bank profitability in korea

8/12/2019 Bank Profitability in Korea

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(Journal of Economics and Management), 2011, Vol. 7, No. 1, 43-72

Profitability of the Korean Banking Sector:

Panel Evidence on Bank-Specific

and Macroeconomic Determinants

Fadzlan Sufian

*

Khazanah Research and Investment Strategy, Khazanah Nasional Berhad, Malaysia

and

Department of Economics, Faculty of Economics and Management, Universiti Putra

Malaysia, Malaysia

The paper analyzes the profitability of banks in Korea, while controlling for a wide

array of bank specific and macroeconomic determinants. We find that Korean banks

with lower liquidity levels tend to exhibit higher profitability. Furthermore, higher

diversification regarding banks income sources towards derivative instruments and

other fee-based activities shows a positive effect. The impacts of credit risk and

overhead costs are always negative whether we control for the macroeconomic and

financial conditions or not. Business cycle effects, particularly inflation, display a

substantial pro-cyclical impact on bank profitability. The industry concentration of

the national banking system positively and significantly affects bank performance.

The impact of the Asian financial crisis is negative, while Korean banks have been

relatively more profitable during the pre-crisis compared to the post-crisis period.

Keywords:banks, profitability, financial crisis, Korea

JEL classification:G21

Received July 27, 2009, revised April 8, 2010, accepted June 29, 2010.*Correspondence to: Khazanah Research and Investment Strategy, Khazanah Nasional Berhad, Level

35, Tower 2, Petronas Twin Towers Kuala Lumpur City Centre, 50088 Kuala Lumpur, Malaysia. e-mail:

[email protected]; [email protected]. Tel.: 603-2034-0197, Fax: 603-2034-0035. We

would like to thank the anonymous referee for the comments and suggestions. Any remaining errors areour own. The usual caveats apply.


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(Journal of Economics and Management)44

1 IntroductionIn recent years, major structural changes have occurred in the Korean banking sector.

These major changes in the Korean banking sector materialized after 1997, when

Korea suffered severe economic damage during the Asian financial crisis. Since then,

the banking sector in Korea has substantially metamorphosed under the

comprehensive financial reform programme agreed upon by the Korean government

and the International Monetary Fund (IMF). For example, the five commercial

banks which the Financial and Supervisory Commission (FSC) evaluated in June

1998 as unviable were ordered to be liquidated and their assets and liabilities

transferred to stronger banks under a purchase and assumption (P&A) arrangement.

Furthermore, Commercial Bank and Hanil Bank, which were conditionally approved

for restructuring by the FSC, were merged. Korea First Bank and Seoul Bank, which

proved to be insolvent in 1997, were recapitalized by the Government and later sold

to foreign banks.

Corporate governance in the Korean banking sector has also improved

dramatically and various financial deregulation measures have been introduced since

the Asian financial crisis. The ownership and governance structure of commercial

banks has been changed extensively by a series of amendments to the Banking Act.

In addition, new standards have been implemented to better protect shareholders

rights. The limit of a 4 percent corporate ownership ceiling for foreign investors has

been lifted and most of the regulations concerning foreign banks were abolished in

the early 1990s.1

It is reasonable to assume that these developments posed great challenges to

financial institutions in Korea as the environment in which they operated changedrapidly, a fact that consequently had an impact on the determinants of Korean banks

profitability. As Golin (2001) points out, adequate earnings are required in order for

banks to maintain solvency, to survive, grow, and prosper in a competitive

environment.

1One of the notable changes in the Korean financial market in recent years is the increasing

ownership by foreign investors. The share of total market capitalization of foreigners shareholdings has

steadily increased and totaled more than 40 percent in January 2004. In addition to the above examples,Kookmin Bank is a 74 percent foreign-held bank.


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Profitability of the Korean Banking Sector 45

As the banking sector is the backbone of the Korean economy and plays an

important financial intermediary role, its health is very critical to the health of the

general economy at large. Given the relationship between the well being of the

banking sector and the growth of the economy (Rajan and Zingales, 1998; Levine,

1998; Levine and Zervos, 1998; Cetorelli and Gambera, 2001; Beck and Levine,

2004), knowledge of the underlying factors that influence the financial sectors

profitability is therefore essential not only for the managers of the banks, but also for

numerous stakeholders such as the central banks, bankers associations,

governments, and other financial authorities. Knowledge of these factors would also

be useful to help the regulatory authorities and bank managers formulate going-

forward policies for the improved profitability of the Korean banking sector.

By using an unbalanced bank level panel data set, this study seeks to examine

the determinants of Korean banks profitability during the period 1992-2003, which

is characterized as a time of significant reform in the countrys financial sector.

While there have been extensive studies examining the profitability of the financial

sector in developed countries, empirical works on factors that influence the

performance of financial institutions in developing economies are relatively scarce.

This remainder of this paper is structured as follows. Section 2 reviews the

related studies in the literature, and is followed by Section 3 that outlines the

econometric framework. Section 4 reports the empirical findings. Finally, Section 5

concludes and offers avenues for future research.

2 Related StudiesThe empirical literature on bank profitability has mainly focused on the U.S.

banking system (Berger, 1995; Angbazo, 1997; DeYoung and Rice, 2004; Stirohand Rumble, 2006; Hirtle and Stiroh, 2007) and the banking systems in the western

and developed countries such as New Zealand (Ho and Tripe, 2002), Australia

(Williams, 2003), Greece (Pasiouras and Kosmidou, 2007; Kosmidou et al., 2007;

Athanasoglou et al., 2008; Kosmidou and Zopounidis, 2008).

By contrast, fewer studies have looked at bank performance in developing

economies. Guru et al. (2002) investigate the determinants of bank profitability in

Malaysia, using a sample of 17 commercial banks during the 1986 to 1995 period.


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The profitability determinants are divided into two main categories, namely, the

internal determinants (liquidity, capital adequacy, and expenses management) and

the external determinants (ownership, firm size, and economic conditions). Their

findings reveal that efficient expenses management is one of the most significant

factors explaining high bank profitability. Among the macro indicators, a high

interest ratio is associated with low bank profitability and inflation is found to have a

positive effect on bank performance.

