University of Huddersfield Repository Tan, Aaron Yong and Floros, Christos Risk, profitability and competition: evidence from the Chinese banking Original Citation Tan, Aaron Yong and Floros, Christos (2014) Risk, profitability and competition: evidence from the Chinese banking. Journal of Developing Areas, 48 (3). pp. 303319. ISSN 0022037X This version is available at http://eprints.hud.ac.uk/20996/ The University Repository is a digital collection of the research output of the University, available on Open Access. Copyright and Moral Rights for the items on this site are retained by the individual author and/or other copyright owners. Users may access full items free of charge; copies of full text items generally can be reproduced, displayed or performed and given to third parties in any format or medium for personal research or study, educational or notforprofit purposes without prior permission or charge, provided: • • •
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The Journal of Developing Areas Volume 48
No. 2
Spring 2014
RISK, PROFITABILITY AND COMPETITION: EVIDENCE FROM THE CHINESE BANKING INDUSTRY Yong Tan University of Huddersfield, United Kingdom Christos Floros Technological Educational Institute of Crete, Greece Hellenic Open University, Greece ABSTRACT This paper investigates the inter-temporal relationship between banking profitability, competition and risk of a sample of Chinese commercial banks by employing several profitability and risk indicators and using Seemingly Unrelated Regression (SUR) under a panel data framework over 2003-2009. The results support the Structure-Conduct-Performance (SCP) theory which states that there is a negative impact of competition on bank profitability. We also find that banks with higher profitability normally operate in a less competitive environment. The results have potentially important implications for the government and banking regulatory authorities to make relevant policies. JEL Classifications: G21, G32, C50, C23. Keywords: banking profitability, competition, risk, SUR, China Corresponding Author’s Email Address:
[email protected]
INTRODUCTION The financial system in China plays an important role in the development of the economy, while the Chinese banking sector is the most important component of the financial system. However, some of the problems are still serious such as: 1) there is a relatively large volume of non-performing loans in Chinese banking sector which indicates that the risk of Chinese banks is still high; and 2) the profitability of Chinese banks is still below the international standards (Garcia-Herrero et al., 2009). There are extensive literatures investigating the determinants of bank profitability (see Athanasoglou et al., 2008; Ramlall, 2009; Dietrich and Wanzenried, 2011). The bank profitability is significantly affected by the bank risk as accumulation of non-performing loans (high bank risk) normally reduces the bank profitability. Furthermore, in a more concentrated banking market where the competition is relatively lower, firms tend to collude with each other to obtain higher profit which is in accordance with the Structure-Conduct-Performance (SCP) theory. The profitability is supposed to have an impact on the risk-taking behaviour undertaken by the banks. The higher profitability of banks reflects the fact that they have a good system of risk management, credit checking and risk monitoring which is expected to reduce the bank risk. In addition, in a highly competitive market, in order not to be kicked out from the market, all banks tend to increase their profit margins through increasing the risk due to the fact that the banks think that higher risks come with higher returns. As argued by Fernandez de Guevara and Maudos (2007), banks with higher risk implies relatively lower margins which lead to lower market power (higher bank
competition), while the higher profitability of banks indicates that there is a relatively inelastic demand of banking services by consumers, the abnormal profit made by the banks induces the potential competitors enter into the banking market which precedes an increase in the competition. Chinese banking industry underwent the financial reform during 2003-2009 to increase the bank competition which is supposed to improve the bank performance, however, it is expected that the reform will negatively affect the bank stability, understanding the relationship between competition, profitability and risk is highly important for the bank managers and helpful for the government and banking regulatory authorities to make relevant policies. There are several studies investigating the impact of risk on bank profitability (see Athanasoglou et al., 2008, Goddard et al., 2004b, among others), while the impact of competition on bank risk is widely examined by Liu et al., 2012 and Liu et al., 2013; further, the relationship between bank competition and profitability is assessed by Llewellyn (2005), Bikker and Bos (2005), among others. Our paper contributes to the existing literature by evaluating the inter-temporal relationship among risk, profitability and competition in Chinese banking sector. In other words, we estimate the above relationship using the Lerner index as the key competition indicator, different risk and profitability indicators under the Seemingly Unrelated Regression (SUR) framework. The paper is structured as follows: following this introduction, Section 2 describes the reforms and evolution in Chinese banking industry. Section 3 reviews the existing literature on the inter-temporal relationship between risk, profitability and competition. Section 4 outlines the empirical methodology. Section 5 presents the main results and section 6 summarizes and concludes. REFORMS AND EVOLUTION IN CHINESE BANKING INDUSTRY Until 1978, Chinese financial system followed the mono-bank model and was operated based on socialist principles. The People’s Bank of China (PBOC) played the dual role as central and commercial bank. A two tiered banking system, consisting of the PBOC and State-Owned Commercial Banks was established during the first stage of financial reform over the period 1979-1992. PBOC was free to serve as central bank. By the end of 1992, nine Joint-Stock Commercial Banks had been established. Furthermore, various financial institutions were established by the end of 1992 which included 12 insurance companies, 387 trust and investment companies, 87 securities companies, 29 finance companies, 11 leasing companies, 59000 rural credit cooperatives, and 3900 urban credit cooperatives (Shang, 2000). Although various types of financial institutions co-existed in China, the bank competition in China was very limited until the mid-1990s especially among the State-Owned Commercial Banks due to the fact that they served as the policy lending arms for the government and lack of incentive to compete (Berger et al., 2009). Because State-Owned Commercial Banks made most of their loans to stateowned enterprises under the direction of the government, their asset quality deteriorated significantly during the 1990s. Three policy banks were established in 1994 to take over the policy-lending activities from the State-Owned Commercial Banks in order to ameliorate this problem. In addition, in 1988, The Ministry of Finance issued 270 billion RMB (USD $32.6 billion) of 30-year government special bonds to recapitalize the big four banks1. At the same year, four asset management companies were established in order to deal with state-owned banks’ bad loans.
