IS THERE DOWNSIDE TO DIVERSIFICATION? THE CASE OF THAI ...

IS THERE DOWNSIDE TO DIVERSIFICATION?

THE CASE OF THAI COMMERCIAL BANKS

BY

MS. PIYAON SRISAILUAN

AN INDEPENDENT STUDY SUBMITTED IN PARTIAL

FULFILLMENT OF THE REQUIREMENTS FOR

THE DEGREE OF MASTER OF SCIENCE

PROGRAM IN FINANCE (INTERNATIONAL PROGRAM)

FACULTY OF COMMERCE AND ACCOUNTANCY

THAMMASAT UNIVERSITY

ACADEMIC YEAR 2014

COPYRIGHT OF THAMMASAT UNIVERSITY

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Independent Study Title IS THERE DOWNSIDE TO DIVERSIFICATION?

THE CASE OF THAI COMMERCIAL BANKS

Author Ms. Piyaon Srisailuan

Degree Master of Science (Finance)

Major Field/Faculty/University Master of Science Program in Finance

(International Program)

Faculty of Commerce and Accountancy

Thammasat University

Independent Study Advisor Associate Professor Kulpatra Sirodom, Ph.D.

Academic Years 2014

ABSTRACT

This paper studies the income structure diversification to the profitability of

seven Thai commercial banks. All data are collected from disclosed financial report

in quarterly data for year 2002 - 2014. The Herfindahl Hirschmann Index (HHI) is

used as an indicator to measure the diversification between interest income and non-

interest income. Although, diversification cannot explain banks’ profitability, higher

proportion of non-interest income increase banks’ profitability. However, non-

interest income must not be fee income since fee income decrease banks’

profitability. Moreover, this study also provide furthered test in size sensitivity and

sector sensitivity, and results indicate some differences in details.

Keywords: Bank, Income diversification, HHI

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ACKNOWLEDGEMENTS

I wish to express my sincere thanks to, Assoc. Prof. Dr. Kulpatra Sirodom, my

advisor, for providing me with all invaluable guidance and support. I place on record,

my sincere thank you to Asst. Prof. Suluck Pattarathammas, DBA for being my

committee member as well as his commends.

I take this opportunity to express gratitude to all MIF people, lecturers who

have gave all knowledge, administrative staffs for their kind assistance, and friends

for their helped and supported. I am also grateful to my parents especially my mother

for the unceasing encouragement, support and attention.

Ms. Piyaon Srisailuan

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TABLE OF CONTENTS

Page

ABSTRACT (1)

ACKNOWLEDGEMENTS (2)

LIST OF TABLES (5)

LIST OF FIGURE (6)

CHAPTER 1 INTRODUCTION 1

CHAPTER 2 REVIEW OF LITERATURE 4

CHAPTER 3 RESEARCH METHODOLOGY 8

3.1 Diversification measurement 10

3.1.1 Revenue diversification 10

3.1.2 Non-interest income diversification 12

3.2 Dependent variables 14

3.3 Control variables 14

3.4 Research model 14

3.5 Sensitivity test 16

3.5.1 Size sensitivity test 16

3.5.2 Sector sensitivity test 16

CHAPTER 4 RESULTS AND DISCUSSION 18

5.1 Result of all banks 18

5.2 Result of size sensitivity 20

5.3 Result of sector sensitivity 22

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CHAPTER 5 CONCLUSIONS AND RECOMMENDATIONS 24

REFERENCES 25

BIOGRAPHY 27

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LIST OF TABLES

Tables Page

2.1 Summary of research findings on bank risk and return from activities 6

diversification

3.1 Summary statistic for large and medium banks and proportion to all Thai 9

banks, 2002Q1 and 2014Q3 (in THB’000)

4.1 All independent variables and profitability of all banks 18

4.2 The relationship of interest income proportion, non-interest income 20

proportion and fee income proportion to banks’ profitability

4.3 All independent variables and profitability of banks: impact of 21

differences in bank size

4.4 The average of non-interest income proportion through the studied periods 21

4.5 All independent variables and profitability of banks: impact of 23

differences loan by sector

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LIST OF FIGURES

Figures Page

3.1 The summation of the average proportion between net interest income 11

and non-interest income of 7 banks from 2002 to 2014 (first 3Q)

3.2 The trend of HHI revenue of 7 banks from 2002 to 2014 (first 3Q) 12

3.3 The summation proportion among non-interest income activities of 13

7 banks from 2002 to 2014 (first 3Q)

3.4 The trend of HHI non-interest income of 7 banks from 2002 to 2014 13

(first 3Q)

3.5 Relationship between HHI_R and net income 17

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CHAPTER 1

INTRODUCTION

Diversification is an important strategy in finance which helps to manage risk

and return. In modern portfolio theory, the proverb “don’t put your eggs in one basket”

is the simplest example, the idea is that when investors invest in efficient portfolio,

diversification helps to mitigate risk or volatility. Marketing theory inform that

expanding product line under the same or different brands or diversify more to different

customer segments can increase sales volume and the number of customers.