Chantapong (2005) investigates the performance of domestic and foreign banks

in Thailand during the period 1995-2000. All banks are found to have reduced their

credit exposure during the crisis years and have gradually improved their

profitability during the post-crisis years. The results indicate that foreign bank

profitability is higher than the average profitability of the domestic banks. This is in

spite of the gap between foreign and domestic bank profitability having been closed

in the post-crisis period, implying that the financial restructuring program has

yielded some positive results.

Ben Naceur and Goaied (2008) examine the impact of bank characteristics,

financial structure, and macroeconomic conditions on Tunisian banks net-interest

margins and profitability during the period from 1980 to 2000. They suggest that

banks which hold a relatively high amount of capital and higher overhead expenses

tend to exhibit higher net-interest margin and profitability levels, while size is

negatively related to bank profitability. During the period under study, they find that

stock market development has a positive impact on bank profitability. The empirical

findings suggest that private banks are relatively more profitable than their state-

owned counterparts. The results indicate that macroeconomic conditions have no

significant impact on Tunisian banks profitability.

Ben Naceur and Omran (2008) analyze the influence of bank regulations,concentration, and financial and institutional development on the Middle East and

North Africa (MENA) countries commercial banks margins and profitability

during the period 1989-2005. They find that bank-specific characteristics, in

particular bank capitalization and credit risk, have a positive and significant impact

on banks net interest margins, cost efficiency, and profitability. On the other hand,

macroeconomic and financial development indicators have no significant impact on

bank performance.


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More recently, Sufian and Habibullah (2009) have investigated the

determinants of the profitability of the Chinese banking sector during the post-

reform period of 2000-2005. They find that liquidity, credit risk, and capitalization

have positive impacts on the state-owned commercial banks profitability, while the

impact of overhead cost is negative. They suggest that the joint stock commercial

banks with higher credit risk tend to be more profitable, while higher costs result in

lower joint stock commercial banks profitability levels. They find that size and cost

result in the lower profitability of city commercial banks, while the more diversified

and relatively better capitalized city commercial banks exhibit higher profitability

levels. The impact of economic growth is positive, while the growth of money

supply is negatively related to the state-owned commercial banks and city

commercial banks profitability levels.

3 Data and MethodologyWe collected our bank-specific variables from the financial statements of a sample

of commercial banks operating in Korea over the period 1992-2003 that are

available in the Bankscope database of Bureau van Dijks company. The

macroeconomic variables are retrieved from the IMF Financial Statistics (IFS)

database. Due to the consolidation and exit of banks during the past decade, the total

number of commercial banks in the sample varied from 11 banks in 1992 to 29

banks in 2000. This gives us a total of 251 bank year observations.

3.1 Performance MeasureIn the literature, bank profitability, which is typically measured by the return onassets (ROA) and/or the return on equity (ROE), is usually expressed as a function

of internal and external determinants. Internal determinants are factors that are

mainly influenced by a banks management decisions and policy objectives. Such

profitability determinants are the level of liquidity, provisioning policy, capital

adequacy, expenses management, and bank size. On the other hand, the external

determinants, both industry- and macroeconomic-related, are variables that reflect

the economic and legal environments where the financial institution operates.


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Following Sufian and Habibullah (2009), Ben Naceur and Goaied (2008), and

Kosmidou (2008) among others, the dependent variable used in this study is ROA.

ROA shows the profit earned per dollar of assets and most importantly reflects

managements ability to utilize the banks financial and real investment resources to

generate profits (Hassan and Bashir, 2003). For any bank, ROA depends on the

banks policy decisions as well as uncontrollable factors related to the economy and

government regulations. Rivard and Thomas (1997) suggest that bank profitability is

best measured by ROA given that ROA is not distorted by high equity multipliers

and ROA represents a better measure of the ability of the firm to generate returns on

its portfolio of assets. ROE, on the other hand, reflects how effectively a bank

management is in utilizing its shareholders funds. Since ROA tends to be lower for

financial intermediaries, most banks utilize financial leverage heavily to increase

ROE to competitive levels (Hassan and Bashir, 2003).

3.2 Internal DeterminantsThe bank-specific variables included in the regression models are LNTA (log of

total assets), LLP/TL (loans loss provisions divided by total loans), NII/TA (non-

interest income divided by total assets), NIE/TA (total overhead expenses divided by

total assets), LNDEPO (log of total deposits), and EQASS (book value of

stockholders equity as a fraction of total assets).

The LNTA variable is included in the regression as a proxy of size to capture

the possible cost advantages associated with size (economies of scale). This variable

controls for cost differences and product and risk diversification according to the

size of the bank. The first factor could lead to a positive relationship between size

and bank profitability if there are significant economies of scale (Akhavein et al.,1997; Bourke, 1989; Molyneux and Thornton, 1992; Bikker and Hu, 2002; Goddard

et al., 2004), while the second could lead to a negative one, if increased

diversification leads to lower credit risk and thus lower returns. Other researchers,

however, conclude that marginal cost savings can be achieved by increasing the size

of the banking firm, especially as markets develop (Berger et al., 1987; Boyd and

Runkle, 1993; Miller and Noulas, 1997; Athanasoglou et al., 2008). In essence,

LNTA may lead to positive effects on bank profitability if there are significant


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economies of scale. On the other hand, if increased diversification leads to higher

risks, the variable may exhibit negative effects.

The ratio of loan loss provisions to total loans (LLP/TL) is incorporated as an

independent variable in the regression analysis as a proxy of credit risk. The

coefficient of LLP/TL is expected to be negative because bad loans reduce bank

profitability. In this direction, Miller and Noulas (1997) suggest that the greater the

exposure of the financial institutions to high-risk loans, the higher would be the

accumulation of unpaid loans and profitability would be lower. Miller and Noulas

(1997) suggest that declines in loan loss provisions are in many instances the

primary catalyst for increases in profit margins. Furthermore, Thakor (1987) also

suggests that the level of loan loss provisions is an indication of the banks asset

quality and signals changes in future performance.

To recognize that financial institutions in recent years have increasingly been

generating income from off-balance sheet business, particularly income from

trading in the stock markets and derivative financial instruments and fee income

generally, the ratio of non-interest income over total assets (NII/TA) is entered in the

regression analysis. Non-interest income consists of commission, service charges,

and fees, guarantee fees, net profits from sales of investment securities, and foreign

exchange profits. The ratio is also included in the regression model as a proxy

measure of bank diversification into non-traditional activities. The variable is

expected to exhibit a positive relationship with bank profitability.

