spread will help
the improvement of bank efficiency, and the improvement of bank efficiency
will increase the bank's performance.
According to (Yan,2013) Through the quantitative study of this paper, it is
proved that this shows the impact relationship between commercial banks and
net interest spread. Through the results, it is proved that the analysis results are
positive, indicating the impact of net interest spread yield on bank
performance, and also the bank's net interest spread of return is decisive for
50
increasing business performance. Impact.
2.Net interest income ratio impact on assets return on assets
This research first hypothesis shows that ( There increase in bank net interest
income ratio has a significant effect on return on assets.) From the T test , the
cofficient value is 0.213 , the significant value is 0.000 that must be small than
0.05 or 5% , passed the T test . It shows that there is a significant positive
correlation between net interest income ratio and total assets net interest rate,
indicating that net interest income ratio has a significant positive effect on the
total bank asset net rate.
According to (She,2015) Interpretation variables in this model better explain
the explanatory variables. The coefficient of the variable NIIR is positive, that
is, the coefficient of net-interest income is exactly 0.005939, which indicates
that the net-interest income increase of the bank is beneficial to improve the
performance of the bank.
According to (Zhou,2016) From the regression results of each sample, it was
found that the impact of various types of commercial bank procedures and
commission income on the operating results was greater than the income
factor of interest income. Visible to interest income, increase the proportion of
net-interest income can obviously improve business performance.
3.Cost to income ratio impact on assets return on assets
This research first hypothesis shows that (There cost to income ratio has a
significant effect on return on assets.) From the T test , the cofficient value is
0.329 , the significant value is 0.000 that must be small than 0.05 or 5% ,
passed the T test . It shows that the cost-to-income ratio is significantly
51
positively related to the total asset net rate, which indicates that the
cost-to-income ratio has a significant positive effect on the bank's total asset
net rate.
According to (Yan,2013) The coefficient of cost-to-income ratio (COR) is 0.
013643, which is a positive value. It means that the greater the bank's
operating cost, the better the performance of the bank. Therefore, the bank will
need to carry out banking business costs in the future when pursuing profit
maximization. A good bank, the better the bank's performance.
According to (Zhou,2016) Based on the results of sample returns, the
cost-to-income ratios of various types of samples are positively related to the
ROA, and the impact coefficient of the China city commercial banks is the
largest, which is 0.0389. It is indicated that the city's commercial bank's
cost-effectiveness is relatively strong. This is mainly due to the fact that the
business development of the city's commercial banks has become more
diverse in the growth stage, and it can be seen that the impact of cost income
on performance is positive.
4.The net interest spread, net interest income ratio and cost to income ratio
have significant effects to the return on assets of China's commercial
banks
According to the T-test of this study, it can be seen that Net interest spread the
cofficient value is 0.287, the significant value is 0.000 that must be small than
0.05 or 5%, passed the T test . It shows that there is a significant positive
correlation between net interest. Income ratio and total assets net interest rate,
indicating that net interest income ratio has a significant positive effect on the
total bank asset net rate.
Net interest income ratio the cofficient value is 0.213 , the significant value is
52
0.000 that must be small than 0.05 or 5% , passed the T test . It shows that
there is a significant positive correlation between net interest income ratio and
total assets net interest rate, indicating that net interest income ratio has a
significant positive effect on the total bank asset net rate.
Cost to income ratio the cofficient value is 0.329 , the significant value is
0.000 that must be small than 0.05 or 5% , passed the T test indicating that net
interest income ratio has a significant positive effect on the total bank asset net
rate.
53
CHAPTER V
CONCLUSION AND RECOMMENDATIONS
5.1 Conclusions
According to the research on the effect of banking income agencies on
business performance, the article uses the net interest spread, net interest
income ratio , cost to income ratio, and the three independent variables and
the return on assets of dependent variable there is a positive influence on the
operating performance of the bank's income agencies. The article uses the
quarterly data of China Commercial Bank from 2011 to 2016 for analysis.
According to the analysis result the three independent variables in the article
have a significant impact on it, and the dependent variable is the total asset
interest rate.