Furthermore, expansion firm to more economic condition horizontally (same industry

or same level of production) and vertically (different level of production) creates more

opportunities to grow.

Bank’s diversification can be divided into several dimensions for example;

geographical diversification, loan type diversification, sources of deposit

diversification but the most popular is income structure diversification and loan by

sector diversification. Income structure diversification means banks diversify their

income between interest income and non-interest income. For loan by sector

diversification means banks expand their loan portfolio to more sectors. By traditional

banking theory, Diamond (1984) finds that when asymmetry information occurs

between depositors and banks, banks can transfer the default risk and monitoring cost

to depositors hence banks can become more profitable. The way to solve this problem

is to diversify bank’s loan portfolio to more sectors, therefore the probability of default

or other related problem can be reduced.

Can we conclude that banks gain benefit from diversification? There are

arguments about banking theory of diversification. The government encourage banks

to diversify their portfolio structure into more activities. The Basel Committee on

Banking Supervision Supervisory framework for measuring and controlling large

exposures (2014) mentions that concentration risk occurs when banks focus on a single

group of customer or a single sector because banks will rely too much on one

counterparty’s exposure. The Bank of Thailand has the single lending limit policy

limits bank’s exposure to a single borrower and also support competition between

banks because they believe that when banks diversify their portfolio on loans or

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activities, their performance should be better and will lead to the stability in the

financial system. Moreover, Demyanyk and Hemert (2008) find that the recent crises

especially the subprime crisis is a result from concentration on a specific sector and

activity. This is supported by paper of Nakornthab (2007), he mention that before the

1997crisis, Thai banks relied too much on loan which was high risk relative to

economy.

Although banks always have their own structure or own strategy to run

business and some banks may feel comfortable to their experience and prefer to stick

with their existing activities, but due to high competition between financial institutions,

banks may change their goal, Amidu and Wolfe (2013) find that banks shift their

business to several portfolio or from interest income activities to non-interest income

activities when competition increases. The findings show that non-interest income

activities give more value to banks and also makes banks more stable. On the other

hand, high competition pushes banks to diversify their portfolio to business that they

lack experience which bring more risk and may cause damage to all financial system.

There are many supporting studies which find that the diversification is not always

good for banks. Acharya et al. (2001); Stiroh et al. (2006); Berger et al. (2010); Tabak

et al. (2011) show that banks’ stability caused by doing the business in the activities

they have long experience, the result prove that banks will have comparative advantage

if they have a strong strategy. They should concentrate on few activities which they

have an experience in and gain benefit from economy of scale.

The highly competitive Thai banking business together with fast growing

economy cause some banks to distribute their customer to more sectors or shift away

from loan toward non-interest activities. According to the 10-year trend of Thai banks,

the proportion of non-interest income is growing significantly. Nakornthab (2007),

finds that porportion of non-interest income to total income of Thai banks is 22% by

average during 2002-2006, increasing from 10% during 1992-1996. According to

Nakornthab (2007), Thai banks used to be the traditional banking which most of

income based on interest income but as the banking sector become more competitive,

Thai banks are changing their income structure to rely more on non-interest income

activities. The average of first three quarter of 2014, the proportion from non-interest

activities to total income is 37% which significantly increase from the last finding by

Nakornthab (2007).

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The primary objective of this study is to investigate the correlation of Thai

banks’ profitability with their income structure diversification strategy between interest

income generated from loans and non-interest income. To explore more in details, this

paper will divide banks into two groups by loan by sector concentration, and by size to

see the sensitivity in the result. In our study, we present two contributions. First we

observe only Thai banks, which no one has done before. Second, we distinguish banks

into two sample groups which may bring different results.

Finally, this study can answer the question “Are there any correlations between

income structure and banks’ performance?” and “Are there any differences between

banks characteristic (loan by sector sensitivity and size sensitivity)?” The benefits are

for investors and interested individual to gain insight to diversification information

because all information are disclosed in the financial report. Regulators of Central bank

can also use the result to consider when promoting diversification.

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CHAPTER 2

LITERATURE REVIEW

There are many dimensions on banks diversification for example, (1) income

diversification between interest income and non-interest income, (2) sectors

diversification (3) loan type diversification, and (4) geographical or regional

diversification. For conclusion of all literature’s result, we can separate them into 2

groups; diversification gives more benefit to banks risk and return and diversification

causes harmful situations to banks risk and return. There are research findings summary

on the table 2.1.