The profitability of Chinese banks is low compared to international standard (Garcia-Herrero et al., 2009). For example, the Return on Equity (ROE) and Return on Assets (ROA) of the Chinese banking system in 2003 was 3.05% and 0.14% respectively, while the Eastern Europe Banks had 13.57% ROE and 1.43% ROA in the same year (Garcia-Herrero et al., 2006). In order to achieve better monitoring of the banking industry, the Chinese Banking Regulatory Commission (CBRC) was created by the government in 2003. At the same year, the pilot state-owned bank-overhaul program was initiated by the State-Council to grant US $45 billion to BOC and CCB to increase their capitals rather than writing off the bad loans. The government also implemented new systems to monitor the asset quality internally and externally. All the Chinese banks are encouraged to be listed on stock exchanges for additional external monitoring and creating a more competitive environment in the banking sector. The first bank listed in stock exchange is the Bank of Communication which raised more than US$ 2 billion through the IPO in Hongkong on June 2005. While three of the State-Owned Commercial Banks (CCB, BOC and ICBC) issued their IPOs on October 2005, June 2006 and October 2006, respectively, while the 46.64 billion RMB (about US$ 5.9 billion) raised by ICBC in the Shanghai Stock Exchange is the world’s biggest IPO so far. All the State-Owned Commercial Banks finish their IPOs following the listing of ABC on July 2010 in Shanghai and Hongkong Stock Exchanges. By the end of year 2010, there are 16 banks listed on stock exchanges in either Hongkong or Shanghai. LITERATURE REVIEW The impact of risk on profitability has been extensively investigated by several studies. Changes in credit risk may reflect changes in the heath of bank’s portfolio (Cooper et al., 2003), which affects the performance of the US bank holding companies over the period 1986-1999. Duca and Mclaughlin (1990), among others, conclude that variations in bank profitability are largely attributable to variations in credit risk, since inverse exposure to credit risk is normally associated with decrease for profitability. Miller and Noulas (1997) suggest that financial institutions being more exposed to high risk loans increase the accumulation of unpaid loans and decrease the profitability in the US banking industry over the period 1984-1990. Fadzlan and Royfaized (2008) find that there is a negative relationship between credit risk and bank profitability in Philippine banking industry over the period 1990-2005; this is in line with Liu and Wilson (2009) for Japanese banks over the period 2000-2007. However, a positive relationship is reported by Fadzlan (2009) for China over the period 2000-2007. There are mainly two approaches used by the empirical literatures to investigate the relationship between competition and performance in the banking sector. One is structural approach and the other one is non-structural approach. There are two hypotheses included in the structural approach which are Structure-ConductPerformance (SCP) hypothesis and the Efficient-Structure (ES) hypothesis. These hypotheses investigate whether the superior performance in the banking sector is obtained through the collusive behaviour among the large banks in the concentrated market, and whether it is the higher efficiency that leads to better bank performance. On the other hand, the non-structural approaches, which derived from the development in the New Empirical Industrial Organization (NEIO)2 literature, stress the analysis of banks’ competitive conducts in the absence of structural measures.
