Pornchai Chunhachinda, Li Li Thammasat University (Chunhachinda), University of the Thai Chamber of Commerce (Li), Bangkok, Thailand Income Structure, Competitiveness, Profitability and Risk: Evidence from Asian Banks Abstract: This paper investigates the impact of Asian banks’ income structure on competitiveness, profitability and risk over the period 2005-2011. Exchange-listed commercial banks of eight Asian countries are included in the study sample. The cross-sectional regression results reveal that higher exposure of net non-interest income in Asian banks increases market risk and asset risk, but lowers insolvency risk, ROA and ROE. However, higher exposure of net fees and commissions reduces return volatility, market risk and asset risk, but increases insolvency risk, ROA and ROE. Further, the exposure of trading and derivatives and other securities tends to decrease the bank’s competitiveness. Keywords: Income Structure, Competitiveness, Profitability, Risk, Asian Bank JEL Classification: G2; G21 1 Introduction Since the 1997 Asian financial crisis, the Asian banking industry has been transforming from a traditional banking model to a universal banking model under the global context of financial deregulation. This leads to greater diversification of a bank’s income structure; i.e., banks not only focus on interest income, but also focus on non-interest income such as fees and commissions, and gains on derivatives and securities trading. However, the effect of the income structure diversification on bank performance and risk is still inconclusive. Some papers conclude that nontraditional banking activities tend to increase risk (DeYoung and Roland, 2001; Stiroh and Rumble, 2006; Baele, Jonghe and Vennet, 2007; Demirguc-Kunt and Huizinga, 2010), whereas the results of other papers indicate that risk is reduced by the diversification effect (Gallo, Apilado and Kolari, 1996; Rogers, and Sinkey, 1999; Geyfman, 2010). Similarly, the return is found to be enhanced by diversification in some studies (Gallo, Apilado and Kolari, 1996; DeYoung and Roland, 2001; Demirguc-Kunt and Huizinga, 2010; Liu and Wilson, 2010), whereas it is found to be reduced by other researchers (Stiroh, 2004; Mercieca, Schaeck and Wolfe, 2007; Berger, Hasan and Zhou, 2010). These non-consistent results motivate the author to study further. Previous studies focused mainly on the effect of diversification on performance and risk of banks in the U.S. and Europe. Since to date there is not much literature in this area using banking data from Asian countries, this paper contributes to the literature on income structure diversification effect on Asian banks’ return and risk. The second contribution of this paper is that one new variable “competitiveness”
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Pornchai Chunhachinda, Li Li
Thammasat University (Chunhachinda), University of the Thai Chamber of Commerce (Li),
Bangkok, Thailand
Income Structure, Competitiveness, Profitability and Risk:
Evidence from Asian Banks
Abstract:
This paper investigates the impact of Asian banks’ income structure on competitiveness, profitability
and risk over the period 2005-2011. Exchange-listed commercial banks of eight Asian countries are
included in the study sample. The cross-sectional regression results reveal that higher exposure of net
non-interest income in Asian banks increases market risk and asset risk, but lowers insolvency risk,
ROA and ROE. However, higher exposure of net fees and commissions reduces return volatility,
market risk and asset risk, but increases insolvency risk, ROA and ROE. Further, the exposure of
trading and derivatives and other securities tends to decrease the bank’s competitiveness.
Keywords: Income Structure, Competitiveness, Profitability, Risk, Asian Bank
JEL Classification: G2; G21
1 Introduction
Since the 1997 Asian financial crisis, the Asian banking industry has been transforming from a
traditional banking model to a universal banking model under the global context of financial
deregulation. This leads to greater diversification of a bank’s income structure; i.e., banks not only
focus on interest income, but also focus on non-interest income such as fees and commissions, and
gains on derivatives and securities trading. However, the effect of the income structure diversification
on bank performance and risk is still inconclusive. Some papers conclude that nontraditional banking
activities tend to increase risk (DeYoung and Roland, 2001; Stiroh and Rumble, 2006; Baele, Jonghe
and Vennet, 2007; Demirguc-Kunt and Huizinga, 2010), whereas the results of other papers indicate
that risk is reduced by the diversification effect (Gallo, Apilado and Kolari, 1996; Rogers, and Sinkey,
1999; Geyfman, 2010). Similarly, the return is found to be enhanced by diversification in some studies
(Gallo, Apilado and Kolari, 1996; DeYoung and Roland, 2001; Demirguc-Kunt and Huizinga, 2010;
Liu and Wilson, 2010), whereas it is found to be reduced by other researchers (Stiroh, 2004; Mercieca,
Schaeck and Wolfe, 2007; Berger, Hasan and Zhou, 2010). These non-consistent results motivate the
author to study further.