Most of the studies find that banks will gain more benefit from concentration

on a few sectors than diversify to other sectors which they lack experience. Mercieca

et al. (2007) study on annual data of small banks in Europe by using Herfindahl

Hirschmann Index (HHI) as a diversification measurement. They study the period when

banks shift from interest income activities to non-interest income which are trading

income, fee income and investment income. They find that when bank shift to non-

interest income banks risk increase and also return decrease. They also study the effect

of sector diversification and they get the same result. Diversification has negative effect

on their loan portfolio. They conclude that small banks are not ready to shift away from

traditional banking that they are familiar to. This is the same finding by Tabak et al.

(2011) who study on sector concentration on high frequency monthly data of Brazilian

Banks (covering year of the Lehman crisis). They discover that before Lehman crisis,

banks have increased risk and lower return from diversification to more sectors. After

the Lehman crisis, the trend shows that they change their strategy to concentrate on few

business or focusing on sectors which have less economic exposure. Moreover, Berger

et al. (2010) and Chen et al. (2013) study on sector diversification of Chinese banks

using ROA and ROE. They provide an evidence that banks will have higher profit from

focusing on few sectors than diversifying loan portfolio because of the higher

monitoring cost. Incidentally, Chen et al. (2013) suggest that diversification can

improve NPL only when banks diversify from sectors that have high systematic risk to

low systematic risk. The research’s result from Acharya et al. (2001) confirms that

banks will be poorly monitored and have higher monitoring cost when they expand

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their loan to sector or activities that they do not have experience. Stiroh et al. (2006)

strongly suggests that more diversification leads to improve return but also increases

volatility of return and increases insolvency risk.

On the other hand, Bebczuka and Galindob (2008) find that Argentine banks

gain benefit from diversification to more sectors because it helps to reduce non-

performing loans and increase return especially when GDP growth is low. Moreover,

bigger banks take more advantage from diversification than smaller ones. Amidu and

Wolfe (2008) provide more evidences that banks in emerging and developing countries

become stable when competition increases. The reason is that banks decide to diversify

their activities to get the opportunities of economic growth so they can increase their

return. The study of Meslier et al. (2014) whose scope is on 39 Philippines banks in

1999 – 2005, he find that Philippino banks are benefited from diversification to non-

interest income, because most of non-interest income come from trading activities

which is low relationship to loan.

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CHAPTER 3

THEORETICAL FRAMEWORK

The Herfindahl–Hirschman Index, or HHI is named after professors Orris

C. Herfindahl and Albert O. Hirschman. Firstly, HHI was introduced to be used as

an indicator to measure the scale of competition among the industry but in recently

years, many researchers apply HHI to be used as an indicator to measure market

concentration. In banking industry, most of researchers use HHI to track changes of

banks’ activities in both income structure concentration and sector concentration.

The main purpose of using the HHI in this study is to measure the change of income

structure from interest income to non-interest income of the studied banks over

time.

The HHI formula is shown as follow:

𝐻𝐻𝐼 = ∑ 𝑠𝑖2𝑁

𝑖=1 ; Where s is share of the activity i (1)

In equation 1, HHI is defined as summation of the squares of the market

shares. Its value is ranged from nearly close to 0 or not less than 1/n (pure

competition) to 1 (pure monopoly). Therefore, the increase in HHI means more

concentration or less diversification. On the other hand, the decrease in HHI means

less concentration or more diversification.

http://en.wikipedia.org/wiki/Market_share

http://en.wikipedia.org/wiki/Market_share

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In this study, we use quarterly data of large and medium listed Thai banks

(classified by the Bank of Thailand) totaling seven commercial banks in Thailand1. The

data is collected from companies’ financial statement report and SETSMART. The

study period ranges from 2002 to 2014Q3 in order to capture interest and non-interest

income from all seven banks.

The summary statistics of selected data are shown on Table 3.1. The total asset

of large and medium banks in 2014Q3 is valued at 11.4 trillion which is 88.6% of

market share of all Thai banks and 83.3% of market share in term of net profit. From

2002 to 2014Q3, the size of large and medium banks grow 2.4 times. Total loan to

customer and net profit increase 217% and 3,833.2% respectively. Net interest income

increases by 456.5% while non-interest income increases by 503.4%. The proportion

of non-interest income grow higher from 29% on average during 2002-2006 to 36% on

average during 2010-2014.

3.1 Diversification measurement

HHI is used as an independent variable to measure market concentration. The

higher HHI means more concentration or less diversification and lower HHI means less

concentration or more diversification.