The SCP hypothesis is partly supported within the context of the NEIO literature by Bikker and Bos (2005). It argues that collusion among banks has the ability to obtain higher profit in a more concentrated market through charging higher loan rates and offering lower deposit rate. The more concentrated bank market leads to smaller degree of competition, while the smaller number of firms makes it more probable for them to collude together. In other words, banks in a more concentrated market achieve higher profit. On the other hand, the Efficient Structure hypothesis states that low cost of production by relatively efficient firms enable them to compete more aggressively, capture a bigger market share and earn high profits (Fu and Heffernan, 2009). So the higher profit achieved by banks attributes to the lower cost through either superior management or production process rather than the concentrated market. Because the efficient banks have the ability to obtain higher market share, one way to distinguish between the two hypotheses is to include both the market share and concentration in the profitability equation, if the concentration is insignificant or the market share is positively related to profitability, then it is in line with the Efficient Structure hypothesis. There are mainly two different views with regards to the relationship between competition and risk; these are: competition-fragility and competitionstability. The former argues that banks have the ability to withstand shocks and decrease the risk-taking behaviour due to the fact that higher profitability can be earned through monopoly rents in a less competitive market (see Allen and Gale, 2000, 2004; Carletti, 2008; Boyd and De Nicole, 2005). The competition-stability view suggests that, in a less competitive banking market, banks normally charge higher interest rate which will increase the probability of default on the loan repayment. By allowing for imperfect correlation across individual firms’ default probabilities, Martinez-Miera and Repullo (2010) indicate that there is a U-shape relationship between competition and risk; therefore, as the number of banks increases, the probability of bank default first declines but increases beyond a certain point. Overall, the issue of whether competition precedes bank stability or fragility has still been unsolved. EMPIRICAL METHODOLOGY We rely on Zellner’s (1962) Seemingly Unrelated Regression (SUR) method3 to investigate the relationship between bank risk, profitability and competition as efficiency in estimation can be gained by combining information on different equations and restrictions can be imposed and/or tested that involve parameters in 4 different equations . In order to disentangle the inter-temporal relationship between bank risk, profitability and competition, we estimate the following equations: RISK it = β 0 + β1 PROFITit + β 2 LERNERit + β3 Bankit + β 4 INDUSTRYit + β5 MACROit + ε it
PROFITit = δ 0 + δ1 RISK it + δ 2 LERNERit + δ 3 BANK it + δ 4 INDUSTRYit + δ 5 MACROit + ε it
LERNERit = γ 0 + γ 1 RISK it + γ 2 PROFITit + γ 3 BANK it + γ 4 INDUSTRYit + γ 5 MACROit + ε it
(1) (2) (3)
Where the i subscript denotes the cross-sectional dimension across banks, and t denotes the time dimension. RISK is the variable accounting for bank’s risk, PROFIT is the profitability indicator represented by the ROA . LERNER is the competition indicator5. Bank , INDUSTRY , and MACRO are bank-specific,
industry-specific and macroeconomic factors influencing the risk-profitabilitycompetition relationship and ε it is the random error term. Equation (1) tests whether competition and profitability temporarily precede variations in bank risk. Equation (2) assesses if risk and competition temporarily explain variations in bank profitability, while Equation (3) examines whether level of bank risk and profitability reflects the changes in bank competition. Table 1 gives full definition of all variables considered in this study. TABLE 1. DEFINITION OF VARIABLES CONSIDERED IN THIS STUDY Variables Risk
Acronyms LLPTP
Definition Loan-loss provision as a fraction to total loans
Volatility of ROA
Standard deviation of ROA
Volatility of ROE
Standard deviation of ROE
Z-scores
Ratio between a bank’s return on assets plus equity capital /total assets and the standard deviation of the return on assets. Ratio between net income and total assets Ratio between net income over equity Lerner Index
Profitability
Return on Assets Return on Equity
Competition Bank specific variables Size Loans to total assets Tax to pre-tax profit Off-balance activity
Lerner
Labour
LP
Industry specific variables Concentration
C(3)
Banking sector development
BSD
Stock market development
SMD
Macroeconomics Inflation GDP growth
IR GDPG
SIZE LIQUIDITY TAXATION OBSOTA
Logarithm of total assets Ratio of loan to total assets Ratio of tax to pre-tax profit Ratio of off-balance-sheet items to total assets Ratio of gross total revenue to number of employees The ratio of large three banks in terms of total assets to the total assets of the banking industry The ratio of banking industry assets over GDP Ratio of stock market capitalization over GDP Annual inflation rate Annual GDP growth rate
We measure the individual bank risk by using the ratio between loan-loss provision and total loans. Higher level of loan loss provision signals higher bank risk. A limitation to measure risk calculating from accounting data, as suggested by Rime (2001) and Shrieves and Dahl (1992), is that providing the portfolio quality can be
accurately reflected by these measures; managers are likely to have some time discretion which is exercised in a way to minimize cost. They also argue that this measurement is quite problematic for the banks which do not have public trade securities. In order to check the robustness of the results, we use three alternative measures of bank’s risk position which are (i) volatility of ROA, (ii) volatility of ROE and, and (iii) Z-score. The volatility of ROA and ROE are calculated for each bank over the examined period (2003-2009), while the Z-score is obtained as the ratio between a bank’s Return on Assets plus equity capital /total assets and the standard deviation of the Return on Assets. Furthermore, we use an alternative profitability indicator to confirm our empirical results which is Return on Equity (ROE). We also include comprehensive bank-specific, industry-specific and macroeconomic variables which are supposed to influence the risk-profitabilitycompetition relationship in the estimation; their expected effects are described in Table 2. TABLE 2. EXPECTATIONS ON THE IMPACTS OF BANK-SPECIFIC, INDUSTRY-SPECIFIC AND MACROECONOMIC VARIABLES ON BANK RISK, COMPETITION AND PROFITABILITY Independent/dependent risk Profitability variables Bank-specific Size + Liquidity Taxation OBSOTA LP + Industry-specific C(3) + BSD + SMD + Macroeconomics Inflation +/GDPG + Notes: -, + represents negative/positive impact, respectively.