The ratio of overhead expenses to total assets,NIE/TA, is used to provide

information on the variations in bank operating costs. The variable represents the

total amount of wages and salaries, as well as the costs of running branch office

facilities. For the most part, the literature argues that reduced expenses improve the

efficiency and hence raise the profitability of a financial institution, implying anegative relationship between the operating expenses ratio and profitability (Bourke,

1989). However, Molyneux and Thornton (1992) observe a positive relationship,

suggesting that high profits earned by firms may be appropriated in the form of

higher payroll expenditures paid to more productive human capital.

The variable LNDEPO is included in the regression model as a proxy variable

for network embeddedness. It would be reasonable to assume that banks with large

branch networks are able to attract more deposits, which is a cheaper source of funds.


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Earlier studies by Chu and Lim (1998) among others point out that large banks may

attract more deposits and loan transactions and in the process command larger

interest rate spreads, while the smaller banking groups with smaller depositor bases

might have to resort to purchasing funds in the inter-bank market, which is more

costly (Lim and Randhawa, 2005). On the other hand, Lim and Randhawa (2005)

suggest that the small banks, with their smaller depositor bases and thus fewer

deposits to transform into loans, have attained higher efficiency levels compared to

their larger counterparts.

EQASS is included in the regressions to examine the relationship between

profitability and bank capitalization. Even though leverage (capitalization) has been

demonstrated to be important in explaining the performance of financial institutions,

its impact on bank profitability is ambiguous. As lower capital ratios suggest a

relatively risky position, one might expect a negative coefficient on this variable

(Berger, 1995). However, it could be the case that higher levels of equity would

decrease the cost of capital, leading to a positive impact on bank profitability

(Molyneux, 1993). Moreover, an increase in capital may raise expected earnings by

reducing the expected costs of financial distress, including bankruptcy (Berger,

1995).

3.3 External DeterminantsBank profitability is sensitive to macroeconomic conditions despite the trend in the

industry towards greater geographic diversification and the larger use of financial

engineering techniques to manage risk associated with business cycle forecasting.

Generally speaking, higher economic growth encourages banks to lend more and

permits them to charge higher margins, as well as improving the quality of theirassets. Neely and Wheelock (1997) use per capita income and suggest that this

variable exerts a strong positive effect on bank earnings. Dermiguc-Kunt and

Huizinga (2001) and Bikker and Hu (2002) identify possible cyclical movements in

bank profitability, i.e., the extent to which bank profits are correlated with the

business cycle. Their findings suggest that such correlation exists, although the

variables used are not direct measures of the business cycle.


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To measure the relationship between economic and market conditions and bank

profitability, LNGDP (the natural log of GDP), INFL (the rate of inflation), CR3

(the ratio of the three largest banks assets), MKTCAP (the ratio of the stock market

capitalization over GDP), DUMTRAN1 (a dummy variable that takes a value of 1

for the first tranquil (pre-crisis) period, and 0 otherwise), DUMCRIS (a dummy

variable that takes a value of 1 for the crisis period, and 0 otherwise), and

DUMTRAN2 (a dummy variable that takes a value of 1 for the second tranquil

(post-crisis) period, and 0 otherwise) are used.

Gross domestic product (GDP) is among the macroeconomic indicators most

commonly used to measure total economic activity within an economy. The GDP is

expected to influence numerous factors related to the supply and demand for loans

and deposits. Favorable economic conditions will also positively influence the

demand for and supply of banking services. Another important macroeconomic

condition which may affect both the costs and revenues of banks is the inflation rate

(INFL). Staikouras and Wood (2003) point out that inflation may have direct effects,

i.e., increases in the price of labor, and indirect effects, i.e., changes in interest rates

and asset prices on the profitability of banks. Perry (1992) suggests that the effects

of inflation on bank performance depend on whether the inflation is anticipated or

unanticipated. In the anticipated case, the interest rates are adjusted accordingly,

thereby causing revenues to increase faster than costs and to subsequently positively

impact bank profitability. On the other hand, in the unanticipated case, banks may be

slow in adjusting their interest rates, resulting in bank costs increasing faster than

bank revenues, and consequently having a negative effect on bank profitability.

Earlier studies by Bourke (1989), Molyneux and Thornton (1992), Dermiguc-Kunt

and Huizinga (1999), among others, have found a positive relationship between

inflation and bank performance.The CR3 variable, measured as the concentration ratio of the three largest

banks in terms of assets, is entered in the regression models as a proxy variable for

the banking sector structure. According to the industrial organization literature, a

positive impact is expected under both views, i.e., the collusion versus the efficiency

views (Goddard et al., 2001).

Following Dermiguc-Kunt and Huizinga (1999) among others, MKTCAP is

introduced to the regression model to reflect the complementarity or substitutability


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between bank and stock market financing. Dermiguc-Kunt and Huizinga (1999) find

that stock market capitalization to bank assets is negatively related to bank margins

and suggests that the relatively well-developed stock markets can substitute for bank

finance. We therefore expect the variable to be negatively related to bank

performance.

To capture the impact of the Asian financial crisis on the profitability of the

Korean banking sector, DUMTRAN1, DUMCRIS, and DUMTRAN2 are included

in regression models 3, 4, and 5, respectively. We expect the Korean banking sector

to exhibit higher profitability levels during both the tranquil periods, i.e.,

DUMTRAN1 and DUMTRAN2, while DUMCRIS is expected to exhibit a negative

relationship with the Korean banks profitability.

Table 1 lists the variables used to proxy profitability and its determinants. We

also include the notation and the expected effect of the determinants according to the

literature.

Table 2 presents the summary statistics of the dependent and the explanatory

variables.

3.4 Econometric SpecificationTo test the relationship between bank performance and the bank specific and

macroeconomic determinants described earlier, we estimate a linear regression

model in the following form:

jtejtitijtjttitXXy +++= , (1)

where j refers to an individual bank; t refers to year;it

y refers to the return on

assets (ROA) and is the observation of bank j in a particular year t ; iX represents the internal factors (determinants) of a bank;

eX represents the external

factors (determinants) of a bank; andjt

is a normally distributed random variable

disturbance term. We apply the least squares method of the fixed effects (FE) model,

where the standard errors are calculated by using Whites (1980) transformation to

control for cross-sectional heteroskedasticity. The opportunity to use a fixed effects

rather than a random effects model has been tested using the Hausman test.


