Ownership Structure, Risk and Profitability in the Chinese Commercial Banking Industry

Ownership Structure, Risk and Profitability in the

Chinese Commercial Banking Industry

University of Groningen Faculty of Business and Economics

Master Thesis Finance Student name: Zijin Zhu Student number: S2622483

Abstract

This paper examines the impact of ownership structure on Chinese commercial banks' risk-taking behaviors and profitability. The Chinese commercial banks are classified into four categories based on the types of controlling shareholder.1 The main results show that ownership concentration has no significant impact on Chinese commercial banks in terms of profitability. There is no evidence that ownership concentration affects riskiness of the banks except during the financial crisis.2 Different natures of shareholders are also found to affect the banks’ riskiness and profitability. Having the government as the dominate shareholder3 will undermine the profitability of the banks. Banks with high proportion of state-owned enterprises shareholding are more risky. Moreover, having the state-owned enterprises as the dominate shareholder can reduce banks’ profitability. The banks controlled by private enterprises tend to be more profitable, but the result is not consistent through time. Finally, the banks controlled by financial institutions are found to be less risky. Consequently, this paper supports the reform in the Chinese banks to transfer more bank ownership from the government to market-oriented entities, especially to the financial institutions.

Keywords:

Chinese commercial banks, bank Risk-taking, profitability, ownership structure, state ownership.

1

The four bank categories in this paper are: banks controlled by the government, banks controlled by state-owned enterprises, banks controlled by private enterprises, and banks controlled by financial institutions.

2

The effect consistency test suggests that banks with high level of ownership concentration are less risky between 2009 and 2011.

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1. Introduction

While American and European banking industries suffered a great loss from the recent financial crisis, the impacts of the crisis seem to be much less on the Chinese commercial banking industry (CCBI). According to the British magazine The Banker, the CCBI takes up 31.78% of the aggregate pre-tax profits of the global banking industry in 2014. In The Banker’s "Top 1000 World Banks" ranking4 in 2014, there are 105 Chinese (mainland) banks in total, among which, the four biggest Chinese banks were selected as the "Top Ten Global Banks". Therefore, it is believed that the growth of CCBI is actually accelerating, and playing a more and more important role in the global banking industry during the recent years.

At the same time as the CCBI is capturing a bigger and bigger share in the global banking industry, Chinese commercial banks are required to adopt the modern banking business rules and regulations like the other banks all over the world. Consequently, a series of reforms have been executed to deal with those problems, which changed the CCBI dramatically. On one hand, although the government still holds a majority numbers of shares in the CCBI, their proportions are shrinking and the proportions of private asset shares are rising. On the other hand, the ownership concentration of the banks is also changing gradually. Therefore, how the recent trend in the ownership structure change in the CCBI would affect the risk taking behavior and the profitability of the banks will be the main question studied in this paper.

The remainder of the paper is structured as follows. Section 2 is the institutional background. Section 3 will be the literature review and establishment of the main hypothesis. Section 4 describes the data. Section 5 presents the methodology. The empirical results are discussed in section 6. Section 7 provides effect consistency and robustness check. Section 8 acknowledges the limitation of this paper. Finally,

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section9 concludes the paper.

2. Institutional background

The reforms of CCBI began in 1979 with the purpose of addressing the institutional, political and management problems faced by the CCBI, and establishing a more stable, competitive, and efficient financial system in China. The most recent round of reform started in 2003, which including financial capital injections, ownership structures reform, foreign investments absorbing, and the listing of banks on stock exchanges. As a result, parts of the CCBI are owned or controlled by private capital, and the Chinese commercial banks are now functioning more like western banks than before. Among those reforms, it is believed that the change of ownership structure is the most significant, which deserves more attention on its effects on the banks and possible further improvements.

Table 1. The transition of components in the CCBI from 2003 to 2014 in account of total asset.

components 2003 2006 2009 2014

state-owned commercial banks 55.1% 51.3% 50.9% 41.2%

joint stock commercial banks 14.2% 16.2% 15% 18.2%

city commercial banks 5.1% 5.9% 7.2% 10.5%

others 25.6% 26.6% 26.9% 30.1%

Note: Data is collected from the China Banking Regulatory Commission annual reports. All the value in table 1 is calculated by using the total asset of each component divided by the total asset of the CCBI. The rural commercial banks, credit cooperatives, foreign banks and etc. are all included in ‘others’.