Competition
+ + + + -
DATA AND RESULTS Our banking data is composed of annual figures from 101 Chinese banks over the period 2003-2009. We examine this period for several reasons. First, full Chinese banking data is not available for recent years. Second, we choose 2003 as the start point because of the establishment of Chinese Banking Regulatory Commission (CBRC) in 2003, which aims to enhance the risk monitoring and management of Chinese banking industry. Further, 2009 is chosen as the ending year for this research due to the fact that 2009 is marked as the point for the 60-year establishment of Chinese government; in addition, the development of Chinese financial system started in 2009. Note that 2003-2009 is a key period for the Chinese banking system due to internationalization. The banks used in this study are five State-Owned Commercial Banks, twelve Joint Stock Commercial Banks and eighty four City Commercial Banks. Since not all banks have available information for all years, we opt for an unbalanced panel not to lose degrees of freedom. The bank specific information is mainly obtained from the Bankscope database maintained by
Fitch/IBCA/Bureau Van Dijk, which is considered as the most comprehensive database for research in banking. The industry specific and macroeconomic variables are retrieved from the China Banking Regulatory Commission (CBRC) and the World Bank database. Table 2 presents the summary statistics of the variables that we use in our specification. Comparing the Mean values of ROA, we find that the profitability of State-Owned Commercial Banks and City Commercial Banks is higher than Joint-Stock Commercial Banks; same result can be obtained when comparing the alternative profitability indicator (ROE). This is different from the finding of Wu and Chen (2010) who argue that the Joint-Stock Commercial Banks have higher efficiency than the State-Owned Commercial Banks over 1996-2002. This is mainly because different period is examined. In addition, the credit risk of City Commercial Banks is the highest, while the State-Owned Commercial Banks have the lowest credit risk. The Mean values suggest that the staffs working in the Joint-Stock Commercial Banks is more productive than staffs in City Commercial Banks, while those in State-Owned Commercial Banks are least productive. The degree of correlation between the explanatory variables used in this study is tested through the correlation matrix. The results show that, in general, the correlation between the independent variables is not strong suggesting that 6 multicollinearity problems are not severe or nonexistent . Estimates from the simultaneous estimation using ROA as the profitability indicator are reported in Table 3. When using the loan-loss provision as a proportion of total loans as the dependent variable, we find that bank profitability has a significant and negative impact on bank risk suggesting that banks with higher profitability tend to have lower volume of loan loss provision. The explanation of this result is based on the fact that less efficient managers, who are able to generate only a lower amount of profit per unit of capital invested, would also be responsible for a deterioration in credit quality. The significant and positive relationship between Lerner index and bank risk indicates that lower competition (higher market power) induces the bank to take higher risk. This result supports the competition-stability assumption. In terms of the bank-specific variables, the significant and negative impact of bank size on bank risk suggests that bigger banks tend to have lower volume of loan loss provision. Hughes et al. (2001) argue that there are potential diversification benefits associated with bank size. The taxation and off-balance-sheet activity have negative impacts on loan-loss provision which underlines that banks with lower taxation and off-balance sheet activity normally have higher volume of loan loss provision. Tax is paid from the profit made by the banks, lower taxation reflects the fact the amount of profit made by the banks is smaller, as discussed earlier, the loan-deposit business is the main service provided by the banks and it is the main resource of their income, the lower profit achieved by the banks can be attributed to the loss on the traditional loan-deposit services, in other words, there is a larger volume of loan-loss provision. Lower volume of off-balance sheet activity indicates that banks use most of their funds engage in providing loans to different firms or companies, the lack of risk monitoring and management leads to accumulation of bad loans. Furthermore, we find in our study that labour productivity is positively and significantly related to bank risk. This finding can be explained by the fact that in order to stimulate the staffs to increase their work efforts, efficiency and productivity, the Chinese banks have the policy that the staffs’ income is linked with the number of loan business they provide. In other words, the more loan transactions they make, the more salaries they will earn. In this case, the banking staffs will focus on the number of transactions they have rather than emphasizing on the quality of the transactions which leads to higher bank risk.