3.1.1 Revenue diversification

The first measurement is HHI revenue (HHI_R) which is used as an indicator

to measure the diversification of banks’ income proportion between interest income

and non-interest income. The decreasing trend of HHI_R from 0.591 to 0.540 on table

3.1 and from HHI_R trend on figure 3.2 illustrate less concentration or more

diversification from interest income to non-interest income. The reason is that high

competition among Thai banks causes non-interest income to grow faster than interest

income especially during the past 10 years. The obvious decreasing trend starts from

2007, which indicates that Thai banks shift their activities from interest income to rely

more on non-interest income activities, the proportion between interest income and

1 (1) Bangkok Bank Plc., (2) Krung Thai Bank Plc., (3) Siam Commercial Bank Plc., (4) Kasikorn Bank Plc., (5) Bank of Ayudhya Plc., (6) TMB Bank Plc., (7) Thanachart Bank Plc.

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non-interest income is shown on figure 3.1 We compute the HHI_R for each bank in

equation 2;

𝐻𝐻𝐼_𝑅𝑖𝑡 = (𝐼𝑁𝑇_𝐴𝑖𝑡

𝑇𝑂𝑇𝐴𝐿_𝑅𝑖𝑡 )

2

+ (𝑁𝐼𝐼_𝐴𝑖𝑡

𝑇𝑂𝑇𝐴𝐿_𝑅𝑖𝑡 )

2

(2)

Where HHI_Rit = Revenue diversification of bank i at time t

TOTAL_Rit = INT_Ait + NII_Ait of bank i at time t

INT_Ait = Amount of net interest income of bank i at

time t

NII_Ait = Amount of non-interest income of bank i

at time t

Figure 3.1 The summation of the average proportion between net interest income and

non-interest income of 7 banks from 2002 to 2014 (first 3Q)

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Figure 3.2 The trend of HHI revenue of 7 banks from 2002 to 2014 (first 3Q)

4.1.2. Non-interest income diversification

3.1.2 Non-interest income diversification

Second diversification measurement is HHI non-interest income or HHI_N

which is used as an indicator to measure the diversification within four components of

non-interest income. Figure 3.1 and figure 3.2 show that non-interest income has

significant growth, therefore we explore further through non-interest income activities

as displayed on figure 3.3 and figure 3.4. Decreasing trend of HHI_N indicates less

concentration or more diversification within four of non-interest income components.

We conduct the HHI non-interest income as equation 3;

𝐻𝐻𝐼_𝑁𝑖𝑡 = (𝐹𝐸𝐸_𝐴𝑖𝑡

𝑇𝑂𝑇𝐴𝐿_𝑁𝑖𝑡 )

2

+ (𝑇𝑅𝐷_𝐴𝑖𝑡


2

+ (𝐼𝑁𝑉_𝐴𝑖𝑡


2

+ (𝑂𝑇𝑂𝑃_𝐴𝑖𝑡


2

(3)

Where HHI_Nit = Non-interest income diversification of bank i at

time t

TOTAL_Nit = FEE_Ait + TRD_Ait + INV_Ait + OTOP_Ait of

bank i at time t

FEE_Ait = Amount of net fees and service income of bank i

at time t

TRD_Ait = Amount of gains on tradings and foreign

exchange transactions of bank i at time t

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INV_Ait = Amount of net gains (losses) on investment of

bank i at time t

OTOP_Ait = Amount of other operating income of bank i at

time t

Figure 3.3 The summation proportion among non-interest income activities of 7 banks

from 2002 to 2014 (first 3Q)

Figure 3.4 The trend of HHI non-interest income of 7 banks from 2002 to 2014 (first

3Q)

Negative return on some non-interest income components in some years is the

main problem because HHI will give incorrect value. Mercieca et al. (2007) suggest

that negative data should be deleted but due to limited observation in this study, we

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cannot eliminate all negative values. Therefore it can be solved by two processes;

firstly, eliminate 5% of negative outlier. Secondly, find the lowest negative value to be

used as a constant number and add that number to all data to maintain HHI value below

1.

3.2. Dependent variables

In this paper, we only perform the test on return measurements

Return on asset (ROA) is the ratio of net profit to total asset. It demonstrates

how banks manage their assets to generate profit.

Return on equity (ROE) is the ratio of net profit to equity. It demonstrates

how much profit that the banks can generate with the invested money from

shareholders.

3.3. Control variables

ln A is a continuous variable for controlling the asset size of the banks.

Equity to asset ratio (E/A) is used as a measurement of capital structure of

the banks.