Previous studies focused mainly on the effect of diversification on performance and risk of banks in the
U.S. and Europe. Since to date there is not much literature in this area using banking data from Asian
countries, this paper contributes to the literature on income structure diversification effect on Asian
banks’ return and risk. The second contribution of this paper is that one new variable “competitiveness”
is added to test whether the Asian banks’ competitiveness perceived by investors will be positively or
negatively affected by the income structure diversification. The sample is exchange-listed commercial
banks of eight Asian countries, including Hong Kong, Indonesia, Malaysia, the Philippines, Singapore,
South Korea, Taiwan and Thailand (although Hong Kong is not strictly a country, it is included as such
in this paper), over the period 2005-2011. The diversified income structure is captured by three
variables: ratio of net non-interest income to net operating income, ratio of net fees and commissions to
net operating income, and ratio of net gains on trading and derivatives and other securities to net
operating income. Tobin’s Q ratio is the proxy for bank competitiveness; the profitability is measured
by five variables: return on assets, return on equity, risk-adjusted return on assets, risk-adjusted return
on equity and the ratio of net interest income to total assets; and the seven risk factors selected are:
standard deviation of stock’s annual return, beta, standard deviation of ROA and ROE, capital ratio
(equity to total assets), Z-score (ratio of the sum of average ROA and average capital ratio to standard
deviation of ROA), and the ratio of loan loss provision to net loans.
The remainder of this paper is organized as follows: Section 2 contains the literature review, Section 3
has details regarding the methodology and data, Section 4 includes a discussion on the empirical
results, and the conclusion is in Section 5.
2 Literature Review
Many papers have studied the income structure diversification effect on banks’ risk and returns. The
majority of these papers have chosen banks from the U.S. and Europe to study. The results, however,
are not consistent in all of the studies.
Even for banks in the same country, e.g. the U.S., the study results are not the same because of
different methodologies or different data sets. A degree of total leverage model was constructed by
DeYoung and Roland (2001) to test how shifts in product mix affected the earnings volatility of 472
U.S. commercial banks between 1988 and 1995. The OLS regression results implied that replacing
traditional lending activities with fee-based activities is associated with higher earnings volatility.
However, the study also found that the shift in product mix was accompanied by an increase in bank
profitability, which can partially compensate banks that have higher risk. Furthermore, the results
implied that banks of all sizes can reduce risk in terms of revenue volatility by increasing the size of the
bank, because larger banks can diversify more and gain more benefits of risk reduction.
Gallo, Apilado and Kolari (1996) employed the LISREL model to investigate the risk structure of U.S.
bank holding companies and the effect of mutual fund activities on bank risk and profitability over the
period 1987-1994. The results of LISREL model suggested that mutual fund activities reduce the
exposure of banks to systematic industry risk but have no significant impact on bank market risk.
Furthermore, mutual fund activities do not significantly reduce bank unsystematic risk but they do
enhance bank profitability.
Stiroh and Rumble (2006) studied the quarterly data of U.S. financial holding companies from 1997 to
2002 to investigate the diversification benefits of offering a range of financial services and shifting
toward activities that generate fees, trading income and other non-interest income. It was found that
diversification gains were more than offset by the costs of increased exposure to volatile non-interest
activities.
The diversification benefits in the U.S. banking industry were studied by Stiroh (2004), using the
aggregate banking industry quarterly data from 1984 to 2001 and the bank level annual data from 1978
to 2000. The study found that volatility of industry net operating revenue declined due to reduced
volatility of net interest income rather than the diversification benefit from non-interest income. At the
bank level, the cross-sectional regression results revealed that a greater reliance on non-interest income
reduces risk-adjusted profits and increases risk.