In terms of the industry-specific variables, the empirical results show that banks operating in a market with higher concentration, higher development of banking and stock market typically have higher volume of loan loss provision; the results in terms of the impacts of the development of banking sector and stock market are in line with our expectation. However, the finding of concentration on risk is in direct contrast with our expectation. The possible reason is that the concentrated banking market in China indicates that the market share is occupied by the StateOwned Commercial Banks. The support from government to the State-Owned Commercial Banks reduces the incentive for the mangers to increase their efforts to engage in prudential lending which leads to accumulation of bad loans. Banks operating in higher inflation and GDP growth environment have lower level of loanloss provision as showed by the negative and significant signs of these two variables; this is in line with our expectation. The risk is also cross-checked by three alternative indicators (Volatility of ROA, Volatility of ROE and Z-score). ROA is significantly and positively correlated with volatility of ROA. This result may be explained by the fact that the higher profit obtained by the bank induces the manager to be careless in the future transactions which leads to more volatile of the return. Further, the signs of coefficients of all the industry-specific variables turn to be negative when volatility of ROA is used as the risk measure. This finding indicates that although the volume of loan loss provision is high, the gain from the relative inelastic demand from consumers can offset the loan losses made by the banks which results in relative more stable returns. The results show that the off-balance-sheet activity in Chinese banking industry has a significant and positive influence on the volatility of ROA. These results confirm that the Chinese banks still lack of the knowledge, experience and staffs to engage in the non-traditional business which leads to more volatile of the return. Further, liquidity is significantly and inversely correlated with volatility of ROA suggesting that loan growth is inextricably linked to loan loss provision levels, which reflects that the degree of risk on different projects or degree of risk from different firms in China is well-diversified. Finally, the results from the ownership dummy variables show that both the State-Owned and Joint-Stock Commercial Banks have higher volume of loan loss provision7. Moreover, the empirical results show that LLPTL has a negative impact on ROA in Chinese banking industry indicating that banks with higher level of loan loss provision normally have lower profitability ratios. This result is in line with the findings of Fadzlan and Royfaized (2008). In addition, we find that Lerner index has a significant and positive impact on ROA. This result is in line with the SCP theory as explained below.
TABLE 3. DESCRIPTIVE STATISTICS OF THE VARIABLES WS
SO
JS
C
WS
SO
0.007 0.134
0.006 0.67
0.004 0.08
4.28 0.01 0.38 52.64 0.007
0.95 0.01 0.37 9.34 0.004
0.26 0.003 0.194 6.08 0.003
0.18 0.004 0.062 54.3 0.254
0.111 0.004 0.504 245.6 0.13
0.058 0.001 0.031 37.77 0.14
JS
C
WS
SO
0.007 0.089
-0.04 -14.5
0.002 -0.06
0.48 0.0042 0.37 6.22 0.037
0.66 0.007 0.39 9.97 0.004
5.97 0.0021 0.149 43 0.0013
4.01 0.0004 -0.39 42 0.003
0.089 0.004 1.36 573.96 0.089
0.12 0.004 0.04 65.4 0.12
0.71 -0.002 -4.56 17.97 3.00e06 0.0001 0 0 -5184 0.003
0.055 0.002 0.016 -14.67 0.05
0.039 0.001 0.022 -5184 0.004
Min -0.04 -14.5
C
WS
SO
-0.005 -0.14
0.089 0.58
Max 0.0125 0.01 0.251 0.3
0.089 0.58
0.71 -0.002 -4.56 17.97 3.00e06 0.001 0 0 -25.86 0.003
7.07 0.042 3.18 83.25 0.019
7.07 0.013 0.93 64 0.01
6.32 0.03 2.84 68.4 0.02
5.73 0.042 3.18 83.25 0.017
0.67 0.029 5.12 475 0.92
0.25 0.004 0.1 119.77 0.92
0.47 0.02 5.12 273 0.32
0.67 0.029 0.19 475 0.55
variables ROA ROE
0.007 0.099
Size Risk Taxation Liquidity Labour
4.67 0.01 0.41 53.4 0.01
Mean 0.007 0.004 0.12 0.075 6.65 5.53 0.007 0.007 0.42 0.5 52.02 57.24 0.006 0.012
OBS VOA VOE Z-scores Lerner index C(3)
0.2 0.003 0.112 44.17 0.23
0.15 0.003 0.063 53.56 0.22
14.54
1.95
10.19
16.29
BSD
51.98
15.49
16.86
63
SMD
77
49.74
31.9
184.1
inflation
2.5
2.17
-0.77
5.86
0.26 0.004 0.452 -3.89 0.12
S.D 0.006 1.65
JS
GDP 11 1.72 9.1 growth Note: WS represents whole sample, SO, JS, C indicate state-owned, joint-stock and city commercial bank, respectively.