The conclusion can be generalized as follows:
1.Net interest spread has a positive effect on the return on assets . When the
net interest spread increases, the impact on the bank's operating
performance also increases. Net interest spread income is the main source of
bank profits. Therefore, the factors that influence the bank's net interest
spread are conducive to the bank's own improvement so as to improve the
management level. The net interest spread is also an important means for
promoting the rational allocation of financial resources and realizing stable
economic operation.
2.Net interest income ratio influence on return on assets is positive.When
net-interest income income increases, bank operating performance will also
54
increase. The proportion of net-interest income has increased year by year,
and the bank’s operating income has become increasingly diversified.
Therefore, net-interest income in China's commercial banks brings multiple
benefits to banks. In the current situation in China, net-interest income has
become an important indicator of improving the profitability of banks.
3. The cost-to-income ratio has a positive impact on return on assets . The
increase in cost and income also increases the efficiency of bank
operations.The cost-to-income ratio has a positive impact on ROA. The
increase in cost and income also increases the efficiency of bank operations.
When it increases, it shows that bank profitability is also increasing. Its ratio is
the ratio of bank operating expenses to operating revenue, reflecting how
much it costs each bank’s revenue. Therefore, the cost-to-income ratio is an
important measure of the profitability of a bank.
4.The net interest spread in commercial banks has a positive effect on the return
on assets. The net-interest income ratio has a positive effect on the return on
assets. The cost-to-income ratio has a positive effect on the return on assets.
The increase in costs and revenue also improves the efficiency of banking
operations. Therefore, the cost-to-income ratio is an important measure of the
profitability of a bank.
5.2 Recommendations
According to this research, the researcher recommend about that:
1. In this topic, researchers should raise their awareness of the impact of
banking revenue agencies on business performance and then gain an
in-depth understanding of the banking industry.
2. Future researchers should expand their research on the factors affecting
bank operating performance.And put forward some policy
recommendations to increase the bank's income agencies.
55
REFERENCES
BOOK :
Zhang Xuelan ( 2011 ). Diversification of bank revenue can reduce bank risk .
Zhu Hongquan & Zhou Li . (610031) An Analysis of Non-Interest Income of China
Commercial Bank and Its Influencing Factors .
Ma Liqing ( 2013 ) An Empirical Analysis of the Effect of Income Structure on the
Performance of China's Listed Commercial Banks .
Liu Pengbo ( 2015 ) Analysis of the Impact of Non-interest Income on Commercial
Bank's Operating Performance.
Huang Guoyan ( 2014 ) Research on Income Structure and Bank Risk of Commercial
Bank .
JOURNALS :
Chi Guofeng & Sun (2016) Research on the Relationship between Income Structure
and Income Efficiency of China's Commercial Banks Journal of Engineering .
Duan Junshan & Su (2011) Study on the Influencing Factors of Non-interest Income
in Commercial Banks. Financial Reform, 2011(5).
Deng Xiaoyi & Li (2006) Empirical Analysis on the Impact of China's Banking
Industry's Multi-Year Income on Its Profit, Special Region Economics,
2006(9):339-341
Hao Guosheng (2016) Western banks' non-interest income business. Economic
management .
He Wei (2014) Research on the Impact of Revenue Institutions of China's Listed
Commercial Banks on Business Performance
Jiang Li (2012) Empirical research on the effect of non-interest income on the
performance of commercial banks I. Panel data based on 16 listed banks in
China [J]. Science Technology and Engineering.
Li Lin (2009) A Discussion on Optimizing the Non-interest Business of China's
56
Commercial Banks, based on a comparative study of banks in China and
the United States. Southwestern University of Finance and Economics.
Liu Mengfei & Zhang (2012) Research on the correlation between business
diversification, operating performance and risk in China's commercial banks.
Lou Yingchun (2008) The research on the impact of non-interest income on business
performance of China's commercial banks cuts the economy. 2008,(4):240-241.
She Dawei (2015) An Empirical Study on the Effect of Commercial Banks' Income
Agency on Bank's Operating Performance
Wang Juan (2013) Non-interest income and its impact on the performance of listed
banks in China .
Wang Jing & Zhou(2008) An empirical analysis of the negative contribution of
non-interest income is based on the model test closure of 12 commercial banks in
China. Contemporary economic research. 2008,(11):49-53.
