THE IMPACT OF NET INTEREST SPREAD, NET INTEREST INCOME

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.)


(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.)


(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


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


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


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


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


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


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.


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


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




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




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

http://www.cbrc.gov.cn/



58

APPENDIX

THE RESULTS

Regression


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



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


Sig. (2-tailed) .000 .001 .000

N 120 120 120 120

NIIR


Sig. (2-tailed) .000 .000 .000

N 120 120 120 120

CIR


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


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


Charts

62

63

64


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

Documents

THE IMPACT OF NET INTEREST SPREAD, NET INTEREST INCOME