Risk, competition, and efficiency in Chinese banking: the role of interest rate liberalization. Yong Tan. Department of Accountancy, Finance and Economics, ...
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Risk, competition, and efficiency in Chinese banking: the role of interest rate liberalization
Yong Tan Department of Accountancy, Finance and Economics, Huddersfield Business School, University of Huddersfield, Queensgate, UKJ, HD1 3DH
Abstract The current study contributes to the banking literature by being the first research investigating the competitive conditions of different banking markets using a sample of Chinese commercial banks over the period 2003-2013. It further contributes to banking literature by testing the interrelationships between competition, different types of efficiencies and different types of risk under a three-stage least square estimator. The results show that competition leads to higher capital, liquidity and credit risk, while higher revenue efficiency leads to lower risk. It is found that efficiency is significantly and negatively related to competition. Finally, the findings show that competition-inefficiency hypothesis holds.
Electronic copy available at: https://ssrn.com/abstract=2949045
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1. Introduction China’s economic development has attracted great attention from the rest of the world. During the period 2003-2013, China has an annual GDP growth rate of over 10.2%. The Chinese banking sector has undergone sustainable and healthy development through several rounds of banking reforms initiated by the government since 1978. The main purpose of these banking reforms has been to increase competitive conditions, enhance stability and improve the performance of the Chinese banking sector. State-owned commercial banks (SOCBs) dominate the industry. However, according to statistics from the China Banking Regulatory Commission (CBRC), the share of SOCB assets in total banking sector assets decreases between 2003 and 2013 to a low point of 43.3%. On the other hand, the joint-stock commercial banks (JSCBs) and city commercial banks (CCBs) keep increasing in size and in 2013 they hold 17.8% and 10.03% of total banking sector assets. This shows that competitive conditions in the Chinese banking sector have increased significantly. The Chinese banking industry has reduced its credit risk undertaken over the period 2003-2013. Non-performing loan ratios over the period 2011-2013 keep at 1% which is lower than the figures for 2008-2010. The Chinese banking industry also reduces the capital risk. CBRC statistics show that by the end of 2013, the average capital adequacy ratio of Chinese banks is 12.2% which increases by 1.6% compared to the previous year. In addition, the liquidity ratio of Chinese commercial banks is 44% by the end of 2013. Although the ratio is lower than the figure for 2012, which is 45.8%, it is higher than the ones for 2010 and 2011 which are 42.2% and 43.2%, respectively. There are few pieces of research investigating competitive conditions in the Chinese banking sector (Yuan, 2006; Masood and Sergi, 2011; Fu, 2009; Park, 2013; Tan and Floros, 2013a; Tan,
Electronic copy available at: https://ssrn.com/abstract=2949045
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2014), while although the above statistics give a general picture of risk conditions in the Chinese banking industry, there is no empirical study investigating different types of risk-taking behaviour for different types of ownership of Chinese commercial banks. There are a number of studies examining the effect of competition on risk-taking behaviour in banking industry (Fu et al, 2014; Liu and Wilson, 2013; Liu et al., 2013; Liu et al., 2012;), however, there are very few studies testing the impact of competition on risk-taking behaviour in the Chinese banking industry (Tan and Floros, 2013b; Tan and Floros, 2014, Tan, 2014), the above-mentioned studies mainly focus on credit risk or insolvency risk, they do not consider other types of risk-taking behaviour such as capital risk and liquidity risk. What is the impact of competition on capital risk and liquidity risk? The answer to this question will not only provide relevant policy implications to Chinese banking regulatory authorities but make a unique contribution to the empirical literature with regards to the issue of competition and risk in the banking industry. Several rounds of banking reforms in China not only aim to improve the competitive condition and reduce the risk-taking behaviour of Chinese commercial banks but improve the performance in the Chinese banking industry. There are a number of empirical studies examining the performance of Chinese commercial banks (Garcia-Herrero et al., 2009; Tan and Floros, 2013a; Tan, 2014). In particular, Tan and Floros (2013a) test the inter-relationships between risk, capital and efficiency in the Chinese banking industry. Fungacova et al. (2013) test the interrelationships between cost efficiency and competition in the Chinese banking industry over the period 2002-2011, there is no study examining the effect of competition on different types of efficiency as well as different types of risk-taking behaviour. The investigation on this issue will provide valuable information to Chinese banking regulatory authorities in order to make relevant policies to further improve the performance of Chinese commercial banks.