The effect of increased non-interest income on U.S. bank holding companies’ market measures of
return and risk was evaluated by Stiroh (2006) using the data from the period 1997-2004. No
relationship was found between non-interest income exposure and average returns of banks, but the
non-interest exposure was found to be statistically positively related to volatility of market returns and
the bank’s market beta. Therefore, it was concluded that the shift toward non-interest income has not
improved the risk-return outcomes of U.S. bank holding companies.
Rogers and Sinkey Jr. (1999) applied a random-effects model to analyze the nontraditional activities in
8,931 U.S. commercial banks over the period 1989-1993. The analysis showed that banks involved
more in nontraditional activities tend to be larger in size, have smaller net interest margin, have
relatively fewer core deposits, and have less risk (in terms of capital adequacy, liquidity risk, interest
rate risk and credit risk).
Analyzing a unique data set of U.S. domestic bank holding companies over the period 1990-1999,
Geyfman (2010) concluded that U.S. domestic bank holding companies that had expanded into
securities activities were more diversified (with higher returns and lower overall risk) and less likely to
go bankrupt (with higher Z-scores) relative to their stand-alone traditional commercial banking and
nontraditional banking subsidiaries.
Other than U.S. banks, European commercial and cooperative banks established in 14 European
countries were investigated by Lepetit et al. (2008) to test the relationship between bank risk and
product diversification over the period 1996-2002.
The study found that banks expanding into non-interest income activities displayed a higher degree of
risk and higher insolvency risk than banks which focused more on traditional lending activities. Cross-
sectional OLS regression results suggested that the positive relationship between risk and product
diversification is more robust for smaller banks and mainly driven by commission and fee-based
activities but not trading activities. The paper also concluded that engaging in trading activities may
decrease the risk for smaller banks to some extent.
Baele, Jonghe and Vennet (2007) explored the impact of bank diversification on competitive advantage
in terms of long-term performance/risk profile compared to their specialized competitors using the
panel data of 17 European banks from the period 1989-2004. The results indicated that a higher share
of non-interest income increases the bank’s franchise value and systematic risk, whereas the impact on
the bank’s idiosyncratic risk is non-linear and downward-sloping.
Mercieca, Schaeck and Wolfe (2007) investigated a sample of 755 small European banks for the period
1997-2003 and determined that there was no direct diversification benefit within and across business
lines. The shift into non-interest income activities negatively affected the small banks’ return (mean
ROA and ROE, risk-adjusted ROA and ROE) and positively affected the banks’ risk (standard
deviation of ROA and ROE, Z-score). The diversification indicators included non-interest income share
and Herfindahl Hirschmann Index (HHI) measures.
Banks in Israel over the period 1991-2001 were found to have gains from diversification and the risk-
adjusted performance was mostly consistent with optimal portfolio choice (Landskroner, Ruthenberg
and Zaken, 2005).
A cross-sectional OLS analysis of 198 Australian credit unions over the 34 quarters from 1993 (Q2) to
2001 (Q3) revealed that the increasing reliance on fee income generating activities is associated with
increased risk (Esho, Kofman and Sharpe, 2005).
Quarterly data of eight Canadian banks over the period 1988-2007 were analyzed to study the impact of
off-balance-sheet activities on banks’ returns (Calmes and Theoret, 2010). The study found that banks’
risk-return trade-off displayed a structural break around 1997. During the period 1988-1996, the share
of non-interest income negatively affected the banks’ returns and a risk premium emerged to price the
risk associated with non-interest income activities. But during the period 1997-2007, the share of non-
interest income did not significantly affect a bank’s return.
Some studies choose samples from multiple countries. Demirguc-Kunt and Huizinga (2010) studied an
international sample of 1,334 banks in 101 countries over the period 1995-2007 to examine the impact
of non-interest-generating activities on return and risk. The empirical results suggested that a higher
level of non-interest income share increases the bank risk, although the rate of return on assets also
increases.
Elsas, Hackethal and Holzhauser (2010) examined the effect of revenue diversification on bank value
using the panel data from nine countries (Australia, Canada, France, Germany, Italy, Spain,
Switzerland, UK, and USA) over the period 1996-2008. The findings indicated that diversification
increases bank profitability and hence market value. Furthermore, the study indicated that this positive
relationship between diversification and bank value also holds during a financial crisis.