Wei Shijie & Ni (2010) The research on the relationship between non-interest income
and commercial bank performance is based on the experience of 40 banks in
China [Jl Futures and Development, 2010, (2):51-55.
Xu Jie & Hao (2010) Analysis and Countermeasures of Non-interest Income Business
of China's Listed Commercial Banks.
Yan Qaing (2013) A comparative study on the net interest rate of return of China's
commercial banks: based on data from 10 listed banks from 2001-2012.
Yuan Yufei & Han(2012) The Diversification of Commercial Banks in China Affects
the Benefits and Risks of Financial Education Research [J]. 2012,9
Zhou Xiaoyu (2016) The Influence of China's Commercial Bank's Income Structure
on Its Business Performance .
57
WEBSITES :
China Banking Regulatory Association. http://www.cbrc.gov.cn/
chinese/home/docViewPage/110009.html.
Financial structure change and banking income: A Canada–U.S. comparison
http://shop.tarjomeplus.com/Uploads/site-1/DownloadDoc/917.pdf
Martyn Shuttleworth, Lyndsay T Wilson (Mar 17,2008). Research Hypothesis .
http://explorable.com/research-Hypothesis
58
APPENDIX
THE RESULTS
Regression
Descriptive Statistics
N Mean Std. Deviation
ROA 120 3.83 .414
NIS 120 2.55 .303
NIIR 120 2.13 .238
CIR 120 3.93 .842
Valid N (listwise) 120
Varibles Entered/Rrmoved
a. Dependent Variable: ROA
b.All requested variables entered
Model Summaryb
a. Predictors: (Constant), CIR, NIS, NIIR
b. Dependent Variable: ROA
Coefficients
Model Variables
Entered
Variables
Removed
Method
1 CIR, NIS,
NIIRb Enter
Model R R Square Adjusted R
Square
Std. Error of the
Estimate
Durbin-Watson
1 .797a .635 .631 .52132 1.520
59
a. Dependent Variable: Y
ANOVAa
a. Predictors: (Constant), X3, X2 , X1
b. Dependent Variable: Y
Model Unstandardized
Coefficients
Standardized
Coefficients
t Sig. Collinearity Statistics
B Std. Error Beta Tolerance VIF
1
(Constant) 1.238 .347 3.569 .001
NIS .287 .085 .257 3.357 .001 .872 1.147
NIIR .213 .048 .799 8.879 .000 .633 1.579
CIR .329 .061 .450 5.018 .000 .638 1.568
Model Sum of Squares df Mean Square F Sig.
1
Regression 62.514 3 20.838 72.405 .000b
Residual 59.075 116 .570
Total 112.589 119
60
Correlations
ROA NIS NIIR CIR
ROA
Pearson Correlation .067 .054 .071 .066
Sig. (2-tailed)
.000 .000 .000
N 120 120 120 120
NIS
Pearson Correlation .068 .067 .075 .062
Sig. (2-tailed) .000 .001 .000
N 120 120 120 120
NIIR
Pearson Correlation .071 .081 .067 .071
Sig. (2-tailed) .000 .000 .000
N 120 120 120 120
CIR
Pearson Correlation .066 .062 .071 .067
Sig. (2-tailed) .000 .001 .000
N 120 120 120 120
a.Dependent Variable : Y
Collinearity Diagnostice
Model Dimension Eigenvalue Condition
Index
Variance Proportions
(Constant) NIS NIIR CIR
1
1 3.560 1.000 .00 .00 .02 .00
2 .416 2.927 .00 .00 .56 .00
3 .021 13.130 .00 .28 .08 .41
4 .003 32.853 .98 .72 .35 .59
a.Dependent Variable : Y
61
Residuals Statisticsa
Minimum Maximu
m
Mean Std.