Loan to asset ratio (L/A) is used for measuring how banks concentrate on

loan business. Moreover, if this ratio is high, it can indicate that banks rely

much on loan and it might give opportunity to explain the research

questions.

3.4. Research model

We use panel regression to construct fixed-effect on linear equation to capture

both cross-sectional and time changed. Firstly, we would like to follow the research

model of Mercieca et al. (2007)’s paper which is on equation 4. Their equation has both

HHI_R represents income structure diversification between interest income and non-

interest income. HHI_N represents the diversification within non-interest income. They

suggest that, in order to avoid perfect collinearity, eliminating one component of each

HHI has to be done before running regression. Therefore, they drop interest income

proportion and investment income proportion out of their equation.

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Before we run regression, we have to test multicollinearity to understand the

relationship between variables. We find that non-interest income proportion (NON)

and HHI_N are highly correlated to other variables so we eliminate them from the

model to avoid multicollinearity problem.

Moreover, from Mercieca et al. (2007)’s suggestion, we have to eliminate one

component of HHI_N, therefore, we choose to drop fee income proportion (𝐹𝐸𝐸𝑖𝑡)

instead of investment income proportion ( 𝐼𝑁𝑉𝑖𝑡 ). The reason is that fee income

proportion is highly related to several variables which is; HHI_N, investment income

proportion, other operating income proportion, and lnAsset. Therefore, the revised

model is derived on equation 5.

𝑦𝑖𝑡 = 𝛼 + 𝛽1𝐻𝐻𝐼_𝑅𝑖𝑡 + 𝛽2NII𝑖𝑡 + 𝛽3𝐻𝐻𝐼_𝑁𝑖𝑡 + 𝛽4𝐹𝐸𝐸𝑖𝑡 + 𝛽5𝑇𝑅𝐷𝑖𝑡 +

𝛽6𝑂𝑇𝑂𝑃𝑖𝑡 + 𝛾𝑉𝑖𝑡 + 𝑎𝑖 +𝜀𝑖𝑡 (4)

𝑦𝑖𝑡 = 𝛼 + 𝛽1𝐻𝐻𝐼_𝑅𝑖𝑡 + 𝛽2𝐼𝑁𝑉𝑖𝑡 + 𝛽3𝑇𝑅𝐷𝑖𝑡 + 𝛽4𝑂𝑇𝑂𝑃𝑖𝑡 + 𝛾𝑉𝑖𝑡 + 𝑎𝑖 +𝜀𝑖𝑡

(5)

Where 𝑦𝑖𝑡 = Dependent variables which is ROA, ROE of each

banks i for year t

HHI_Rit = Diversification measurement between interest

income and non-interest income of bank i at

time t

INVit = Proportion of net investment income to total

non-interest income of bank i at time t

TRDit = Proportion of net trading income and foreign

exchange transactions to total non- interest

income of bank i at time t

OTOPit = Proportion of other operating income to total

non-interest income of bank i at time t

𝑉𝑖𝑡 = Control variables in term of vector which is InA,

E/A, and L/A of bank i at time t

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From model 5, the coefficient of HHI_R can indicate only sign and magnitude,

but it does not indicate the reasons. The positive sign of the coefficient of HHI_R means

more concentration or less diversification will generate more return to Thai banks. On

the other hand, if the coefficient of HHI is negative it would mean that less

concentration or more diversification will generate more profit to Thai banks.

3.5 Sensitivity test

In this section, we explore more to the sensitivity of bank’s characteristic by

using the same methodology. We present two sensitivity tests by dividing banks into

two groups; by size and by loan by sector concentration.

3.5.1 Size sensitivity test

According to Bebczuka and Galindob (2008) who find that size affects banks’

profitability so the checking on size sensitivity is important. The sizes of Thai banks

are defined by Bank of Thailand by total asset. BBL, KTB, SCB, and KBANK are in

large size group which account for 68% market total asset of Thai banks. BAY,

TBANK, and TMB; are classified as a medium size group which account for 21%.

Since we want to explore whether size matters, therefore the size’s control variable is

remove from the model. Therefore, we construct a new model with most variables

similar to equation 4, unless we eliminate lnA out from the model’s control variables.

3.5.2 Sector sensitivity test

Due to limited data available to test loan by sector diversification in normal

regression, we instead run regression model to test sector sensitivity analysis. To

explore more in details, we observe the amount of loan outstanding of sector

concentration for 10 years, then we can separate banks into two groups; corporate loan

banks and retail loan banks. Corporate loan banks (BBL KBANK and TMB) focus on

wholesale customer2 whereas the remaining banks (SCB, KTB, BAY and TBANK) are

classified into retail loan bank which focus on retail customer3.