Since 2003, the Chinese government began to reduce their shares in the banking sector and encourage private capital to be more active in the country’s banking sector. The four biggest Chinese state-owned banks then started the partial privatization strategy to transfer the shareholding ownership structure by listing the four biggest banks through foreign and Chinese stock exchanges. With those measures, a significant portion of the shares of these four banks have been transferred to domestic institutions, foreign investors and the public individuals, while dominate control of the banks remains with the government.

second tier of Chinese banks, and they are playing a more and more important role in the development of the country. The establishment and success of joint-stock banks are considered to be the results and proof of the successful Chinese economic and financial reform. Joint-stock banks shares are distributed among all types of investors including the central government, local government, state-owned enterprises (SOEs), private and foreign investors, so their ownership structures are different from each other. Joint-stock banks offer a wide variety of banking services, including taking deposits, making loans, and providing foreign exchange and international transaction services. The state-owned banks are usually tend to provide more loans tostate-owned enterprises, because they are large corporation backed by the government unlike the small and medium scale enterprises. The joint-stock banks however regularly finance small and medium enterprises, which makes the market dynamic and sustainable.

Since 1995, city commercial banks (CCBs) have been created to provide financial support for local economic development. City commercial banks are relatively small scale banks who mainly operating within a certain province. They represent the third tier of Chinese banks. Although through the restructuring and consolidation their ownerships have been diversified to certain extent, most of the city commercial banks are wholly owned or controlled by local government. Until recent years, the city commercial banks are usually enjoy government subsidies and still nursing their strength.

ratio (CAR) of 8%, which is in line with the Basel rules. Interest rates (both deposit and loan lending rate) are tightly controlled for all banks in China. Each bank may use their desired interest rates, but these interest rates must be within a range of the interest rate baseline which is designated by the Chinese central bank.

Chart 1 below shows the asset growth of Chinese banking industry from 2003 to 2014. By December 2014, the total asset of the Chinese commercial banks is 27124.4 billion USD, much larger than that of the US commercial banks (which is 15026.3 billion USD); however, the financial industry (including banks and all other types of financial institutions) only contributed 7.38% of the total Chinese GDP. By contrast, the American financial industry already contributed more than 10% of the total GDP of the country in 2009.5 Therefore, although the Chinese commercial banks have been growing rapidly indeed, the industry is still not as effective as the banking industries in the developed countries.

Chart1. Total asset of CCBI in USD 2003-2014.

Note: Data is collected from the China Banking Regulatory Commission annual reports. The original data is recorded with the Chinese currency RMB, to transform the data into the USD, the exchange rate is set as 1 USD=6.2 RMB.

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3. Literature review & Hypothesis development

Barry et al. (2011) and Yeung et al. (2008) suggest that the performance and risk-taking behavior of organizations have a strong connection to the banks’ ownership structure (or the control power of each type of shareholders in the bank). According to Ianotta et al. (2007), a firm ownership structure can be defined within two main dimensions: the degree of ownership concentration and the nature of the owners. The first dimension means firms may differ due to their different ownership dispersion. The second dimension suggests firms can differ by the nature of the owners even when their degree of ownership concentration is the same.

Based on the two dimensions of ownership structure described by Ianotta et al. (2007), this paper has three objectives. The first objective is to investigate whether ownership concentration can influence the risk-taking decisions and profitability of the banks. The second objective is to identify commercial banks have differences in terms of riskiness and profitability due to the different nature of the owners. The third objective is to test whether these effects are consistent during the time period of 2006 to 2014. In this paper, the nature of the owners will be divided into four categories, which are the (central and local) government, state-owned enterprises (SOEs), private enterprises, and financial institutions. Combined with the literature review in the previews section, therefore three hypotheses cat be set up.

Hypothesis 1: Ownership concentration has significant impact on the riskiness and profitability of the banks. High ownership concentration will increase the risks and decrease the profitability of the banks.

Hypothesis 3: The effects of ownership structure on the riskiness and profitability of the Chinese commercial banks are consistent through the investigated time period.

3.1. Impacts of ownership concentration

In terms of effect of the first dimension of the ownership structure (namely the ownership concentration), this paper will measure the ownership concentration of the sample which then will be used to investigate its impact on the risk-taking behavior of the Chinese commercial banks. Ownership concentration is measured by the distribution of the ownership within the shareholders, and it is related to the shareholders' controlling power within the organization.

The impacts of ownership concentration on a bank’s performance are still unclear. Different approaches have been employed to explore the relationship between ownership concentration and bank risk-taking behavior or performance.