Deviation
N
Predicted Value .385872 1.488455 .714108 .2152612 120
Std. Predicted Value -1.525 3.597 .000 1.000 120
Standard Error of
Predicted Value .024 .095 .046 .014 120
Adjusted Predicted
Value .387459 1.546797 .716051 .2182743 120
Residual -.5779740 .6401085 .0000 .2604913 120
Std. Residual -2.191 2.426 .000 .987 120
Stud. Residual -2.255 2.450 .0004 1.005 120
Deleted Residual -.6121764 .6525578 .0019426 .2698661 120
Stud. Deleted Residual -2.296 2.505 .003 1.012 120
Mahal. Distance .031 14.297 2.975 2.525 120
Cook's Distance .000 .095 .009 .014 120
Centered Leverage
Value .000 .120 .025 .021 120
a.Dependent Variable : Y
Charts
62
63
64
Descriptive Statistics
N Minimum Maximum Mean Std. Deviation Variance
ROA (Y) 120 2.78 4.89 3.83 .414 .323
NIS (X1) 120 1.74 3.29 2.55 .303 .092
NIIR(X2) 120 1.18 2.86 2.13 .238 .056
CIR(X3) 120 3.57 4.62 3.93 .842 .071
Valid N
(listwise)
120
65
APPENDIX I
RAW DATA
Data of five Commercial Banks , 2011 to 2016 , quarterly data .
Unit % ROA NIS NIIR CIR
2011 Q1 HXB 0.1579 2.4088 10.4217 38.56
2011 Q2 0.3913 2.5 10.1706 39.41
2011 Q3 0.6168 2.7105 9.4609 38.73
2011 Q4 0.8073 2.63 9.692 38.87
2012 Q1 0.1885 2.4528 10.9857 39.57
2012 Q2 0.4675 2.59 11.6114 40.57
2012 Q3 0.6824 2.4715 11.3652 37.65
2012 Q4 0.9364 2.52 11.1457 39.95
2013 Q1 0.1962 2.3804 12.9565 41.02
2013 Q2 0.4872 2.54 13.7884 39
2013 Q3 0.7319 2.4543 13.5351 39.11
2013 Q4 0.9813 2.5 13.9698 38.93
2014 Q1 0.219 2.3352 15.8131 39.42
2014 Q2 0.5036 2.44 16.3817 37.85
2014 Q3 0.7646 2.5673 16.0979 37.38
2014 Q4 1.0228 2.52 15.7493 37.57
2015 Q1 0.2263 2.9368 11.7117 38.2
2015 Q2 0.4942 2.45 17.9575 36.6
2015 Q3 0.7355 2.8383 18.7946 35.47
2015 Q4 0.9789 2.4 21.6862 35.01
2016 Q1 0.2169 2.4388 20.1799 34.94
2016 Q2 0.4625 2.34 22.7367 34.87
2016 Q3 0.6841 2.4699 23.2062 34.8
2016 Q4 0.9028 2.29 23.4846 34.5
2011 Q1 PAB 0.313 2.56 12.2219 35.56
2011 Q2 0.5991 2.5 14.401 36.3
2011 Q3 0.8006 2.4 14.5904 38.44
2011 Q4 1.0465 2.37 14.6857 39.99
2012 Q1 0.2662 2.27 18.1905 38.18
2012 Q2 0.4998 2.22 17.68 38.13
2012 Q3 0.7564 2.2 16.8346 38.56
2012 Q4 0.9433 2.19 16.8894 39.41
2013 Q1 0.21 2.01 19.3946 38.73
2013 Q2 0.4387 2.03 20.91 38.87
66
2013 Q3 0.6756 2.07 22.2 39.57
2013 Q4 0.8708 2.14 22.0372 40.77
2014 Q1 0.2534 2.23 28.2609 37.65
2014 Q2 0.5001 2.32 29.3064 37.59
2014 Q3 0.7777 2.36 29.7 36.7
2014 Q4 0.9711 2.4 27.74 36.33
2015 Q1 0.2507 2.57 26.7379 33.38
2015 Q2 0.4871 2.57 33.1873 32.22
2015 Q3 0.7414 2.59 32.25 32.14
2015 Q4 0.9317 2.63 31.2636 31.31
2016 Q1 0.2346 2.76 33.1759 29.35