14.2
JS
C
In terms of the bank-specific variables, taxation is found to be negatively related to profitability of Chinese banks, indicating a negative relationship between taxation and bank profitability. One could argue that, the more taxes paid by the banks, the higher cost incurred by the banks; this decreases profitability. The result is supported by Bashir (2003) for Islamic banks from Middle East and Tan and Floros (2012a) for Chinese banks. Labour productivity is found to have a positive and significant relationship with Chinese bank profitability, which is in accordance with Athanasoglou et al. (2008). Rather than profitability, the profit efficiency of small US commercial banks over the period 1990-1996 is examined by Akhigbe and McNulty (2003). The results indicate that small banks are more profit efficient than large banks. This finding is supported by Han et al. (2012) for Korean saving banks for the period from 2002 to 2008, and Berger and Mester (1997) for 6000 US commercial banks over the period 1990-1995. However, our study does not provide any robust relationship between bank size and profitability. Turning to the industry specific factors, the concentration is significant at 1% level and the sign of the coefficient is positive indicating that there is a positive relationship between concentration and bank profitability (ROA). This result is supported by Demirguc-Kunt and Huizinga (1999) and Hassan and Bashir (2003). However, this result is conflicted with Garcia-Herrero et al. (2009) for Chinese banks who find that there is a negative relationship between bank profitability and concentration. This is mainly because different period is examined and different methods are used in our study. Further, we find that there is a positive and significant relationship between banking sector development and bank profitability (ROA); this result is not in line with previous findings (e.g. Demirguc-Kunt and Huizinga, 1999). However, this is consistent with the finding of Tan and Floros (2012b) for Chinese banks. A large proportion of bank assets in GDP indicates that there is a high demand of bank services. According to the circumstance of banking industry in China, the establishment of a new bank involves a very complicated procedure, and the requirement made by the government to open a new bank is very strict. This makes a potential competitor difficult to enter the market, because the demand is increasing which makes the profitability of existing banks increase. The sign of stock market development is positive and this variable is significant at 10% level indicating that there is a positive relationship between stock market development and bank profitability (ROA). This finding confirms the empirical results of Ben Naceur (2003) for Tunisian banks who suggests that as stock market enlarges, more information become available. This leads to an increase number of customers to banks by making easier the process of identification and monitoring of borrowers. Consequently, this will contribute to a higher profitability. The positive relationship between stock market development and bank profitability shows that there are complementaries between stock market and banking development in China (this is in line with the theory). The GDP growth is found to be significantly and negatively related to bank profitability in China. This result is consistent with Liu and Wilson (2009) for the Japanese banking industry. This result partially supports the view that high economic growth improves business environment and lowers bank entry barriers. The consequently increased competition dampens bank’s profitability. In terms of the determinants of the Lerner index, we find that profitability has a significant and positive relationship with market power suggesting that banks with higher profitability ratio normally have higher market power (i.e. lower level of competition). Higher profitability is obtained through larger market share which
dampens the competition. Taxation is found to be significantly and positively correlated with Lerner index suggest that higher taxation reduces the competition in Chinese banking industry, which is in line with our expectation. Labour productivity has significant and negative impact on Lerner index indicating that higher labour productivity increases the degree of competition in Chinese banking industry. We further report that concentration, banking sector development and stock market development are significantly and negatively correlated with market power which underlines that higher concentrated banking market, higher developed banking and stock market precede to competition improvement in Chinese banking industry. The positive impact of concentration on competition