Similar studies on banks in Asian countries have not been conducted as often as studies on banks in
western countries. Lin et al. (2012) studied a sample of 262 commercial banks in nine Asian countries,
including China, India, Indonesia, Japan, the Philippines, Singapore, South Korea, Taiwan and
Thailand over the period 1997-2005. The results implied that banks can reduce the sensitivity of net
interest margin due to idiosyncratic risk by diversifying their income sources.
The relationship between diversification and performance of Chinese banks during the 1996-2006
period has been investigated by Berger, Hasan and Zhou (2010). It was found that all four dimensions
of diversification were negatively associated with profits (ROA) and positively associated with costs
(ratio of total expenses to total assets).
The determinants of the profitability of 685 Japanese banks over the period 2000-2007 were
investigated by Liu and Wilson (2010). The results indicated that for Second Association Regional
banks and Shinkin banks, there is a positive relationship between diversification and ROA and ROE,
suggesting a diversification benefit. The higher share of non-interest income, however, leads to lower
banks’ net interest margins.
Yang et al. (2006) investigated the empirical relationship between the use of derivatives and bank risk
in Korea. The results showed that a bank’s derivative activities tend to reduce systematic risk and ex
ante earnings volatility.
Regarding the competitiveness of Asian banks, Chunhachinda and Jumreornvong (1999) used Tobin’s
Q ratio to measure and compare the competitiveness of Thai banks and finance companies during the
period 1990-1996. They concluded that finance companies were more competitive than banks since the
Q ratios of the banks are significantly lower than those of the finance companies. The study found that
the higher competitiveness of a bank depends on higher profitability, liquidity and leverage, and
smaller size of assets. Later, Chunhachinda and Li (2011) studied the competitiveness of banks,
measured again by Tobin’s Q, in eight major Asian countries over the period 2004 – 2010. Countries
studied were Hong Kong, Indonesia, Malaysia, the Philippines, Singapore, South Korea, Taiwan and
Thailand. For most countries, return on average assets, loan loss reserves/gross loans, and equity/total
assets were significantly correlated with the Q ratio.
3 Methodology and Data
3.1 Methodology
This paper studies the impact of income structure on competitiveness, profitability and risk of banks in
eight Asian countries, including Hong Kong, Indonesia, Malaysia, the Philippines, Singapore, South
Korea, Taiwan and Thailand over the period 2005-2011. This period was chosen because the data of
many banks are not available for years before 2005. Three major Asian countries -- China, India and
Japan -- are excluded because banks in these countries are very different from those in the countries
included in this study.
The bank income structure is measured by the three variables following Lepetit et al. (2008):
1. NNII_OI: the ratio of net non-interest income1 to net operating income where net operating income
is the sum of net interest income and net non-interest income
2. FEE_OI: the ratio of net fees and commissions to net operating income
3. TS_OI: the ratio of net gains on trading and derivatives and other securities to net operating income
Bank competitiveness is measured by Tobin’s Q ratio following Chunhachinda and Li (2011), which is
the ratio of the sum of market value of equity and book value of debt to the book value of total assets.
The result is the competitiveness of banks based on the perception of market investors. Banks with
higher Q ratios are more competitive than banks with lower Q ratios.
The five bank profitability variables chosen are the conventional measures in many studies such as
Stiroh (2004), Stiroh and Rumble (2006), Mercieca, Schaeck and Wolfe (2007), Calmes and Theoret
(2010), etc.:
1 The net non-interest income includes 4 components: net fees and commissions, net gains on trading and derivatives and
other securities, net insurance income, and others. Net insurance income and others are not included in this paper due to
missing or unavailable data. However, the results will not be affected much since net insurance income and others consist of
only small percentage of net non-interest income.