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Table 1: Description of the Variables Used in the Regression Models

Variable Description Hypothesized

Relationship with

Profitability

Dependent

ROA The return on average total assets of bankjin year t. NA

ROE The return on average total shareholders equity of bankjin

year t.

NA

Independent

Internal Factors

LNTA The natural logarithm of the accounting value of the total

assets of bankjin year t.

+/

LOANS/TA A measure of liquidity, calculated as total loans/ total assets.

The ratio indicates what percentage of the assets of the bank

is tied up in loans in year t.

+

LLP/TL Loan loss provisions/ total loans. An indicator of credit risk,

which shows how much a bank is provisioning in year t

relative to its total loans.

NII/TA A measure of diversification and business mix, calculated as

non-interest income/ total assets of bankjin year t.

+

NIE/TA Calculated as non-interest expense/ total assets and provides

information on the efficiency of the management regarding

expenses relative to the assets in year t. Higher ratios imply

a less efficient management.

LNDEPO LNDEP is a proxy measure of network embeddedness,

calculated as the log of total deposits of bankjin year t.

+/

EQASS A measure of bank js capital strength in year t, calculated

as equity/ total assets. A high capital asset ratio is assumed

to be an indicator of low leverage and therefore lower risk.

+/

External Factors

LNGDP Natural logarithm of gross domestic product. +/

INFL The annual inflation rate. +/

CR3 The three largest banks asset concentration ratio +/

MKTCAP The ratio of stock market capitalization. The variable serves

as a proxy of financial development.

DUMTRAN1 Dummy variable that takes a value of 1 for the first tranquil

(pre crisis) period, and 0 otherwise.

+

DUMCRIS Dummy variable that takes a value of 1 for the crisis period,

and 0 otherwise.

DUMTRAN2 Dummy variable that takes a value of 1 for the second

tranquil (post crisis) period, and 0 otherwise.

+


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Table 2: Summary Statistics of Dependent and Explanatory Variables

ROA ROE LNTA LOANS/

TA

LLP/

TL

NII/

TA

NIE/

TA

LNDEPO EQASS LNGDP INFL CR3 MKTCAP

Mean 0.01 0.34 15.57 0.60 0.01 0.02 0.03 15.14 0.06 13.17 4.04 0.36 0.39

Min 0.12 17.00 9.81 0.19 0.11 0.10 0.01 11.01 0.06 12.83 1.40 0.24 0.18

Max 0.08 4 .86 19.05 4.79 0 .15 0.12 0.17 18.72 1.12 13.40 6.60 0.47 0.58

Std. Dev. 0.03 1.82 1.98 0.32 0.03 0.02 0.02 1.91 0.08 0.16 1.43 0.09 0.12

Note: The table presents the summary statistics of the variables used in the regression analysis.

By extending equation (1) to reflect the variables, as described in Table 1, the

baseline model is formulated as follows:

.2

1

3

//

//

7

654

3217

654

3210

jt

t

tttjt

jtjtjt

jtjtjtjt

DUMTRAN

DUMCRISDUMTRANMKTCAP

CRINFLLNGDPEQASS

LNDEPOTANIITANII

TLLLPTALOANSLNTAROA

++

+++

++++

+++

+++=

(2)

Table 3 provides information on the degree of correlation between the

explanatory variables used in the multivariate regression analysis. The matrix shows

that, in general, the correlation between the bank-specific variables is not strong,

suggesting that multicollinearity problems are not severe or non-existent. Kennedy

(2008) points out that multicollinearity is a problem when the correlation is above

0.80, which is not the case here. However, it is worth noting that the correlations

among the macroeconomic and market condition variables are relatively high. To

address this concern, we have estimated all the regression models by using a step-

wise regression. The empirical findings do not qualitatively change our results.

Therefore, we choose not to report the regression results in the paper, but they are

available upon request.


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Table 3: Correlation Matrix for the Explanatory Variables

The notation used in the table below is defined as follows: LLP/TL is a measure of bank risk calculated as the ratio of total loan loss provisions divided by total loans; NII/TA is a measure of bank diversification towards

calculated as total non-interest income divided by total assets; NIE/TA is a proxy measure for management quality, calculated as personnel expenses divided by total assets; LOANS/TA is used as a proxy measur

calculated as total loans divided by total assets; LNTA is a proxy measure of size, calculated as a natural logarithm of total bank assets; LNDEPO is a proxy measure of network embeddedness, calculated as the log of tot

is a measure of capitalization, calculated as book value of shareholders equity as a fraction of total assets; LNGDP is natural log of gross domestic products; DUMTRAN1 is a dummy variable that takes a value of 1 for t

crisis) period, 0 otherwise; DUMCRIS is a dummy variable that takes a value of 1 for the crisis period, 0 otherwise; DUMTRAN2 is a dummy variable that takes a value of 1 for the second tranquil (post crisis) period,

Independent

Variables

LNTA LOANS/TA LLP/TL NII/TA NIE/TA LNDEPO EQASS LNGDP INFL CR3 MKTCAP DUMTRAN1 DUMCR

LNTA 1.000

LOANS/TA 0.333** 1.000

LLP/TL 0.156* 0.138* 1.000

NII/TA 0.001 0.104 0.071 1.000

NIE/TA 0.137* 0.049 0.169** 0.531** 1.000

LNDEPO 0.981** 0.253** 0.142* 0.032 0.055 1.000

EQASS 0.361** 0.749** 0.052 0.113 0.070 0.328** 1.000

LNGDP 0.195** 0.353** 0.344** 0.118 0.074 0.133* 0.006 1.000

INFL 0.143* 0.158* 0.415** 0.172** 0.109 0.098 0.008 0.498** 1.000

CR3 0.210** 0.297** 0.330** 0.069 0.084 0.145* 0.002 0.752** 0.667** 1.000

MKTCAP 0.124* 0.197** 0.452** 0.347** 0.121 0.079 0.042 0.448** 0.820** 0.490** 1.000

DUMTRAN1 0.186** 0.241** 0.291** 0.085 0.015 0.142* 0.041 0.764** 0.508** 0.672** 0.281** 1.000

DUMCRIS 0.040 0.098 0.245** 0.464** 0.162* 0.011 0.055 0.133* 0.426** 0.239** 0.715** 0.328** 1.000

DUMTRAN2 0.204** 0.300** 0.462** 0.289** 0.142* 0.140* 0.005 0.810** 0.806** 0.809** 0.826** 0.663** 0.491**

Note: The table presents the results from Spearman correlation coefficients.**and *indicates significance at 1% and 5% levels.