can be explained by the fact that large amount of banking assets is occupied by the State-Owned Commercial Banks, this situation will decrease the competition between State-Owned Commercial Banks and other ownerships of banks (Joint-Stock and City Commercial Banks), lower proportion of assets held by Joint-Stock and City Commercial Banks will increase the competition among them. Our previous result is cross-checked by using ROE as the profitability indicator; the findings are reported in Table 4. We support the findings by Ariff and Can (2008) about profit efficiency of 28 Chinese banks over the period 1995-2004; they suggest that low level of asset quality normally leads to a decline of profit efficiency. We find qualitatively similar results to ROA, i.e. LLPTL has a negative impact on ROE, while ROE is significantly and negatively correlated with LLPTL. We further report that bank size, taxation and off-balance-sheet activity negatively affect the risk (LLPTL) of Chinese banks. Off-balance-sheet activity has a positive impact on volatility of ROA, while negative impact is exerted on the Z-score. Liquidity is found to have a negative impact on volatility of ROA. The finding confirms that both the State-Owned and Joint-Stock Commercial Banks have higher volume of loan loss provision. In terms of the determinants of ROE, the findings show that taxation has a significant and negative impact on ROE, while there is a positive impact of labour productivity on ROE. Further, labour productivity is found to be significantly and positively related to ROE in Chinese banking industry. We report that the higher development of banking sector in China increases the degree of competition (as reported in Table 5).
TABLE 4. EMPIRICAL RESULTS (ROA AS THE PROFITABILITY INDICATOR)
ROA risk Lerner Size liquid tax labour OBS C(3) BSD SMD INF GDPG SO JS
risk=z-score z-score ROA 3615 (1.29) 3.38e-06 (1.29) 29.57 0.01*** (0.60) (8.66) -16.68 -0.01* (-1.00) (-1.77) 0.1 0.00004 (0.11) (1.33) -37.2 -0.01*** (-0.73) (-8.60) -758 0.33*** (-0.30) (4.60) -142** 0.0003 (-2.02) (0.15) -2.35 0.001** (-0.22) (2.33) 0.51 0.001*** (0.93) (4.02) -0.07 0.001*** (-0.21) (3.68) 2.34 -0.0002 (0.34) (-1.16) 7.49 -0.01*** (0.65) (-2.87) 35.87 0.003** (0.90) (2.41) 58.1** -0.001 (2.34) (-0.81)
Lerner 37.32*** (1.29) 0.0001 (0.60)
0.028 (0.97) -0.002 (-0.97) 0.39*** (4.53) -9.09** (-2.07) 0.05 (0.44) -0.04** (-2.35) -0.004*** (-4.65) -0.002*** (-3.22) 0.02 (1.28) 0.06*** (2.80) -0.12* (-1.67) -0.07 (-1.57)
risk=LLPTL LLPTL -2.35*** (-11.32)
0.03*** (6.36) -0.01*** (-5.72) 0.0001 (1.63) -0.02*** (-4.98) 0.75*** (3.69) -0.01* (-1.82) 0.003*** (3.92) 0.0001** (2.09) 0.001*** (4.32) -0.001* (-1.93) -0.004*** (-4.65) 0.016*** (5.03) 0.004** (1.98)
ROA
-0.26*** (-11.32) 0.012*** (10.30) -0.002*** (-5.14) 0.0001* (1.88) -0.01*** (-8.62) 0.32*** (4.99) -0.003 (-1.50) 0.001*** (4.17) 0.001*** (3.70) 0.001*** (5.20) -0.001** (-2.06) -0.002*** (-4.98) 0.005*** (5.03) 0.001 (1.49)
Lerner 46.72*** (10.30) 10.91*** (6.36)
0.096*** (3.14) -0.002 (-1.41) 0.43*** (5.07) -12.11*** (-2.77) 0.15 (1.27) -0.07*** (-3.61) -0.004*** (-4.38) -0.002*** (-4.36) 0.02* (1.92) -0.09*** (4.30) —0.24*** (-3.39) -0.11** (-2.51)
risk=VOA VOA 0.99*** (4.95)
-0.003 (-0.87) -0.002 (-1.30) -0.001*** (-3.40) 0.01*** (2.85) 0.01 (0.08) 0.02*** (3.58) -0.001* (-1.80) -0.0001* (-1.73) -0.001*** (-3.03) 0.0004 (0.90) 0.002** (2.34) 0.001 (0.19) 0.001 (0.44)
ROA
0.17*** (4.95) 0.01*** (8.4) -0.001 (-1.07) 0.0001** (2.49) -0.01*** (-9.08) 0.27*** (3.82) -0.003 (-1.47) 0.001*** (2.78) 0.001*** (4.43) 0.001*** (4.45) -0.0003 (-1.37) -0.001*** (-3.42) 0.002** (2.09) -0.0004 (-0.56)
Lerner 37.2*** (8.4) -1.8 (-0.87)
0.021 (0.73) -0.002 (-1.12) 0.38*** (4.39) -8.27* (-1.87) 0.08 (0.59) -0.05** (-2.42) -0.004*** (-4.64) -0.002*** (-3.28) 0.016 (1.34) 0.06*** (2.91) -0.106 (-1.52) -0.07 (-1.54)
risk=VOE VOE 17.91 (0.53)
-1.05* (-1.79) 0.09 (0.43) -0.01 (-1.22) 0.29 (0.47) -12.8 (-0.42) -0.25 (-0.30) 0.05 (0.37) -0.002 (-0.30) 0.003 (0.89) -0.024 (-0.30) -0.096 (-0.70) -0.23 (-0.48) 0.43 (1.46)
ROA
0.0001 (0.53) 0.01*** (8.78) -0.001* (-1.93) 0.0001 (1.41) -0.01*** (-8.92) 0.33*** (-8.92) -0.0001 (-0.06) 0.001** (2.29) 0.001*** (4.17) 0.001*** (3.61) -0.0002 (-1.11) -0.001*** (-2.76) 0.003 (2.56) -0.001 (-0.66)
Lerner 37.58*** (8.78) -0.02* (-1.79)
0.03 (0.98) -0.002 (-1.13) 0.39*** (4.54) -9.37** (-2.14) 0.04 (0.28) -0.04** (-2.26) -0.004*** (-4.59) -0.002*** (-3.02) 0.015 (1.23) 0.05*** (2.67) -0.12* (-1.69) 0.051 (-1.19)
TABLE 5. EMPIRICAL RESULTS (ROE AS THE PROFITABILITY INDICATOR
ROE risk Lerner Size liquid tax labour OBS C(3) BSD SMD INF GDPG SO JS