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In addition, all the empirical studies examined the competitive condition in the whole banking industry and further tested its impact on bank profitability. Therefore, the competitive condition reflects all banking activities including the traditional deposit and loan services as well as the non-interest income generating activities. There are quite a few questions arising: 1) what are the competitive conditions in different banking markets; i.e. deposit market, loan market, and noninterest income market? 2) What are the inter-relationships between competition in different banking markets and different types of efficiency? 3) What are the inter-relationships between competition in different banking markets and different types of risk? 4) What are the interrelationships between different types of risk and different types of efficiency? All these questions have not been investigated in the banking literature. Investigating these issues not only fills the gaps in the empirical banking literature but more importantly, provides significant implications for government and banking regulatory authorities to make relevant policies.
The investigation of this issue is particularly important to the Chinese banking industry. In order to improve the competitive conditions in deposit market and loan market, the Central Bank of China- the Peoples’ Bank of China, has taken the effort to liberalize the interest rate since 1996. From 20th July 2013 and 23rd October 2015, the loan interest rate, as well as the deposit interest rate in China, have been liberalized. The process of interest rate liberalization in these two different markets is supposed to have an impact on their competitive conditions. In the Chinese banking industry, the traditional interest generating activities still contribute to the largest proportion of bank income, however, the interest generating activities are affected significantly by economic cycles as well as credit risk. Thus, Chinese commercial banks have taken the effort to further develop the non-interest generating business which not only can reduce the bank risk but also significantly promote the bank profitability. Therefore, what is the competitive condition
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in the non-interest income market among Chinese commercial banks? This issue has not been addressed by the empirical literature and will be examined in the current paper.
This study contributes to the empirical banking literature by the following ways: firstly, the current study uses stability inefficiency as the insolvency risk indicator rather than the accounting ratio (Deng et al., 2013) and jointly test the inter-relationships between different types of risk and different types of efficiencies, this will provide more accurate results; secondly, this paper is the first study investigating the competitive conditions in different banking markets (deposit market, loan market and non-interest income market); thirdly, this paper is the first study testing the inter-relationships between different types of risk and competition in different banking markets. Fourthly, the current paper is the first study investigating the inter-relationships between different types of efficiency and competition in different banking markets.
2. Theoretical background 2.1 The impacts of competition and efficiency on risk in the banking industry The competition-fragility hypothesis argues that banks have the ability to withstand shocks and decrease risk-taking behaviour due to the fact that in a less competitive environment, banks are able to earn higher profitability through monopoly rents (Allen and Gale, 2004; Boyd and De Nicole, 2005). The competition-stability view suggests that in a less competitive banking market, banks charge higher interest rates, which will increase the probability of default on loan repayments. The bad management hypothesis (Berger and DeYoung, 1997 and Williams, 2004) suggests that lower level of efficiency leads to higher costs because banks do not monitor the credit adequately and also they do not control the expenses efficiently. The declines in efficiency will result in increases in banks’ risk because of credit, operational, market and reputational
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problems. On the other hand, the moral hazard hypothesis (Jeischko and Jeung, 2005) argues that banks with lower levels of efficiency tend to take higher risk. The moral hazard problem arising from the presence of informational friction and the existence of agency problem will make bank managers take on higher risk. There are a number of studies investigating the impact of efficiency on risk in the banking industry (Altunbas et al., 2007; Chortareas et al., 2011; Tan and Floros, 2013a; Saeed and Izzeldin, 2014). 2.2 The impacts of risk and efficiency on competition in the banking industry There are a number of empirical studies investigating the impact of risk on competition (market power) in the banking industry (Fernández de Guevara and Maudos, 2007; Tan and Floros, 2014; Kasman and Carvallo, 2014), and all the papers focus on the investigation of either credit risk or insolvency risk. The impact of efficiency on competition is mainly documented in the efficientstructure hypothesis (Demsetz, 1973). The hypothesis argues that performance plays a decisive role in the structure. To be more specific, this theory suggests that banks with higher levels of efficiency gain market share at the expenses of less efficient banks, so the concentration increases and the competitive conditions reduce. There are a number of research articles investigating the impact of efficiency on competition, most of which focus on the European banking sector (Casu and Girardone, 2006; Andries and Capraru, 2012; Andries and Capraru, 2014). 2.3 The impacts of competition and risk on efficiency in the banking industry Competition-inefficiency hypothesis suggests that competition leads to a decline in bank efficiency for the following reasons. First, Boot and Schmeits (2005) argue that the relationships between customers and banks are less stable and shorter in a higher competitive environment.