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4 Empirical FindingsIt is in the public interest to know what banks can do to improve their profitability so

that scarce resources are allocated to their best uses and not wasted during the

production of services and goods (Isik and Hassan, 2003). For this purpose, we

investigate whether any aspects of the banks are related to their degree of

profitability. In the preceding analysis, we will discuss the performance of the

Korean banking sector based on the results derived from a series of parametric and

non-parametric tests, before we embark on a discussion of the results derived from a

multivariate regression setting.

4.1 The Performance of the Korean Banking Sector: A UnivariateSetting

To examine the difference in the relative performance of the Korean banking sector

during the pre and post-crisis periods, we perform a series of parametric (t-test) and

non-parametric (Mann-Whitney [Wilcoxon] and Kruskall-Wallis) tests. The results

are presented in Table 4. It is observed that on average the Korean banking sector

has been relatively more profitable during the pre-crisis period under both

profitability measures, i.e., ROA and ROE (significant at the 5% level or better

under both the parametric t-test and non-parametric Mann-Whitney [Wilcoxon] and

Kruskall-Wallis tests). The empirical findings also suggest that the Korean banking

sector has been relatively larger ( 16819.1512692.16 ) and has incurred higher

overhead expenses ( 0.024470.02795 ) during the pre-crisis period (statistically

significant at the 1% level in the non-parametric Mann-Whitney [Wilcoxon] andKruskall-Wallis tests).

It is observed from Table 4 that Korean banks credit risk has been lower

during the pre-crisis period ( 0.026950.00262 ) and is statistically significant at the

1% level under both the parametric t-test and non-parametric Mann-Whitney

[Wilcoxon] and Kruskall-Wallis tests. We also find that Korean banks have derived

a higher proportion of income from non-interest sources during the pre-crisis period

( 0.012460.01578 ), but it is not statistically significant at any conventional levels


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Table 4: Summary of Parametric and Non-Parametric Tests

Test Groups

Parametric Test Non-Parametric Test

Individual

Tests

t-test Mann-Whitney

[Wilcoxon Rank-Sum]

test

Kruskall-Wallis

Equality of Populations

test

Test Statistics t ( t>Prb ) z ( z>Prb )

Mean t Mean

Rank

z 2

(2

Prb > )

ROA

Pre-Crisis

Post-Crisis

0.00404

0.00770

3.495***

113.68

94.00

2.328** 5.422**

ROE

Pre-Crisis

Post-Crisis

0.06675

0.27247

2.714***

110.61

95.89

1.739* 3.023*

LNTA

Pre-Crisis

Post-Crisis

16.12692

15.16819

3.414***

115.20

93.06

2.615***

6.836***

LOANS/TA

Pre-Crisis

Post-Crisis

0.48506

0.69276

4.388***

56.59

129.16

8.570***

73.443***

LLP/TL

Pre-Crisis

Post-Crisis

0.00262

0.02695

6.279***

57.44

128.64

8.495***

72.161***

NII/TA

Pre-Crisis

Post-Crisis

0.01578

0.01246

1.207 107.96

97.52

1.234 1.523

NIE/TA

Pre-Crisis

Post-Crisis

0.02795

0.02447

1.598 125.78

86.54

4.640***

21.534***

LNDEPO

Pre-Crisis

Post-Crisis

15.54269

14.86871

2.462** 111.45

95.37

1.900* 3.608*

EQASS

Pre-Crisis

Post-Crisis

0.06326

0.05901

0.358 120.33

89.90

3.594***

12.918***

Note: The testing methodology follows Aly et al. (1990), Elyasiani and Mehdian (1992), and Isik and

Hassan (2002), among others. The parametric (t-test) and non-parametric (Mann-Whitney and Kruskall-

Wallis) tests test the null hypothesis of an equal mean between the two models.***, **, *denotes significance at the 1%, 5%, and 10% levels, respectively.

under both the parametric t-test and non-parametric Mann-Whitney [Wilcoxon] and

Kruskall-Wallis tests. It is also apparent that the Korean banking sector has better


16/30


network embeddedness ( 14.8687115.54269 > ) during the pre-crisis period

(statistically significant at the 5% level under the parametric t-test). The results from

the parametric t-test are further confirmed by the non-parametric Mann-Whitney

[Wilcoxon] and Kruskall-Wallis tests albeit at a lower significance level. Finally, it

is interesting to note that the Korean banking sector seems to be relatively better

capitalized during the pre-crisis period and is statistically significant at the 1% level

under the non-parametric Mann-Whitney [Wilcoxon] and Kruskall-Wallis tests.

4.2 Determinants of Bank Profitability: A Multivariate AnalysisThe regression results focusing on the relationship between bank profitability and

the explanatory variables are presented in Table 5. To conserve space, the full

regression results, which include both bank- and time-specific fixed effects, are not

reported in the paper. Several general comments regarding the test results are

warranted. The model performs reasonably well with most variables remaining

stable across the various regressions tested. The explanatory power of the models is

also reasonably high, while the F-statistics for all models are significant at the 1%

level.

The relationship between size (LNTA) and Korean banks profitability is

positive, a fact that supports the results of Spathis et al. (2002) and Kosmidou

(2008). Hauner (2005) offers two potential explanations for which size could have a

positive impact on bank performance. First, if it relates to market power, large banks

should pay less for their inputs. Second, there may be increasing returns to scale

through the allocation of fixed costs (e.g., research or risk management) over a

higher volume of services or from efficiency gains from a specialized workforce.

However, the coefficient is not statistically significant at any conventional levels inall of the regression models estimated.