2 Wholesale customer is customer who are borrowing for their business for example, Manufacturing,

Real Estate, Construction and Mining 3 Retail customer is customer who are borrowing for their own purpose for example, Housing loan,

Hire purchase loan, and services.

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Figure 3.5 Relationship between HHI_R and net income

From statistic summary or trend of income structure as shown on table 3.1,

figure 3.2 and figure 3.4, Thai banks used to be traditional banking which focus on loan

and interest income activities but in recently years, Thai banks rely more on non-

interest income activities. From figure 3.5 shows that if HHI_R is low, the net income

will be higher. The main reasons is that after the 1997 crisis, the banking system have

been developing rapidly, Thai banks are stronger and they are quite cautious before

they run business and they will shift their activities only if such businesses bring more

return. Therefore the negative sign and significant of the coefficient of diversification

measurement (HHI_R) should be expected. Therefore, it can be concluded that Thai

banks gain benefit from changing their income structure from interest income to non-

interest income.

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CHAPTER 4

RESULTS AND DISCUSSION

4.1 Result of all banks

Table 4.1 presents the overall results of fixed effect regression model from

equation (5) which has ROA and ROE as dependent variables. The result between ROA

and ROE are very similar and the sign of coefficient of all independent variables are

the same.

HHI_R indicates that diversification from interest income to non-interest

income has no direct significant effect toward bank’s profitability. Investment income

proportion and other operating income proportion have significant and display a

positive sign which mean that more investment income proportion and other operating

income proportion make more banks’ profitability.

Table 4.1 All independent variables and profitability of all banks

Table III All independent variables and profitability of all banks

HHI_R 0.0058809 0.0983802

(1.23) (1.00)

investment income 0.0082338 ** 0.1412700 *

(2.68) (2.19)

Trading income 0.0019985 0.1106227

(0.20) (0.79)

Other operating income 0.0092812 ** 0.1343644 **

(3.33) (2.82)

lnA 0.0015312 0.0217682

(2.21) (1.68)

loan to asset ratio -0.004882 -0.0972465

(-1.49) (-1.22)

equity to asset ratio 0.0270471 ** 0.0518208

(2.51) (0.27)

Cons 0.0348477 * -0.4837207

(-2.13) (-1.57)

All banks

ROA ROE

Fixed effected regression of large and midium Thai banks. Dependent variables are return on

assets (ROA) and return on equity (ROE). All independent and all control variables are

observed. The net effect is the impact of a 1% increase in independent variable on dependent

variable. ***, **, * Indicates statistical significance at the 1%, 5%, and 10% level,

respectively.

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The equity to asset ratio is one of the control variables that significant to ROA.

The positive sign indicates that banks will have a greater profit when they have a higher

equity to asset ratio. Stiroh et. al (2006) provide the reason that banks will have more

opportunity to expand business from a higher capital buffer.

Since HHI_R is not significant and Table 4.1 provides only details of non-

interest income components; investment income proportion, trading income

proportion, and other operating income proportion, therefore, it cannot be concluded

whether banks should or should not diversify the income structure from interest income

to non-interest income.

We further perform regression on equation 6 – 8 to test interest income

proportion, non-interest income proportion, and fee income proportion to banks’

profitability separately. All equations deliver ROA and ROE as dependent variables

and utilize the same control variables that we provide on equation 5. All results come

from running fixed effect regression. The results, summary are provided on Table 4.2.

𝑦𝑖𝑡 = 𝛼 + 𝛽1𝐼𝑁𝑇𝑖𝑡 + 𝛽2𝑙𝑛𝐴𝑖𝑡 + 𝛽3𝐿/𝐴𝑖𝑡 + 𝛽4𝐸/𝐴𝑖𝑡 + 𝜀𝑖𝑡 (6)

Where 𝐼𝑁𝑇𝑖𝑡 = Proportion of net interest income to total income of

bank i at time t

𝑦𝑖𝑡 = 𝛼 + 𝛽1𝑁𝑂𝑁𝑖𝑡 + 𝛽2𝑙𝑛𝐴𝑖𝑡 + 𝛽3𝐿/𝐴𝑖𝑡 + 𝛽4𝐸/𝐴𝑖𝑡 + 𝜀𝑖𝑡 (7)

Where 𝑁𝑂𝑁𝑖𝑡 = Proportion of net non-interest income to total income

of bank i at time t

𝑦𝑖𝑡 = 𝛼 + 𝛽1𝐹𝐸𝐸𝑖𝑡 + 𝛽2𝑙𝑛𝐴𝑖𝑡 + 𝛽3𝐿/𝐴𝑖𝑡 + 𝛽4𝐸/𝐴𝑖𝑡 + 𝜀𝑖𝑡 (8)