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Furthermore, higher bank competition increases customers’ propensity to switch to other service providers. This phenomenon will amplify the information asymmetries and requires additional resources for screening and monitoring borrowers. Second, Chan et al. (1986) argue that in a competitive environment, there is a shorter duration of bank relationships, the reduction of relationship-building activities inhibits the reusability and value of information. A number of studies show that there is a negative impact of competition on efficiency (Evanoff and Ors, 2002; DeYoung et al., 1998; and Kumbhakar et al., 2001). The competition-efficiency hypothesis (Zarutskie, 2013) argues that higher competition induces banks to specialize and focus on certain types of loans or particular groups of borrowers. It will also induce bank managers to adjust their lending technologies. The banks are able to lower the costs of processing and originating loans and better monitor the borrowers. This positive impact is in line with the “Quiet Life hypothesis” which argues that managers with monopoly power enjoy a share of monopoly rents, they are careless in the expense management which leads to a decline in efficiency. The positive impact of competition on efficiency is in accordance with the research findings of Chen (2007), and Dick and Lehnert (2010). The bad luck hypothesis (Berger and DeYoung, 1997) argues risk has a significant impact on efficiency. The hypothesis suggests that increase in problem loans for the banks is mainly attributed to the external events rather than manager’s skills or their risk-taking appetite. The increase in risk incurs additional costs and managerial efforts. Thus, increase in risk precedes a decline in bank efficiency. There are quite a few empirical studies investigating the impact of risk on bank efficiency (Altunbas et al., 2007, Tan and Floros, 2013b). 2.4 The inter-relationships between risk, competition and efficiency in the banking industry
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In the empirical literature, there are very few studies testing the inter-relationships between risk, competition and efficiency in the banking sector. Using a sample of investment banks in ten large developed countries over the period 2000-2008, Firodelisi et al. (2011) examine these interrelationships under Generalized Method of Moments estimators. The findings show that the competition in investment banking worldwide is quite limited, although relatively low competitive pressures are helpful in enhancing banks’ stability, the results report that banks tend to undertake higher risk in a lower competitive environment. Their findings show that competition-stability paradigm holds for the investment banking industry. Using a sample of 272 commercial banks from 15 Latin American countries for the period 20012008, Kasman and Carrallo (2014) test the inter-relationships between competition, risk and efficiency under a granger causality technique. The results show that higher competition leads to greater financial stability, while banks with higher stability enjoy higher market power, also banks with higher market power have higher efficiency. In summary, the empirical banking literature has not investigated the inter-relationships between risk, competition and efficiency very well, while there is no study examining this issue in the Chinese banking sector. The investigation of this issue will provide more policy implications to the Chinese government as well as the banking regulatory authorities. To be more specific, the current paper contributes to the empirical literature in banking not only on testing the interrelationships between these three factors but more importantly, comprehensively evaluating different types of risk-taking behaviour and different types of efficiencies in the Chinese banking industry. Most importantly, this is the first study in banking literature to test the competitive conditions in different banking markets. The findings will be helpful for the Chinese government to enhance stability in the Chinese banking industry.
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3. Methodology 3.1 Estimation on different types of risk in the Chinese banking industry This paper investigates different types of risk-taking behaviour in the Chinese banking industry including credit risk, liquidity risk, capital risk, as well as insolvency risk, the current paper uses relevant accounting ratios to measure the former three types of risk. To be more specific, The ratio of non-performing loans to total loans measures the credit risk, the higher figure of this ratio indicates higher credit risk the ratio of liquid assets to total assets measures the liquidity risk, the higher figure of this ratio shows that the bank has lower liquidity risk the total regulatory capital ratio measures the capital risk, higher total regulatory capital ratio indicates that the bank has lower capital risk, the last type of risk-taking behaviour is insolvency risk, rather than using the accounting ratio, namely the Z-score, the current study uses a translog specification to estimate the stability inefficiency (Tabak et al., 2012) which is supposed to provide more robustness results. The specification is expressed as:
Ln(
W W Z score 1 1 ) it 0 j LnY jit jk LnY jit LnYkit 1 Ln( 1 ) it 2 Ln( 1 ) it W2 2 j k W2 2 W2 j
W j LnY jit Ln( 1 ) it it W2 it j
(1)
Where W represents input prices, there are two input prices which are the price of funds (the ratio of interest expenses to total deposits) and price of capital (the ratio of non-interest expenses to fixed assets). Y represents four outputs which are total loans, total deposits, securities and non-interest income. The sub-index i and t represent bank i operates at time t while j and k represent different outputs. The error term it equals it it . The first term it captures the random disturbance, which is assumed to be normally distributed and represents the
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measurement errors and other uncontrolled factors, i.e. it ~N (0, 2 ). The second term it captures the technical and allocative inefficiency, both under managerial control, and it is assumed to be half-normally distributed, i. e. it ~ N ( it , 2 ). Higher stability inefficiency indicates higher risk while lower stability inefficiency means the risk is lower.