Referring to the impact of bank liquidity, LOANS/TA is positively related to

the profitability of Korean banks, indicating a negative relationship between bank

profitability and the level of liquid assets held by the bank. As higher figures for the

ratio denote lower liquidity, the results imply that more (less) liquid banks tend to

exhibit lower (higher) profitability levels. As pointed out by Sufian (2009), the

positive relationship found between bank profitability and LOANS/TA may be


17/30


supporting the efficient markets hypothesis, since market power in the loan markets

could be the result of efficient operations. Due to their ability to manage operations

more productively, relatively efficient banks might have lower production costs,

which enable them to offer more reasonable loan terms and ultimately gain larger

market shares over their inefficient peers.

Table 5: Panel Fixed and Random Effects Regression Results - ROA

jt

jt

jtjtjt

jtjtjtjt

DUMTRAN


CRINFLLNGDPEQASS

LNDEPOTANIETANII

TLLLPTALOANSLNTAROA

++

+++

++++

+++

+++=

2

1

3

//

//

7

654

3217

654

3210

The dependent variable is ROA calculated as net profit divided by total assets; LNTA is a proxy measure

of size, calculated as a natural logarithm of total bank assets; LOANS/TA is used as a proxy measure of

loans intensity, calculated as total loans divided by total assets; LLP/TL is a measure of bank credit risk

calculated as the ratio of total loan loss provisions divided by total loans; NII/TA is a measure of bank

diversification towards non-interest income, calculated as total non-interest income divided by total assets;

NIE/TA is a proxy measure for management quality, calculated as personnel expenses divided by total

assets; LNDEPO is a proxy measure of network embeddedness, calculated as the log of total deposits;

EQASS is a measure of capitalization, calculated as the book value of shareholders equity as a fraction oftotal assets; LNGDP is the natural log of gross domestic product; INFL is the rate of inflation; CR3 is the

three bank concentration ratio; MKTCAP is the ratio of stock market capitalization divided by GDP;

DUMTRAN1 is a dummy variable that takes a value of 1 for the first tranquil (pre crisis) period, and 0

otherwise; DUMCRIS is a dummy variable that takes a value of 1 for the crisis period, and 0 otherwise;

and DUMTRAN2 is a dummy variable that takes a value of 1 for the second tranquil (post crisis) period,

and 0 otherwise.

(1) (2) (3) (4) (5)

CONSTANT 0.070559

(1.449393)

0.021953

(0.088732)

0.250111

(1.140400)

0.076477

(0.386152)

0.128424

(0.370957)

Bank

CharacteristicsLNTA 0.002619

(0.203503)

0.004155

(0.322971)

0.006991

(0.533809)

0.007206

(0.563736)

0.004141

(0.323730)

LOANS_TA 0.029880**

(2.540711)

0.024043**

(2.195571)

0.024202**

(2.211245)

0.023780**

(2.160525)

0.024402**

(2.217046)

LLP/TL 0.469738***

(2.891933)

0.428787***

(3.237606)

0.414561***

(3.059252)

0.417854***

(3.134731)

0.425236***

(3.168178)

NII/TA 0.728529***

(7.535184)

0.649505***

(9.177574)

0.630097***

(8.261724)

0.628930***

(8.329660)

0.649352***

(9.104791)


18/30


(1) (2) (3) (4) (5)

NIE/TA 0.843040***

(4.788621)

0.748699***

(5.427843)

0.705193***

(5.107667)

0.683008***

(4.907431)

0.764581***

(5.617721)

LNDEPO 0.001740

(0.125761)

0.005647

(0.364840)

0.002364

(0.148470)

0.002936

(0.189836)

0.004983

(0.318695)

EQASS 0.018313

(0.254844)

0.031989

(0.423491)

0.031462

(0.408546)

0.034806

(0.446557)

0.029186

(0.386090)

Economic andMarket Conditions

LNGDP 0.019298

(0.923546)

0.002208

(0.115034)

0.010497

(0.614542)

0.007524

(0.261556)

INFL 0.008630***

(4.989742)

0.007149***

(5.603595)

0.007310***

(4.883670)

0.008411***

(5.107783)

CR3 0.053100**

(2.057333)

0.056716***

(3.115341)

0.040125**

(2.256394)

0.067063***

(3.251060)

MKTCAP 0.064593***

(2.852045)

0.050429**

(2.548016)

0.029571

(1.142165)

0.081069**

(2.492021)

DUMTRAN1 0.010463***

(3.622630)

DUMCRIS 0.011191***

(2.893924)

DUMTRAN2 0.009279

(0.897570)

R2 0.660158 0.707700 0.717774 0.716870 0.709031

AdjustedR2 0.601124 0.650358 0.660786 0.659700 0.650277

DurbinWatson

statistic1.439565 1.618103 1.557754 1.558969 1.607438

Fstatistic 11.18275*** 12.34190*** 12.59520*** 12.53917*** 12.06790***

No. of

Observations251 251 251 251 251

Values in parentheses are t-statistics.***, **, and *denote significance at the 1, 5, and 10% levels, respectively.

As expected, the impact of credit risk (LLP/TL) has a negative relationship

with bank profitability and is statistically significant at the 1% level in all regression

models, suggesting that banks with higher credit risk exhibit lower profitability

levels. The results imply that Korean banks should focus more on credit risk


19/30


management, which has been proven to be problematic in the recent past. Serious

banking problems have arisen from the failure of financial institutions to recognize

impaired assets and create reserves for writing off these assets. An immense help

towards smoothing out these anomalies would be provided by improving the

transparency of the banking sector, which in turn will assist banks to evaluate credit

risk more effectively and avoid problems associated with hazardous exposure.

The coefficient of NII/TA is positive and is statistically significant at the 1%

level whether we control for macroeconomic and market conditions or not. The

results imply that banks which derived a higher proportion of their income from

non-interest sources such as derivatives and stock market trading and other fee based

services tend to report a higher level of profitability levels. It is observed from Table

5 that the intensity of NII/TA towards Korean banks profitability is relatively high.

We find that Korean banks profitability would increase in the range of 63 to 73 per

cent for every 1 per cent increase in non-interest income. The empirical findings are

consistent with an earlier study by Canals (1993) who suggests that revenues

generated from new business units have significantly contributed to improving bank

performance. On the other hand, Stiroh (2004) suggests that greater reliance on non-

interest income, particularly trading revenue, is associated with lower risk-adjusted

profits and higher risk, while Stiroh and Rumble (2006) find that the diversification

benefits of U.S. financial holding companies are offset by the increased exposure to

non-interest activities, which are much more volatile, but not necessarily more

profitable, than interest-generating activities.