2016 Q2 0.4631 2.67 33.91 28.8
2016 Q3 0.7066 2.62 33.4338 27.7
2016 Q4 0.8277 2.6 29.0619 25.97
2011 Q1 CMBC 0.324 2.818 21.8745 31.3516
2011 Q2 0.7055 2.85 23.9628 33.01
2011 Q3 1.0949 3.1649 22.4392 34.8761
2011 Q4 1.4036 2.96 21.3032 35.61
2012 Q1 0.4019 3.2556 24.2261 25.8678
2012 Q2 0.8048 2.93 26.4012 30.26
2012 Q3 1.1658 3.0329 26.2 31.82
2012 Q4 1.4081 2.75 25.1748 34.01
2013 Q1 0.3466 2.4624 30.35 26.9245
2013 Q2 0.7096 2.24 30.41 28.67
2013 Q3 1.0455 2.4932 29.53 30.65
2013 Q4 1.3445 2.3 28.35 32.75
2014 Q1 0.3973 2.6908 32.13 26.78
2014 Q2 0.7676 2.42 33.06 29.12
2014 Q3 1.0701 2.7113 32.54 30.51
2014 Q4 1.2585 2.41 31.99 33.27
2015 Q1 0.3378 2.692 35.7424 25.33
2015 Q2 0.6559 2.19 38.89 27.34
2015 Q3 0.9312 2.6781 39.32 27.85
2015 Q4 1.1018 2.1 38.96 31.22
2016 Q1 0.2986 2.62 39.9362 23.76
2016 Q2 0.567 1.88 39.14 23.03
2016 Q3 0.7854 2.6641 39.0889 26.7
2016 Q4 0.9365 1.74 39 30.98
2011 Q1 CMB 0.3533 2.89 22.2461 32.77
2011 Q2 0.7373 2.89 22.45 32.63
2011 Q3 1.1275 2.9 20.865 33.25
2011 Q4 1.3902 2.94 20.64 36.19
2012 Q1 0.4043 3.05 24.0867 31.86
67
2012 Q2 0.7642 2.96 23.6 32.2
2012 Q3 1.1714 2.89 22.7645 33.01
2012 Q4 1.4597 2.87 22.05 35.98
2013 Q1 0.3762 2.78 25.3007 31.15
2013 Q2 0.7277 2.72 25.94 31.41
2013 Q3 1.0829 2.66 25.5908 31.38
2013 Q4 1.3938 2.65 25.41 34.36
2014 Q1 0.3555 2.44 35.9375 27.28
2014 Q2 0.6745 2.37 36.08 26.78
2014 Q3 1.0509 2.3 34.2006 28.1
2014 Q4 1.2814 2.33 32.47 30.54
2015 Q1 0.359 2.72 34.205 24.14
2015 Q2 0.6665 2.6 36.52 24.4
2015 Q3 0.9802 2.59 35.25 25.09
2015 Q4 1.1368 2.59 32.1346 27.67
2016 Q1 0.3377 2.49 41.1076 21.68
2016 Q2 0.6417 2.45 40.243 23.44
2016 Q3 0.9483 2.43 37.16 25.22
2016 Q4 1.0927 2.37 35.6082 28.01
2011 Q1 ECTTIC 0.3119 2.662 14.7712 31.3012
2011 Q2 0.706 2.76 15.5637 30.3229
2011 Q3 1.113 2.7907 15.3094 27.8203
2011 Q4 1.2727 2.85 15.39 29.86
2012 Q1 0.3198 2.5796 15.5404 28.9589
2012 Q2 0.6893 2.68 16.4 28.43
2012 Q3 0.9702 2.492 15.938 27.7466
2012 Q4 1.0963 2.61 15.6 31.51
2013 Q1 0.307 2.5952 17.19 31.0363
2013 Q2 0.6454 2.41 18.5 28.68
2013 Q3 0.9822 2.7117 17.98 28.9427
2013 Q4 1.2033 2.4 18 31.41
2014 Q1 0.2868 2.7752 25.7181 28.0705
2014 Q2 0.564 2.14 26.6 26.83
2014 Q3 0.8548 2.9305 24.33 27.3118
2014 Q4 1.0657 2.19 24 30.32
2015 Q1 0.259 3.2964 27.09 28.3402
2015 Q2 0.528 2.14 29 25.95
2015 Q3 0.7606 3.2995 28.58 25.8597
2015 Q4 0.9014 2.13 28 27.85
2016 Q1 0.2114 3.2948 31.91 24.9216
2016 Q2 0.4414 2 31.6719 24.88
2016 Q3 0.6494 3.0099 31.05 26.4011
2016 Q4 0.7561 1.89 31 27.56
68