risk=z-score z-score ROE 554*** (6.26) 0.001*** (6.26) 39.84 0.05 (0.90) (1.28) -7.21 -0.014 (-0.45) (-0.96) 0.643 -0.001 (0.71) (-1.01) 60.25 -0.211*** (1.35) (-5.94) -2627 (-1.14) -122.9* (-1.82) 1.55 (0.15) 0.34 (0.65) 0.14 (0.46) 0.58 (0.09) -1.62 (-0.15) 26.8 (0.71) 34.03 (1.42)
5.17** (2.54) 0.03 (0.45) -0.002 (-0.16) 0.001 (1.06) -0.0001 (-0.55) 0.001 (0.11) 0.007 (0.75) 0.015 (0.43) 0.019 (0.85)
Lerner 0.24 (1.28) 0.0002 (0.90)
risk=LLPTL LLPTL -0.05*** (-6.70)
ROE
-0.007 (-0.24) -0.0001 (-0.04) 0.027 (0.03)
0.007 (1.61) -0.007*** (-4.55) -4.59e-06 (-0.06) -0.007* (-1.80)
-5.27*** (-6.70) 0.09** (2.28) -0.05*** (-3.45) -0.001 (-0.66) -0.22*** (-6.08)
4.21 (0.94) 0.09 (0.68) -0.03 (-1.32) -0.003*** (-3.25) -0.001 (-1.33) 0.01 (0.84) 0.031 (1.45) -0.014 (-0.20) -0.155*** (-3.40)
0.26 (1.21) -0.01* (-1.93) 0.002* (1.79) -8.04e-06 (-0.17) 0.0001 (0.97) -0.001 (-0.82) -0.002* (-1.68) 0.011*** (3.19) 0.008*** (3.58)
5.13** (2.50) -0.09 (-1.47) 0.008 (0.85) 0.001 (1.19) 0.0001 (0.22) -0.002 (-0.27) -0.002 (-0.24) 0.08** (2.42) 0.074*** (3.34)
Lerner 0.425** (2.28) 2.98 (1.61)
risk=VOA VOA -0.008 (-1.15)
ROE
0.01 (0.30) 0.00001 (0.01) 0.044 (0.50)
0.007** (2.17) -0.003** (-2.21) -0.0002*** (-2.87) -0.002 (-0.70)
-1.21 (-1.15) 0.08** (1,98) -0.03* (-1.73) -0.001 (-1.09) -0.3*** (-6.78)
3.54 (0.78) 0.111 (0.83) -0.03 (-1.54) -0.003*** (-3.10) -0.001 (-1.46) 0.012 (0.96) 0.036* (1.69) -0.041 (-0.54) -0.172*** (-3.65)
0.39** (2.20) 0.02*** (3.42) -0.001 (-0.90) 1.10e-08 (0.00) -0.0001* (-1.66) 0.0002 (0.48) 0.001 (1.33) 0.004 (1.32) 0.001 (0.27)
5.84*** (2.72) -0.01 (-0.21) -0.002 (-0.25) 0.001* (1.67) -0.0002 (-0.61) 0.002 (0.28) 0.011 (1.05) 0.04 (1.14) 0.04* (1.93)
Lerner 0.35** (1.98) 4.69** (2.17)
risk=VOE VOE 1.63 (1.44)
ROE
0.005 (0.14) 0.001 (0.53) 0.05 (0.53)
-1.13** (-2.07) 0.103 (0.52) -0.012 (-1.06) 0.49 (0.88)
0.009 (1.44) 0.08** (2.01) -0.02 (-1.60) -0.001 (-0.70) -0.3*** (-6.80)
1.85 (0.41) -0.01 (-0.09) -0.02 (-1.10) -0.003*** (-3.12) -0.001 (-0.93) 0.009 (0.74) 0.024 (1.14) -0.028 (-0.38) -0.15*** (-3.28)
-14.82 (-0.52) -0.19 (-0.22) 0.06 (0.47) -0.003 (-0.40) 0.004 (1.12) -0.03 (-0.36) -0.12 (-0.91) -0.24 (-0.50) 0.33 (1.12)
5.48*** (2.60) -0.034 (-0.53) -0.002 (-0.22) 0.001* (1.71) -0.0002 (-0.59) 0.002 (0.27) 0.011 (1.03) 0.04 (1.07) 0.04* (1.74)
Lerner 0.36** (2.01) -0.03** (-2.07)
-0.005 (-0.18) -0.0003 (-0.17) 0.05 (0.55) 3.3 (0.74) 0.07 (0.50) -0.024 (-1.19) -0.003*** (-3.20) -0.001 (-1.06) 0.01 (0.77) 0.03 (1.23) -0.02 (-0.23) -0.14*** (-3.00)
CONCLUSIONS This paper analyzes the empirical inter-temporal relationship between profitability, risk and market power (competition) for 101 Chinese commercial banks over the period 2003-2009. We delve deeply into these relationships by including two profitability indicators and four different risk measurements under the SUR framework. Our results show that banks with higher market power (or lower level of competition) normally have higher profitability; this finding is in line with the Structure-Conduct-Performance (SCP) theory. In addition, the direction of impact also flows from profitability to market power. The empirical results indicate that banks with higher profitability typically have higher market power (lower level of competition). We do not find any clear evidence of relationship between risk and competition as suggested by either competition-frangibility or competition-stability hypothesis. In addition to the gains from building on previous work on the relationship between bank risk, profitability and competition, our empirical results are highly important for the government and banking regulatory authority to make relevant policies. This is particularly the case with respect to the finding that lower competition precedes an increase of bank profitability in China. Further work should consider the amount of deposit and number of branches which are important in explaining the profitability of banking sector and risk as suggested by Hughes et al. (1996). ENDNOTES 1 They are four of the state-owned commercial banks namely Bank of China (BOC), China Construction Bank (CCB), Agricultural Bank of China (ABC) and Industrial and Commercial Bank of China (ICBC). 2 The NEIO literature was pioneered by Iwata (1974) and enhanced by Bresnahan (1982, 1989) and Panzar and Rosse (1987). 3 SUR estimation, developed by Zellner (1962), is used when the set of equations have contemporaneous cross-equation error correlation. 4 See Moon and Perron (2006). 5 The calculation of Lerner index is not reported in this paper, but it is available upon request. 6 These results are not reported here, but they're available upon request. 7 Our paper also includes the state-owned and joint-stock commercial banks as dummy variables due to the fact that including all three ownerships of banks will cause multicollinearity problem.
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