Previous literature works suggest that the ownership concentration could deeply affect a bank's performance and riskiness. Theoretical works such as Shleifer & Vishny (1986) claim that by increasing monitoring and preventing free-rider problem, a concentrated ownership can enhance corporate control which can lead to better performance of the banks. Aghion & Tirole (1997) and Edwards & Nibler (2000) also support this theory. Moreover, Shehzad et al. (2010) argue that dispersed ownership may actually prevent efficient decision-making process. However, Shleifer and Vishny (1997) suggest that dominate shareholders sometimes have the incentive of abusing the control rights to create private benefits instead of the firm’s benefits.

3.2. Impacts of different shareholder nature

claim that political interference may have conflicts with market objectives, which lead to possible misallocation of resources, different value maximization aims, and inefficiency in business operations and corporate governance.

3.2.1. Banks with Government as first shareholder

Bonin et al. (2005), Berger et al. (2005) and Fries & Taci (2005) claim that state-owned banks tend to be less efficient and perform worse in the long term. Ianotta et.al (2007) finds that government-owned banks have lower profitability and poorer loan quality. Angkinand & Wihlborg (2010), Faccio et.al, (2006), and Okazaki (2007) argue that state-owned banks generally enjoy the advantage of either implicit or explicit financial support (such as large amount of policy-directed loans) and regulatory support or protection (such as foreign bank entrance barrier) from the government which might encourage them to take riskier projects and less efficient in operations.

There are also some contradictory results. State-owned banks are also found to be less risky, more efficient, and more profitable. However, those findings are mainly in countries with weak financial markets and strong state-owned banking section traditions such as Russia, Turkey, and India (see Solanko et al., 2009, Bhattacharyya et al., 1997 and Isik & Hassan, 2002). Regarding to the researches of commercial banks in China, Fu & Heffernan (2005) finds that joint-stock banks are more efficient than state-owned banks on an average term.

state-owned banks find themselves can hardly deny such harmful government interference.

In other words, unlike private banks who can employ more reasonable lending and risk protection policies and profit-maximizing strategies, state-owned banks need to consider their own return as well as the government’s return, in some cases, the latter even comes as the priority. Furthermore, the government also has the right to appoint the banks mangers. Therefore, senior officers of the state-owned banks have less incentive to monitor and manage the bank. Because of those significant differences, a shift in the banks’ ownership structure between state-owned banks and private banks will definitely have significant impacts on the industry or even to the whole economy.

3.2.2. Banks controlled by SOEs

Furthermore, state-owned enterprises as shareholders of a bank are different from the government itself in many aspects. First, state-owned enterprises are more essentially corporations than governments which suggest that they usually do not have story political and social objectives. Chinese state-owned enterprises have become more and more responsible for their own operations in the recent years. Thus, state-owned enterprises shareholders have the incentives to ensure that banks under their control are in good condition. Second, banks controlled by state-owned enterprises have financial constraints (these banks usually do not have large amount of financial support from the government). Therefore, they are more motivated to adopt profit-maximizing strategies and efficient management. Finally, banks controlled by state-owned enterprises generally adopt a performance-related promotion and reward system, which given the top executives sufficient incentives to use their talent and work hard.

3.2.3. Banks controlled by private enterprises

practices. However, overall according to previews literatures such as Sheshinski et al. (2003) the banks controlled by private enterprises are believed to be more profitable and less risky.

3.2.4. Banks controlled by financial institutions

Barry et al. (2011) claim that institutional investment shareholders will always finance a project with positive expected return, because the financial institutions are operating in a variety of financial products, so their risks have been diversified away. Typically, financial institutions as shareholders have some advantage in terms of financial regulations, risk management skills and operation practices. Therefore, it is reasonable to believe that banks controlled by the financial institutions are more profitable and less risky.

4. Data & sample selection

In order to identify the impact of ownership structure on the riskiness and profitability of the Chinese commercial banks, this paper collects the detailed information about the banks' ownership structures from the banks' annual reports, such as the proportions of bank shares holding by the top ten shareholders. The bank-specific accounting data are collected from the BankScope and also from annual reports of the banks. The final sample contains 135 annually observations covering 16 Chinese commercial banks (including 5 biggest commercial banks, 8 national joint-stock commercial banks, and 3 city commercial banks). over the period from 2006 to 2014 Since their combined assets account for more than 79% of CCBI in 2013, therefore, an analysis conducted on these banks would be representative of the CCBI.