As for the impact of overhead costs, NIE/TA exerts a negative and significant

impact on bank profitability. The results imply that an increase (decrease) in these

expenses reduces (increases) the profits of banks operating in Korea. Pasiouras and

Kosmidou (2007) and Kosmidou (2008), among others, have also found poorexpenses management to be among the main contributors to poor profitability.

Clearly, efficient cost management is a prerequisite for the improved profitability of

the Korean banking sector, i.e., the high elasticity of profitability of this variable

indicates that banks have much to gain if they improve their managerial practices.

Furthermore, the Korean banking sector has not reached the maturity level required

to link quality effects from increased spending to higher bank profitability.


20/30


The impact of network embeddedness (LNDEPO) on bank profitability is

positive, supporting the earlier findings of Lim and Randhawa (2005) and Sufian

(2007), among others, which indicate that the large banks are relatively more

managerially efficient. It could be argued that the large banks with extensive branch

networks across the nation may have an advantage over smaller bank counterparts as

they may attract more deposits and loan transactions, and in the process command

larger interest rate spreads and subsequently higher levels of profitability.

The level of capitalization (EQASS) is positively related to the profitability of

Korean banks and is statistically significant at the 5% level or better in all regression

models. This empirical finding is consistent with Berger (1995), Dermiguc-Kunt and

Huizinga (1999), Staikouras and Wood (2003), Goddard et al. (2004), Pasiouras and

Kosmidou (2007), and Kosmidou (2008), providing support to the argument that

well-capitalized banks face lower costs of going bankrupt, thus reducing their cost

of funding. Furthermore, a strong capital structure is essential for banks in

developing economies, since it provides additional strength to withstand financial

crises and increased safety for depositors facing unstable macroeconomic conditions.

The results regarding the impact of GDP growth on ROA are mixed. It is

observed from Table 5 that the coefficient of GDP is negative, but it becomes

positive when we control for both the crisis and the tranquil periods. However, the

coefficient of the variable is not statistically significant in any of the regression

models estimated. The impact of inflation (INFL) is positively related to the Korean

banks profitability, implying that during the period under study the levels of

inflation were anticipated by the Korean banks. This gave them the opportunity to

adjust the interest rates accordingly and consequently to earn higher profits. This

result is consistent with the findings by Pasiouras and Kosmidou (2007) among

others.During the period under study, we find that the impact of concentration (CR3)

is positive and is statistically significant at the 5% level or better in all regression

models. If anything could be delved, the empirical findings clearly support the

Structure-Conduct-Performance (SCP) hypothesis. To recap, the SCP hypothesis

states that banks in highly concentrated markets tend to collude and therefore earn

monopoly profits (Short, 1979; Gilbert, 1984; Molyneux et al., 1996).


21/30


The impact of stock market capitalization (MKTCAP) on bank profitability is

positive and is significant at the 5% level or better, implying that during the period

under study the Korean stock market is a relatively developed market, and thus

offers substitution possibilities to potential borrowers. However, it is interesting to

note that the coefficient of the variable loses its explanatory power when we control

for the Asian financial crisis period (DUMCRIS), suggesting that the stock market

does not exert any influence on the profitability of the Korean banking sector during

the period of economic turbulence.

As expected, the empirical findings seem to suggest that Korean banks have

been relatively more profitable during the first tranquil period (DUMTRAN1)

implying that the Korean banking sector has been relatively more profitable during

the pre-Asian financial crisis period compared to the post-crisis and crisis periods. It

is also observed from Table 5 that the coefficient of DUMCRIS is negative and is

statistically significant at the 1% level, thus supporting the notion that the Asian

financial crisis has a significant negative impact on the profitability of the Korean

banking sector.

4.3 Robustness Checks: Alternative Measure of Bank ProfitabilityIn order to check for the robustness of the results, we have performed a number of

sensitivity analyses. First, we replace the ratio of the return on assets (ROA) with the

ratio of the return on equity (ROE) as the dependent variable. The results are

presented in Table 6. It is worth noting that all the regression models perform

reasonably well with most of the baseline variables coefficients staying the same:

they keep the same sign, the same order of magnitude, they remain significant as

they were in the baseline regressions (albeit sometimes at different levels) and, withfew exceptions, they do not become significant if they were not in the baseline

regressions.

It is interesting to note that when we replace ROE as the dependent variable,

the coefficient of CR3 loses its explanatory power in most of the regression models

estimated and is only statistically significant at the 10% level in regression model 5.

In a similar vein, the empirical findings seem to suggest that the coefficient of

DUMCRIS as a proxy variable for the Asian financial crisis remains negative in the


22/30


regression model, but no longer statistically significantly explains the variations in

the Korean banks profitability.

Table 6: Panel Fixed and Random Effects Regression Results ROE

jt

jt

jtjtjt

jtjtjtjt

DUMTRAN


CRINFLLNGDPEQASS

LNDEPOTANIETANII

TLLLPTALOANSLNTAROE

++

+++

++++

+++

+++=

2

1

3

//

//

7

654

3217

654

3210

The dependent variable ROE is calculated as net profit divided by total shareholders equity; LNTA is a

proxy measure of size, calculated as a natural logarithm of total bank assets; LOANS/TA is used as a

proxy measure of loan intensity, calculated as total loans divided by total assets; LLP/TL is a measure of

bank credit risk calculated as the ratio of total loan loss provisions divided by total loans; NII/TA is a

measure of bank diversification towards non-interest income, calculated as total non-interest income

divided by total assets; NIE/TA is a proxy measure for management quality, calculated as personnel

expenses divided by total assets; LNDEPO is a proxy measure of network embeddedness, calculated as

the log of total deposits; EQASS is a measure of capitalization, calculated as the book value of

shareholders equity as a fraction of total assets; LNGDP is the natural log of gross domestic product;

INFL is the rate of inflation; CR3 is the three bank concentration ratio; MKTCAP is the ratio of stock

market capitalization divided by GDP; DUMTRAN1 is a dummy variable that takes a value of 1 for the

first tranquil (pre-crisis) period, and 0 otherwise; DUMCRIS is a dummy variable that takes a value of 1

for the crisis period, and 0 otherwise; DUMTRAN2 is a dummy variable that takes a value of 1 for the

second tranquil (post-crisis) period, and 0 otherwise.