Where 𝐹𝐸𝐸𝑖𝑡 = Proportion of net fee income to total non-interest

income of bank i at time t

20

Table 4.2 The relationship of interest income proportion, non-interest income

proportion and fee income proportion to banks’ profitability

Furthered tests Equation Results

ROA ROE

Interest income

proportion

Equation 6 Negative and

significant level 10%

Negative but not

significant

Non-interest income

proportion

Equation 7 Positive and


Positive but not

significant

Fee income

proportion

Equation 8 Negative and


Negative and


The coefficient of interest income proportion is negative and significant for

ROA, meaning that more interest income proportion decrease ROA. Whereas the

coefficient on the non-interest proportion is significant and delivers the positive sign to

ROA, which means that increasing in non-interest income proportion can provide

higher profitability. Mercieca et al. (2007) suggests that when competition increases,

banks can gain more profit from non-interest income activities.

Fee income proportion is only one of non-interest income components that

provides a negative. Moreover, it has strong significant correlation at 1% level to both

ROA and ROE. The interpretation is that when banks increase fee income proportion,

their profit will shrink. Meslier et al. (2014) suggest that fee activities are highly related

to loan activity, therefore, banks should not shift to fees.

4.2 Result of size sensitivity test

According to Bebczuka and Galindob (2008) find that size affects banks’

profitability. Their result indicates that small banks are specialized in a few activities

which can give strong return. Therefore we separate banks into two groups by size,

large banks and medium banks.

Our findings of size sensitivity is a little bit different between groups. Table 4.3

shows the size sensitivity result between all independent variables and banks’

profitability. For large banks, there is no significant relationship of independent

variables and banks’ profitability while for medium banks, we can find some significant

relationship between independent variables and ROA.

21

Table 4.3 All independent variables and profitability of banks: impact of differences

in bank size

The result of medium banks are quite similar to the results of all banks, the co-

efficient of investment income proportion and other operating income proportion are

significant and provide positive sign. It indicates that increasing in proportion of

investment income and other operating income result in higher banks’ profitability.

While the results of large banks display no significant relationship between

diversification measurement and non-interest income components to banks’

profitability.

Table 4.4 The average of non-interest income proportion through the studied periods

Non-interest income

proportion Large banks Medium banks

Fee income 0.5039 0.3502

Trading income 0.1960 0.2117

Other operating income 0.1617 0.2373

Investment income 0.1384 0.2008

HHI_R -0.0052299 -0.0684249 0.0267784 0.4193576

(-2.16) (-2.01) (2.17) (1.48)

investment income 0.0024288 0.0497431 0.0171038 * 0.2680762

(0.90) (1.04) (3.02) (1.87)

Trading income -0.0073078 -0.0439473 0.0073132 0.1692916

(-0.72) (-0.34) (0.741) (0.63)

Other operating income 0.0034922 0.0562305 0.0201619 ** 0.2765901

(1.29) (1.38) (5.01) (2.62)

loan to asset ratio -0.0034552 -0.0746268 -0.0030151 -0.0579654

(-1.27) (-1.56) (-0.76) (-0.61)

equity to asset ratio 0.0375464 * -0.0030720 0.0191586 0.0380205

(2.46) (-0.02) (1.97) (0.20)

Cons 0.0060029 0.1234736 * -0.023398 -0.3457338

(1.96) (2.48) (-2.39) (-1.78)

Fixed effected regression impact on large and medium Thai banks. Dependent variables

are return on assets (ROA) and return on equity (ROE). All independent variables are

observed and eliminate lnA from control variables. The net effect is the impact of a 1%

increase in independent variable on dependent variable. ***, **, * Indicates statistical

significance at the 1%, 5%, and 10% level, respectively.

Large banks

ROA ROE

Medium banks

ROA ROE

22

The statistics on Table 4.4 shows non-interest income proportion between large

banks and medium banks. The difference of non-interest income structure between

large banks and medium banks is large banks focus more on fee income which amounts

to 50.39% of total non-interest income while medium banks are more diversified to

other non-interest income. According to fee income proportion decreases bank’s

profitability so when medium banks have a lower fee income proportion, they can gain

higher profit from higher proportion of investment income and other operating income.

In conclusion, for both groups, HHI cannot explain banks’ profitability.

However, since large banks focus more on fee income, which is strongly negative and

significant to banks’ return, whereas medium banks are more diversified to other non-

interest income activities, therefore, medium banks can receive a higher return from

increasing proportion of investment income and other operating income.

4.3 Result of sector sensitivity

Table 4.5 shows the results on a subsample of banks who focus on wholesale

sector versus retail sector. The results illustrate that all variables do not affect retail

sector banks’ profitability, but we can find some relationship on wholesale sector

banks.