1. ROA -- return on assets, ratio of net income to average assets
2. ROE -- return on equity, ratio of net income to average equity
3. RAROA -- risk adjusted ROA, ratio of average ROA to standard deviation of ROA
4. RAROE -- risk adjusted ROE, ratio of average ROE to standard deviation of ROE
5. NII_TA -- ratio of net interest income to total assets
Risk measures are proxied by the following seven variables, as used by Lepetit et al. (2008), Stiroh and
Rumble (2006), Stiroh (2004), etc.:
1. Market risk -- beta (BETA) and standard deviation of stock’s annual return (SDSR); the
higher the BETA or SDSR, the higher the market risk
2. Standard accounting measures -- standard deviation of ROA and ROE (SDROA and
SDROE); the higher the SDROA or SDROE, the higher the return volatility
3. Insolvency risk -- capital ratio (equity to total assets: E_TA); Z-score (ratio of the sum of
average ROA and average capital ratio to standard deviation of ROA); the higher the Z-
score, the lower the insolvency risk
4. Asset risk -- ratio of loan loss provision to net loans (LLP_NL); the higher the ratio, the
higher the asset risk
Four control variables were selected to account for the specific characteristics of a particular bank:
1. Size effect -- LN_TA, natural logarithm of total assets
2. Financial leverage -- E_TA, ratio of equity to total assets
3. Growth opportunity -- GTA, the annual growth rate of total assets
4. Asset allocation -- L_TA, ratio of net loans to total assets
To study the impact of income structure on other factors, the following cross-sectional multiple
regression models will be utilized:
Competitiveness or Profitability or Risk = 1
(1)
1 2TS_OI
(2)
All variables except dummies are mean values of each bank over the period 2005-2011. The dependent
variables are the average Q (competitiveness) or the mean values for each profitability measure, or each
risk measure for each bank over the period 2005-2011. Control variables are the mean value of each
control variable for each bank over the period 2005-2011. Country dummies are equal to 1 if the bank
belongs to that country and 0 otherwise; this is to take into account any country differences. Thailand is
used as the base country.
3.2 Data
All annual balance sheet and income statement accounting data for the banks studied were compiled
from Bankscope for the period 2005-2011. To be consistent with other data, year-end stock prices over
the period 2004-2011 were also compiled from Bankscope to compute the stock’s annual return and
standard deviation of stock’s annual return which is one proxy for market risk. Similarly, only limited
information on beta is available from Bankscope, thus the 1 year beta is chosen to be another proxy of
market risk.
This paper studies only the commercial banks2 listed on each country’s stock exchange. There are total
of 99 banks meeting the study criteria during the period 2005-2011. However, banks with less than
three consecutive years of data are excluded. Consequently, the final sample consists of 72 banks from
the eight Asian countries as presented in Table 1. Although the total number of bank-year observations
is 469, the sample size or the number of observations used in the regression is 72 since all variables are
mean values of each bank over the period.
Table 1. Sample Banks from the Eight Asian Countries.
Country No. of Banks
(2005-2011)
No. of Bank-Year Observations
(2005-2011)
Hong Kong 7 49
Indonesia 27 169
Malaysia 2 14
Philippines 11 76
Singapore 2 14
South Korea 2 6
Taiwan 11 71
Thailand 10 70
Total 72 469
4 Empirical Results
4.1 Descriptive Results
The trend of mean income structure of Asian banks over time is shown in Table 2. It can be seen that
the mean share of net non-interest income in net operating income (NNII_OI) changes year by year
from 2005 to 2011, with the highest share of 86.3 percent in year 2008 and lowest share of 21.7 percent
in year 2010. The mean share of fees and commissions (FEE_OI) reaches the highest point of 16.8% in
2 Commercial banks are banks conducting traditional banking activities such as taking deposits and making loans, and
conducting non-traditional banking activities such as earning fees and commissions and gains on trading and derivatives and
other securities. Thus, financial groups, holding companies, finance companies, securities companies and Islamic banks,
etc. are excluded.
2010 and the lowest share is 4.8% in 2008. The mean share of net gains on trading and derivatives and
other securities (TS_OI) is smaller than the share of fees; it has the highest share of 8.0% in 2006, but it
changes to negative 1 percent in 2008 due to negative net gains on trading and derivatives and other
securities. For TS_OI, there are only 43 observations because the data is not available for 29 banks.
Table 2. Mean Income Structure of Banks over the Period 2005-2011.
2011 2010 2009 2008 2007 2006 2005 Average Observations