5. Methodology

5.1. Empirical models

return on assets ratio is introduced to measure the profitability. To examine the impacts of ownership concentration on the risk-taking and profitability of banks in China, the following regression equation 1 and equation 2 are employed:

LN(Z)i,t = α + ϐ1 HHIi,t+ ϐ2 Ln(TA)i,t + ϐ3 CARi,t + ϐ4 NPLi,t + ϐ5 LTDi,t+

μ

τ

ε

ROAi,t = α + ϐ1 HHIi,t+ ϐ2 Ln(TA)i,t + ϐ3 CARi,t + ϐ4 NPLi,t + ϐ5 LTDi,t+

μ

τ

ε

Where: the dependent variable LN(Z) in regression equation 1 refers to the natural logarithm of Z-Score. It is designed to test the impacts of ownership concentration on riskiness of the Chinese commercial banks. The regression equation 2 studies the relationship between ownership concentration and profitability, and the dependent variable ROA refers to as the return on assets ratio of the Chinese commercial banks. The independent variable HHI is the Herfindahl index which measures the ownership concentration level of a bank. The calculation of HHI is based on the number of shares (not reweighted) holding by the top 10 shareholders of the banks.

The other independent variables in the model are control variables. Ln(TA) represents the natural logarithm of total asset of a bank, it is the proxy of the bank size. CAR is the capital adequacy ratio of the bank at certain time period. NPL is the non-performing loans to the gross loans ratio of a bank in a certain time period. LTD is the ratio of total custom loans divided by the total custom deposits which assesses the extent to which customer loans are financed by customer deposits, and is related to the bank's liquidity.

μ

τ

ε

LN(Z)i,t = α + ϐ1Governi,t + ϐ2SOEi,t + ϐ3PEi,t + ϐ4Fini,t + ϐ5Ln(TA)i,t + ϐ6NPLsi,t +ϐ7CARi,t

+ϐ8FUNDi,t +

μ

τ

ε

ROAi,t = α + ϐ1Governi,t + ϐ2SOEi,t + ϐ3PEi,t + ϐ4Fini,t + ϐ5Ln(TA)i,t + ϐ6NPLsi,t +ϐ7CARi,t

+ϐ8FUNDi,t +

μ

τ

ε

Where: The dependent variables and control variables in regression equation 3 and equation 4 are in line with the two previous equations. With regard to other independent variables, Govern represents the proportion of government controlled shares; SOE is the proportion of bank shares held by the state-owned enterprises; PE is the proportion of bank shares controlled by private enterprises; and Fin represents the proportion of shares hold by the financial institutions. The calculations of Govern, SOE, PE, and Fin are based on the number of shares holding by the top ten shareholders of the banks and the data is not reweighted.

5.2. Variables description

5.2.1. Measurement of the bank risk

The Z-score is introduced as a risk measure by Boyd and Graham (1986), which is used as a proxy of probability of default a bank. An individual bank’s Z-score equals its return on assets ratio (ROA) plus the capital to assets ratio (E/A) divided by the standard deviation of asset returns (σ(ROA)). A higher Z-score indicates that a bank is more stable and less risky.

Z − score =𝑅𝑂𝐴 + 𝐸/𝐴 𝜎(𝑅𝑂𝐴)

et al. (2009) argue that Z-score could underestimate banking risk, because the Z-score only measures risk in a single time period and does not account possible negative profits in the long term. However, since all the banks in the sample have positive profits from 2006 to 2014, thus, this drawback of the Z-Score does not have an important impact on the results. Another concern is the usage of accounting data in the calculation of Z-score which would undermine the comparability of Z-score, because the accounting rules and credibility can differ from banks to banks. This, however, is also less of a concern as well, since this paper focus on Chinese banks that are all adopted to the same accounting principles.

There is one problem in the calculation of the Z-score that desires attention though. Because the standard deviation of ROA over certain time period is involved in the calculation, the Z-score calculated for the observation at the beginning of the sample would be have included the future ROA volatility of the sample. Lepetit & Strobel (2013) use a simple root mean squared error criterion to examine the explanatory power of the Z-scores calculated with different approaches over the banking sectors of the G20 countries during 1992-2009. Their results suggest that for banks in China or the USA, the Z-score which uses standard deviation estimates of the ROA calculated over the full sample together with current values of the financial ratios actually fits the sample data better. Since the Z-score is highly skewed, it is suggested that its natural logarithm should be used to smooth out the skewness.