For wholesale sector banks, other operating income proportion is the only non-

interest income that is significant to banks’ profitability. The positive sign of the co-

efficient indicates that the higher proportion of other operating income will result in

higher banks’ profitability. The reason why other operating income proportion is

significant only for wholesale sector banks is because most of other operating income

are one time item and wholesale sector banks gain greater amount of one time item than

retail banks. For example, if a wholesale sector bank has profit on sale of foreclosed

properties from only 1 debtor, banks can gain large amount of profit. Moreover, large

size bank can gain higher return since lnA is positive and significant.

23

Table 4.5 All independent variables and profitability of banks: impact of differences

loan by sector

The co-efficient of loan to asset ratio provides significant and negative sign

means more loan to asset ratio will lower banks’ profit. This result confirm that banks

should not concentrate on loan activity.

In conclusion of sector sensitivity test, wholesale sector banks gain higher

return from other operating income than retail banks. Moreover, the larger the banks

are, the higher return they will have. When banks display higher loan to asset ratio,

they are more focused on loans which means that they will have higher interest income.

As mentioned before, higher interest income will lower return.

HHI_R 0.0184333 0.3954005 0.0013153 0.0020292

(2.27) (2.34) (0.33) (0.05)

investment income 0.0115478 0.2692677 0.0044147 0.0500605

(2.16) (2.10) (1.48) (1.20)

Trading income 0.0109737 0.2468220 -0.0113929 -0.0939183

(1.91) (3.59) (-0.85) (-0.58)

Other operating income 0.0057127 ** 0.1602969 * 0.0070683 0.0665026

(5.63) (3.85) (1.79) (1.40)

lnA 0.0032195 ** 0.0727590 ** 0.0007360 0.0066079

(5.24) (5.16) (1.16) (0.80)

loan to asset ratio -0.0124792 ** -0.2703811 ** -0.0010932 0.0029531

(-6.60) (-7.81) (-0.94) (0.18)

equity to asset ratio 0.0161952 -0.3366161 0.0230277 0.0202959

(1.41) (-1.66) (1.83) (0.12)

Cons -0.0727734 ** -1.6149330 ** -0.0144477 -0.1139368

(-5.38) (-4.42) (-1.13) (-0.67)

Fixed effected regression of Wholesale sector Thai banks and retail sector Thai banks.

Dependent variables are return on assets (ROA) and return on equity (ROE). All independent

and all control variables are observed. The net effect is the impact of a 1% increase in

independent variable on dependent variable. ***, **, * Indicates statistical significance at the

1%, 5%, and 10% level, respectively.

ROA ROE

Wholesale sector banks Retail sector banks

ROA ROE

24

CHAPTER 5

CONCLUSIONS AND RECOMMENDATIONS

This paper studies the income structure diversification to the profitability of

seven Thai commercial banks. All data are collected from disclosed financial report in

quarterly basis for the year 2002 - 2014. The HHI_R is assumed to be an indicator to

measure the diversification between interest income and non-interest income. Although

the results cannot indicate any relationship between diversification measurement and

banks’ profitability, higher proportion of non-interest income increases banks’

profitability.

Our findings is consistent with several studies Amidu and Wolfe (2013) who

study emerging economies, find that high competitive market in the banking sector

cause banks to shift away from traditional banking and generate more return from non-

interest activities. Meslier et al. (2014) explain that banks can increase profit by

expanding to non-interest income. However, non-interest income must not fees because

fee income is highly related to interest activity.

From all the results, we can conclude that there is no relationship between

income diversification and banks’ profitability. However, bank can gain more profit by

increasing non-interest income proportion, especially when banks increase proportion

of investment income and other operating income. Nevertheless, they need to be careful

when they intend to increase fees because it decreases in their profit.

For medium banks which have less fee income proportion comparing to large

size banks, the difference of non-interest income structure provide some benefit to

medium banks. They can get a higher profit from higher investment income proportion

and higher other operating income proportion.

For retail banks, there is no relationship between independent variables and

banks’ profitability whereas other operating income proportion provides significant

and positive for wholesale banks. The reason is that wholesale banks can gain greater

amount of one time item than retail banks. Therefore, when other operating income

proportion increases, wholesale banks can gain large amount of profit.

25

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27

BIOGRAPHY

Name Ms. Piyaon Srisailuan

Date of Birth November 11, 1986

Educational Attainment

2014: Master in Finance (M.Sc.)

2008: Business Administration program in

Finance (B.Sc.)

Work Position Examiner

Bank of Thailand

Work Experiences 2011: Credit Administration Officer

Tisco Bank

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