(1) (2) (3) (4) (5)

CONSTANT 1.343613

(0.651463)

0.114728

(0.010471)

14.02609

(1.401453)

4.854258

(0.507500)

8.984372

(0.442980)

Bank

Characteristics

LNTA 0.090190

(0.170328)

0.040194

(0.061386)

0.187615

(0.278228)

0.194195

(0.277654)

0.039348

(0.059683)

LOANS_TA 0.852899**

(1.979422)

0.593311*

(1.674857)

0.601533*

(1.899558)

0.580004*

(1.818440)

0.615023*

(1.785918)

LLP/TL 11.09474***

(2.764171)

10.96305***

(2.645905)

10.22359**

(2.487381)

10.41112**

(2.552723)

10.74813**

(2.568973)

NII/TA 25.58707***

(2.967671)

22.81365***

(2.636403)

21.80491**

(2.488447)

21.77502**

(2.411209)

22.80440***

(2.612622)

NIE/TA 46.85006***

(4.229252)

41.86026***

(4.232723)

39.59898***

(3.914223)

38.54404***

(3.469264)

42.82126***

(4.070992)


23/30


(1) (2) (3) (4) (5)

LNDEPO 0.024163

(0.052865)

0.035795

(0.057771)

0.134851

(0.220910)

0.101062

(0.163678)

0.004372

(0.007049)

EQASS 0.863157

(0.308512)

1.576571

(0.569097)

1.549146

(0.561274)

1.718772

(0.614701)

1.406947

(0.510442)

Economic and

Market

Conditions

LNGDP 0.285526

(0.259694)

0.832250

(0.842886)

0.158760

(0.162453)

0.426917

(0.228632)

INFL 0.292381***

(2.783704)

0.215397***

(3.124804)

0.225757***

(3.626002)

0.279114**

(2.367351)

CR3 1.276970

(0.738428)

1.464960

(1.009721)

0.622003

(0.476577)

2.121909*

(1.612584)

MKTCAP 2.932080**

(2.349064)

2.195883***

(2.654284)

1.164070

(1.049647)

3.929001***

(2.776224)

DUMTRAN1 0.543836***

(2.627242)

DUMCRIS 0.564971

(1.438797)

DUMTRAN2 0.561435

(0.805503)

R2 0.307530 0.318303 0.323618 0.322867 0.319255

AdjustedR2 0.187242 0.184573 0.187041 0.186138 0.181796

Durbin-Watson

statistic

1.925437 1.928966 1.940680 1.932229 1.937097

F-statistic 2.556607*** 2.380192*** 2.369486*** 2.361365*** 2.322558***

No. of

Observations

251 251 251 251 251

Values in parentheses are t-statistics.***, **, and *denote significance at the 1%, 5%, and 10% levels.


24/30


4.4 Additional Robustness ChecksTo further check for the robustness of the results, we conduct several additional tests.

Firstly, we restrict our sample to banks with more than three years of observations.

All in all, the results remain qualitatively similar in terms of directions and

significance levels. Secondly, we address the effects of outliers in the sample by

removing the data and exclude the top and bottom 1% of the sample. The resultsremain robust in terms of directions and significance levels. Thirdly, we replace

ROA with PBT/TA (Profit Before Tax/Total Assets) and repeat Eq. (1). In general,

the results confirm the baseline regression results. Finally, we perform the test for

redundancy to ensure that all the variables incorporated in the regression models are

relevant. All in all the results of this test fail to be rejected at the 5% level that the

explanatory variables are redundant in any of the regression models estimated. To

conserve space, we do not report the full regression results in the paper, but they are

available upon request.

5 Concluding Remarks and Directions for FutureResearch

The Asian financial crisis has had a profound negative impact on the Korean

banking sector. The sharp decline in the domestic currency had damaging effects on

the leading banks balance sheets. Moreover, banks revenues shrank as the banks

could not pass on higher rates to distressed corporate borrowers, subsequently

resulting in negative interest rate spreads and reductions in banks net income that

damaged their capital adequacy.

By using unbalanced bank level panel data, this study seeks to examine the

determinants of Korean banks profitability during the period 1992-2003. The

empirical findings suggest that Korean banks with lower liquidity levels tend to

exhibit higher profitability levels. Furthermore, higher diversification regarding

banks income sources towards derivative instruments and other fee-based activities

has a positive effect. The impact of credit risk and costs are always negative.

Business cycle effects exert a substantial pro-cyclical impact on bank profits. The


25/30


industry concentration of the national banking system positively and significantly

affects banks profitability. The impact of the Asian financial crisis is negative,

while Korean banks have been relatively more profitable during the pre-crisis period

compared to the post-crisis period.

The findings of this study have considerable policy relevance. It could be

argued that the more profitable financial institutions will be able to offer more new

products and services. To this end, the role of technology advancement is

particularly important given that a financial institution with relatively more

advanced technologies may have an added advantage over its peers. The continued

success of the Korean banking sector depends on its efficiency, profitability, and

competitiveness. Furthermore, in view of the increasing competition attributed to the

more liberalized banking sector, bank management as well as the policymakers will

be more inclined to find ways of obtaining the optimal utilization of capacities as

well as making the best use of their resources, so that these resources are not wasted

during the production of banking products and services.

Moreover, the ability to maximize risk-adjusted returns on investment and

sustaining stable and competitive returns is an important element in ensuring the

competitiveness of the Korean banking sector. Thus, from the regulatory perspective,

the performance of the financial sector will be based on their efficiency and

profitability. The policy direction will be directed towards enhancing the resilience

and efficiency of the financial institutions with the aim of intensifying the robustness

and stability of the financial sector.

Future research could include more variables such as taxation and regulation

indicators, exchange rates as well as indicators of the quality of the offered services.

Another possible extension could be the examination of differences in the

determinants of profitability between small and large or high and low profitabilitybanks. In terms of methodology, a statistical cost accounting and frontier techniques

could also be used.


26/30


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