3.2 Measurement of competition in different banking markets in China
The current study uses the method proposed by Boone (2008) to measure the competition. The Bonne indicator holds the idea that the performance of efficient firms is improved and the performance of inefficient firms is weakened by competition. The basic logic of Boone indicator is in line with the argument of efficiency structure hypothesis as developed by Demsetz (1973) which links the influence of efficiency on performance. The performance can be measured by profit or market share. The stronger effect will lead to a more negative Boone indicator. The Boone indicator for bank i can be defined by the simplest equation as follows:
LN (MS ki ) LN (MCki )
(2)
Where i represents a specific bank, k stands for a specific bank output, MS is the market share while MC measures the marginal cost. denotes the Boone indicator. In this paper, we focus on the analysis of competition in different markets reflecting traditional loan and deposit activities as well as non-interest income generating business, this significantly contributes to the empirical banking literature which just focuses on the examination of the whole banking market or only the loan market. Thus, K=loans, deposits, non-interest income.
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Compared to other competition indicators such as Lerner index, Panzar-Rosse H-statistic or Herfindahl-Hirschman index the Boone indicator has the advantages of measuring competition for several specific product markets and different categories of banks. This advantage provides more insight for future research and also provides more valuable information for government and banking regulatory authorities to make relevant policies due to the fact that it does not only know from this indicator on which banking output is subject to more or less competitive pressures but also different types of banks in terms of competition is compared (Tabak et al., 2012). Tan (2016) already uses the Lerner index to measure competitive conditions for different ownership types of Chinese commercial banks, thus, the current study focuses on the estimation of competition in different banking markets on a year by year basis. There is no benchmark for the value of beta, it is only known that the more negative of the value, the stronger competition it is.
With regard to the calculation of marginal cost, rather than using the average variable cost (Boone et al., 2004), we use a translog cost function to estimate it. The advantage of using the translog cost function to estimate the marginal cost lies to the fact that it allows focusing on segments of the market, such as loan market, deposit market, and non-interest income market, where no direct observations of individual cost items are available. Furthermore, rather than using the profit as the dependent variable, we use the market shares following Tabak et al. (2012). The advantage of using market share rather than profit as the dependent variable is that the market shares are always positive while the profit values can be either positive or negative. If the log-linear specification is used, the negative profits would be excluded. In other words, the biased estimation results will be obtained due to the fact that banks with higher inefficiency and higher levels of losses would have to be ignored.
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The marginal cost is estimated on the basis of a translog cost function with four outputs (total loans, total deposits, securities and non-interest income) and two input prices (price of funds, price of capital). The specification of the translog cost function is shown as below (Tabak et al., 2012):
LN (
W W W W C 1 1 ) it 0 j LNY jit jk LNY jit LNYkit 1 LN ( 1 ) it 11LN ( 1 ) it LN ( 1 ) it j LNY jit LN ( 1 ) it it W2 2 j k W2 2 W2 W2 W2 j j
(3)
where C represents total cost of a bank, Y represents four outputs including total deposits, total loans, non-interest income and securities, W stands for two input prices with W1 representing the price of funds which is measured by the ratio of interest expenses to total deposits, W2 represents the price of capital, which is measured by the ratio of non-interest expenses to fixed assets, two input prices are considered due to the fact that non-interest expenses include the labour cost as well (Hasan and Morton, 2003). In other words, the price of capital considers the factors relating to the price of physical capital as well as the price of human capital. The linear homogeneity is ensured by normalizing the dependent variable and W1 by anther input price W2. The summary statistics of the variables are reported in Table 1.
The marginal cost of loans can be obtained by taking the first derivative of the dependent variable in the above equation in relationship to the output loans as follows:
MCilt (
Cit / W2 W )( j l 2 ll LNYilt lk LNYikt l LN ( 1 )) Yilt W2 k 1....k , k l
(4)
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The marginal costs of deposit and non-interest income can be obtained similarly by taking the first derivative of the dependent variable in the above equation in relationship to the outputs deposits and non-interest income as below:
MCidt (
Cit / W2 W )( j d 2 dd LNYidt dk LNYikt l LN ( 1 )) Yidt W2 k 1....k , k d
(5)
MCint (
Cit / W2 W )( j n 2 nn LNYint nk LNYikt l LN ( 1 )) Yint W2 k 1....k , k n
(6)
3.3 Estimation of different types of efficiencies in the Chinese banking industry
There are mainly two approaches which are widely used in estimating bank efficiency, they are Stochastic Frontier Approach (SFA) and Data Envelopment Analysis (DEA). The main argument for using the DEA rather than parametric techniques, such as SFA, lies in the fact that it works particularly well with small samples. Furthermore, it is able to handle multiple inputs and outputs stated in different measurement units and it does not necessitate knowledge of any functional form of the frontier (Charnes et al., 1995). Most empirical papers show that using DEA to estimate the efficient frontier can yield robust results (Seiford and Thrall, 1990). However, although DEA has a few advantages compared to SFA with regard to the efficiency estimation, it also suffers from a number of disadvantages. First and foremost, DEA does not assume statistical noise, which means that the error term in the estimation is attributed to inefficiency. Therefore, the influence of a number of factors such as bad data, luck and extreme observations is accounted as inefficiency in DEA. Secondly, Sun and Chang (2011) further argue that measuring DEA in small samples is sensitive to the difference between the number of firms and the sum of inputs and outputs used. Fries and Taci (2005) argue that the SFA is more appropriate over the
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DEA in efficiency studies in developing countries where problems of measurement errors and uncertain economic environment are more likely to prevail. Therefore, the current study uses the SFA to estimation cost efficiency in the Chinese banking industry. The efficiency level can be estimated by specifying the commonly-used translog functional form for the cost function, the cost function will be the same as equation (3), while an additional equation has been added to separate the error term into two components as follows:
it it uit
(7)
Where it is a two-sided normal disturbance term with zero mean and variance v and represent 2
the effect of statistical noise, and
uit is a non-negative random disturbance term capturing the
effects of inefficiency. With regard to the estimation of profit efficiency and revenue efficiency, we use the same specification and just replace the dependent variable to profit and gross revenue. The profit indicator used is Return on Assets (ROA). 3.4 Estimation on the interrelationships between risk, competition and efficiency We use the Three-Stage Least Square estimator to investigate the inter-relationships between risk, efficiency and competition as it takes into account both endogeneity and the cross correlation between the error terms. In order to disentangle the inter-relationships, the following equations will be estimated:
(8)
(9)
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(10) Where subscript i and j represent specific bank operating at a specific year, risk is different risk conditions in the Chinese banking industry including credit risk, liquidity risk, capital risk, as well as insolvency risk. Efficiency is the cost, revenue and profit efficiencies derived from the SFA, while the competition is competitive conditions in different banking markets derived from the Boone indicator. The simultaneous equations also control for various bank-specific, industryspecific and macroeconomic variables. The bank-specific variables include bank size (size) (natural logarithm of total assets), diverse represents bank diversification (the ratio of noninterest income to gross revenue), while the last bank-specific variable is profit, (Return on Assets (ROA)). The current study controls for two industry-specific variables which are, BSD, representing banking sector development (the ratio of total banking sector assets to GDP) and SMD, standing for stock market development (the ratio of market capitalization of listed companies to GDP). Finally, there are two macroeconomic variables which are, inflation (annual inflation rate) and GDPG (annual GDP growth rate). All these controlled bank-specific, industryspecific and macroeconomic variables are supposed to influence the risk, competition and efficiency of Chinese commercial banks. Table 2 provides the summary of the variables used in the current study.
The banking data includes 100 Chinese commercial banks (5 SOCBs, 12 JSCBs and 83 CCBs) over the period 2003-2013. Due to the fact that not all selected banks have available information for all years, the current paper opts for an unbalanced panel dataset not to lose degrees of freedom. With regard to the data sources, the current study collects the data of bank-specific
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variables from Bankscope, the industry-specific variables and macroeconomic variables are from the CBRC annual reports as well as the World Bank database. Table 3 shows the summary statistics of the variables used in the current study. The results show that the difference of liquidity risk undertaken by Chinese commercial banks is smaller than the ones of credit risk and capital risk. While higher levels of credit risk undertaken by Chinese commercial banks is attributed to the fact that during 2003-2006, there are large volumes of non-performing loans in SOCBs especially in the Agricultural Bank of China, while the large difference of capital risk is attributed to the opening of one joint-stock commercial bank, namely, the China Bohai Bank in 2006 which has total regulatory capital ratio of over 60%. With regard to other bank-specific variables, the results indicate that Chinese banks have a big difference in the degree of diversified activities engaged in, while the difference in the profitability among Chinese banks is relatively smaller. The difference in bank size is attributed to the fact that SOCBs is bigger than JSCBs, while CCBs is the smallest. The results further show that there is a stronger volatility with regard to the stock market development than the banking sector development and the macroeconomic environment. The stronger volatility of stock market development can be mainly attributed to the segregation reform initiated by the Chinese government in 2005 which leads to a substantial amount of companies listed on stock exchange. By the end of 2007, there are 1550 listed companies in Shanghai and Hong Kong stock exchanges, the value of which reaches RMB 32.71 billion, accounting for 132,6% of GDP at that year. On the other hand, the stock market development is in its early stage before 2005.
4. Empirical results 4.1. risk conditions in the Chinese banking industry
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Figures 1a, 1b, 1c, and 1d report the risk conditions of Chinese banks over the period 20032013, Figure 1a shows that over the period 2003-2008, the credit risk of SOCBs is substantially higher than that of JSCBs and CCBs, Although the figure shows that after 2008, all the three different ownership types of Chinese commercial banks have few differences with regards to credit risk, the credit risk of CCBs is higher than that of JSCBs between 2005 and 2010. Figure 1b shows that in general, the ratio of liquid assets to total assets for SOCBs is lower than that of JSCBs and CCBs. In other words, the SOCBs have the highest liquidity risk. However, liquidity is the highest in CCBs over the period 2005-2008. In general, the capital level of CCBs kept increasing for most of the years over the period examined, although CCBs showed a slight decrease in some of the years. Figure 1d shows that the risk conditions in the Chinese banking sector over the period 2003-2006 were highly volatile; while during 2007-2013, they reduced.
4.2. Efficiency in the Chinese banking industry
Figure 2 reports the results with regard to the cost, revenue and profit efficiencies of three different ownership types of Chinese commercial banks over the examined period. It is noticed from the figure that city commercial banks have the highest cost, revenue and profit efficiencies, which is followed by state-owned commercial banks, while the joint-stock commercial banks have the lowest efficiencies over the examined period. Figure 3 shows the results with regard to cost, revenue and profit efficiencies of three different ownership types of Chinese commercial banks on an annual basis. The findings show that cost efficiency of Chinese commercial banks is more stable over the examined period compared to revenue efficiency, while it is found that Chinese commercial banks have the strongest volatility in profit efficiency.
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4.3. Competition conditions in different banking markets in China
Figure 4 shows the competitive conditions in different banking markets (loan market, deposit market and non-interest income market) in China over the period 2003-2013. The finding shows that the competition among Chinese commercial banks in the loan market, deposit market and non-interest income market is relatively stronger over the period 2003-2005 while the competition for the rest of the examined period is relatively lower although, in 2007-2008, the competition is higher than the rest of the years. It is noticed that over the period 2006-2013, the competitive condition in these three different markets is the same; the main difference is noticed during the period 2003-2005. The figure shows that the competitive condition in the non-interest income market is the highest in general between 2003 and 2005 compared to the other two markets while the competitive conditions in the loan market and the deposit market are the same.
4.4.
Inter-relationships between risk, competition and efficiency in the Chinese banking
industry Tables 4, 5 and 6 report the results of the inter-relationships between credit risk, competition and efficiency in the loan market, deposit market and non-interest income market, respectively. The findings suggest that higher competition leads to higher credit risk of Chinese commercial banks. This result is in line with the competition-fragility hypothesis. It is further indicated that higher
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credit risk undertaken by the Chinese commercial banks leads to high level of competition (lower market power) in the Chinese banking industry. This can be explained by the fact that higher levels of credit risk induce a larger amount of cost, which further reduce the profit margin and precedes a decline in market power and an increase in the level of competition. The tables further report that Chinese commercial banks with higher levels of credit risk have higher cost efficiency. This is not in line with the bad luck hypothesis. With regard the impacts of efficiencies on credit risk, the findings show that Chinese commercial banks with higher levels of cost efficiency have higher credit risk, while the impacts of profit and revenue efficiencies on credit risk are significant and negative. It is reported from the table that higher efficiency leads to lower competition in all the markets. Finally, it is reported that higher level of competition leads to lower cost efficiency in the Chinese banking industry. This is in line with the competitionefficiency hypothesis.
Tables 7, 8 and 9 report the results with regard to the inter-relationships between liquidity risk, competition and efficiency in deposit market, loan market as well as non-interest income market, respectively. The results indicate that higher level of competition in the Chinese banking industry leads to higher levels of liquidity risk of Chinese commercial banks, while it is suggested that Chinese commercial banks with higher levels of profit and revenue efficiencies have lower liquidity risk. The same as the findings as the credit risk, the results show that higher levels of liquidity risk lead to higher level of competition in the Chinese banking industry. In deposit, loan
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and non-interest income market, higher levels of efficiency of Chinese commercial banks result in lower level of competition. The tables find that there is a significant and negative impact on liquidity risk and revenue and profit efficiencies. In other words, lower levels of liquidity risk lead to higher revenue and profit efficiencies. Theses tables show that in the Chinese banking industry, it follows the theory of competition-inefficiency hypothesis due to the fact that it is found that higher competition leads to lower cost and revenue efficiencies of Chinese commercial banks.
Tables 10, 11 and 12 report the results in terms of the inter-relationships between capital risk, competition and efficiency in the loan market, deposit market and non-interest income market, respectively. The findings show that higher level of competition in China leads to higher capital risk. It is further suggested that higher levels of revenue and profit efficiencies lead to lower levels of capital risk. It is the same as liquidity risk and credit risk, the results report that higher levels of capital risk precede to an increase in the level of competition. Chinese commercial banks with higher levels of efficiency reduce the competitive conditions in all the banking markets. It confirms the finding reported previously in credit risk and liquidity risk which is higher competition in all the markets leads to lower efficiencies of Chinese commercial banks. Finally, it is reported that Chinese commercial banks with higher levels of capital risk have lower revenue and profit efficiencies.
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Finally, tables 13, 14 and 15 report the results with regard to the inter-relationships between insolvency risk, competition and efficiency in three different markets namely loan market, deposit market and non-interest income market, respectively. The findings suggest that Chinese commercial banks with higher levels of cost efficiency lead to higher insolvency risk in the Chinese banking industry, while higher revenue efficiency reduces the instability(lower insolvency risk). It is further reported that higher efficiencies of Chinese commercial banks reduce the competitive conditions in all the banking markets. The tables indicate that higher instability in the banking (higher insolvency risk) leads to higher cost efficiency, but lower revenue and profit efficiencies of Chinese commercial banks. Finally, it is suggested that higher levels of competition in all the banking markets lead to lower efficiencies of Chinese commercial banks.
In summary, the results of the current paper show that higher levels of competition in the Chinese banking industry lead to higher capital, liquidity and credit risk, while higher levels of revenue efficiency result in lower levels of all types of risk. In addition, the results report that higher levels of efficiencies of Chinese commercial banks reduce the competitive conditions in all the banking markets in China. It is found that lower levels of capital, liquidity and insolvency risk leads to higher revenue and profit efficiencies of Chinese commercial banks, while higher
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levels of credit risk and insolvency risk lead to higher cost efficiency. Finally, it is found that higher levels of competition lead to lower cost efficiency. 5. Conclusion The current study contributes to the banking literature (in particularly China) by comprehensively investigating different types of efficiencies of Chinese commercial banks including cost efficiency, profit efficiency and revenue efficiency. With regard to the risk-taking behavior, the current study contributes to general banking literature by comprehensively examining different types of risk the Chinese banking industry namely credit risk, capital risk, liquidity risk as well as insolvency risk. Most importantly, the current study is the first piece of research investigating the inter-relationships between different types of efficiencies, competition in different markets as well as different types of risk in the banking literature which is supposed to provide invaluable policy implications to the Chinese government as well as banking regulatory authorities. The findings of the current paper suggest that the competition in loan market, deposit market as well as non-interest income market is strong over the period 2003-2005 compared to the rest of the period examined and it is also found that between 2003-2005, the competition is strongest in the non-interest income market, while the competition between deposit market and loan market is nearly the same over the examined period. With regards to different types of efficiencies, the results report that city commercial banks have the highest cost, profit and revenue efficiencies over the examined period, which is followed by state-owned commercial banks, while the jointstock commercial banks have the lowest efficiencies. In the Chinese banking industry, it is found that cost efficiency is more stable over the examined period compared to revenue efficiency and profit efficiency has the highest volatility.
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The results of the current paper show that the impacts of competition in all the markets on credit risk, liquidity risk and capital risk are significant and positive. It is further suggested that revenue efficiency of Chinese commercial banks reduces the levels of all types of banking risk. The levels of competition conditions in different banking markets can be reduced by further improving the efficiencies of Chinese commercial banks. The current paper shows that the competitive conditions in different banking markets in China reduce the cost efficiency of Chinese commercial banks. The results of the current paper suggested to Chinese government and banking regulatory authorities to further improve the management of input and output process in banking operation, the resulted improvement in efficiency will reduce the risk levels of commercial banks, while relevant policies should be made to reduce the level of competition in order to improve the efficiency and reduce levels of risk-taking behavior.
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Figure 1 Risk conditions in the Chinese banking industry: 2003-2013
Figure 1a Credit risk in the Chinese banking industry
Figure 1c Capital risk in the Chinese banking industry
Figure 1b Liquidity risk in the Chinese banking industry
Figure 1d Insolvency risk in the Chinese banking industry
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Figure 2 Cost, revenue and profit efficiencies of Chinese commercial banks by ownership types
Table 2 Description of the variables used in the study Variables
Indicator
Definition
Risk
Credit risk
Ratio of non-performing loans to total loans
Competition
Liquidity risk
Ratio of liquid assets to total assets
Capital risk
Total regulatory capital ratio
Insolvency risk
Stability inefficiency
Boone indicator
Competition in deposit market
Competition in loan market
Competition in non-interest income market Efficiency
Cost, profit and revenue
SFA
efficiencies Bank-specific variables Bank size
Size
Natural logarithm of total assets
Bank diversification
Diverse
Ratio of non-interest income to gross revenue
Bank profitability
ROA
Return on Assets
BSD
Ratio of total banking sector assets
Industry-specific variables Banking sector development
to GDP Stock market development
SMD
Ratio of market capitalization of listed companies to GDP
Macroeconomic variables Inflation
INF
Annual inflation rate
GDP growth
GDPG
Annual GDP growth rate
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Table 3 Descriptive statistics of the variables used in the current study
Variables
Observations
Mean
S.D
Min
Max
Credit risk
632
2.78
4.48
0
41.86
Liquidity risk
777
0.27
0.11
0.02
0.67
Capital risk
637
11.91
4.7
0.62
62.62
Insolvency
1100
0.33
0.21
0.025
0.789
806
0.009
0.007
-0.04
0.106
Bank size
843
4.9
0.992
0.71
8.51
Bank
828
13.98
13.31
-12.94
79.4
1100
2.22
0.24
1.98
2.66
Stock market 1027
71.2
43.49
31.9
184.1
2.86
1.92
-0.77
5.86
10.19
1.87
7.7
14.2
risk Bank profitability
diversification Banking sector development
development Inflation GDP rate
1227
growth 1199
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Table 4 The inter-relationships between credit risk, competition and efficiency in the Chinese banking industry (loan market) Y=credit risk Credit risk competition
efficiency Bank size Bank diversification ROA Banking sector development Stock market development inflation GDP growth Constant
Table 5 The inter-relationships between credit risk, competition and efficiency in the Chinese banking industry (deposit market) Y=credit risk Credit risk competition
efficiency Bank size Bank diversification ROA Banking sector development Stock market development inflation GDP growth Constant
Table 6 The inter-relationships between credit risk, competition and efficiency in the Chinese banking industry (non-interest income market) Y=credit risk Credit risk competition efficiency Bank size Bank diversification ROA Banking sector development Stock market development inflation GDP growth Constant
Y=competition (non-interest income market) -0.0009*** (-14.10)
Table 7 The inter-relationships between liquidity risk, competition and efficiency in the Chinese banking industry (loan market) Y=liquidity risk liquidity risk competition efficiency Bank size Bank diversification ROA Banking sector development Stock market development inflation GDP growth Constant
Table 8 The inter-relationships between liquidity risk, competition and efficiency in the Chinese banking industry (deposit market) Y=liquidity risk liquidity risk competition efficiency
Bank size Bank diversification ROA Banking sector development Stock market development inflation GDP growth Constant
Table 9 The inter-relationships between liquidity risk, competition and efficiency in the Chinese banking industry (noninterest income market) Y=liquidi ty risk liquidity risk competition efficiency Bank size Bank diversification ROA Banking sector development Stock market development inflation GDP growth Constant Observations Chi-square
Table 10 The inter-relationships between capital risk, competition and efficiency in the Chinese banking industry (loan market) Y=capital risk capital risk competition efficiency Bank size Bank diversification ROA Banking sector development Stock market development inflation GDP growth Constant
Table 11 The inter-relationships between capital risk, competition and efficiency in the Chinese banking industry (deposit market) Y=capital risk Capital risk competition efficiency Bank size Bank diversification ROA Banking sector development Stock market development inflation GDP growth Constant
Table 12 The inter-relationships between capital risk, competition and efficiency in the Chinese banking industry (non-interest income market) Y=capital risk capital risk competition efficiency Bank size Bank diversification ROA Banking sector development Stock market development inflation GDP growth Constant
Table 13 The inter-relationships between IR (insolvency risk), competition and efficiency in the Chinese banking industry (loan market) Y=capital risk IR competition efficiency Bank size Bank diversification ROA Banking sector development Stock market development inflation GDP growth Constant
Table 15 The inter-relationships between IR (insolvency risk), competition and efficiency in the Chinese banking industry (non-interest income market) Y=IR
IR competition efficiency Bank size Bank diversification ROA Banking sector development Stock market development inflation GDP growth Constant