5.2.2. Measurement of the bank profitability

reflects the ability of a bank to generate profits from its assets. ROE is as another measure of profitability which reflects the return to owners’ investment. ROE is equal to net income (after preferred stock dividends, before common stock dividends), divided by total equity (excluding preferred shares). It is often used as the reflection of the bank’s equity multiplier measuring financial leverage (Delis et al., 2006). The net interest margin (NIM) is introduced as an alternative indicator of bank profitability, which focuses on earning assets and interest activities. It is a measure of the difference between the interest income generated by banks and the amount of interest paid out to their lenders, relative to the amount of their interest-earning assets. The higher the ratio, the lower cost of the funding and the higher margin is acquired for the bank.

denominator are reduced by the non-performing assets, that does not mean the profitability of the banks are higher however. For those reasons, the ROE and NIM are considered to be a less appropriate overall indicator of Chinese banks’ profitability. Although ROA also does not accounting for the off-balance-sheet operations profits (Ayadi & Pujals, 2005; Delis et al., 2006), it does not undermine the explanatory power of ROA become the overall profitability measurement for CCBI, since the off-balance-sheet operations of Chinese commercial banks are relatively small and limited within only few business categories.

5.2.3. Non-Performing Loans

Bank non-performing loans (NPLs) to total gross loans ratio is the value of nonperforming loans divided by the total value of the loan portfolio. The link between the Non-Performing Loans and loss of banks, is regarded a fact in literature of banking. Increases in the non-performing loans ratio are the main reason of reduction in earnings and rise in riskiness of banks. Hence, the non-performing loans ratio can be a very important indicator for both banking risks and profitability.

5.2.4. Measurement of bank ownership concentration

HHI is the Herfindahl index (HHI), which is based on the ownership held by the ten largest shareholders of the bank. The HHI equals the sum of the squared ownership shares of the ten largest shareholders of the bank. The higher is the value of the Herfindahl index, the more concentrated is the ownership of the bank.

5.2.5. Bank size

It is widely accepted that higher levels of capital decrease bank risk, and the “too big to fail” logic is most aware by the people. However, recent studies find some evidences of a negative and significant relationship between the bank size and stability. Dam & Koetter (2012) find evidence that safety nets such as the bailout system in the banking industry may lead to moral hazard and additional risk taking. Furthermore, the development model of the Chinese commercial banks is usually combined with risky assets and rapid loan expand. However, Borio & Lowe (2002) find that higher amount of risky assets is always expected to be associated with higher bank failure and too rapid expansion of loan portfolio can lead to more riskiness of the banks. This situation raised a lot of concerns in the CCBI, and the recent financial reform is dealing with this issue. Another criticism against the CCBI (mostly against the biggest four banks) is that they are inefficient in many aspects. When a bank grows too big, it may become unorganized and shorthanded which usually lead to inefficient and increase the risks especially the operation risks faced by the bank (Bos et al., 2013). In this paper the total asset is used to measure the bank size, and it will be represented as the mean of the natural logarithm of total assets.

5.2.6. Capital adequacy ratio (CAR)

Capital adequacy ratio is also called "Capital to Risk Weighted Assets Ratio (CRAR)." It is the ratio of a bank's capital to its risk-weighted assets, which can be expressed as:

CAR= (Tier 1 capital + Tier 2 capital) / (Risk weighted assets)

systems. Two types of capital are measured in the CAR: tier one capital, which can absorb losses without a bank being required to cease trading; and tier two capital, which can absorb losses in the event of a winding-up and so provides a lesser degree of protection to depositors6.

Since a high CAR means that a bank's large amount of money is occupied in provisions or risk management, so there will be less money left for investment or for the continuation of the activity. In August 2011 new capital adequacy rules from the China Banking Regulatory Commission (CBRC) require Chinese banks to shore up additional capital to protect against risks. The previous regulation requires the Chinese commercial banks keep a minimum CAR of 8%, which is in line with the Basel rule. Under the new rules, systemically important banks in China will be required to a minimum CAR of 11.5%; other banking institutions will be required to adhere to a minimum CAR of 10.5%. The new rule is a reaction to some serious potential risks faced by the CCBI, which mainly including loans related to real estate, local government bonds, and off-balance sheet credit business. In order to guard against risks and create a buffer zone under the new rules, most of the banks enhanced their capital and decreased the financial leverage, which made a great difference regarding to their riskiness and profitability.

5.2.7. Loan to deposit ratio

The loan to deposit ratio (or LTD) is the ratio between a bank’s total loans and total deposits. It essentially indicates two main things. It has been used by the Chinese central bank as a liquidity indicator of the commercial banks. If a bank has an LTD of more than one, it indicates that the bank lends more than it deposits and may have a liquidity problem when in distress. The loan to deposit ratio also indicates bank’s is earning level on its loans. Base on the LTD ratio, a bank should make reasonable decision about the growth of its loan business. Typically, in consideration

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of the capital requirements, banks may want the total loans to total deposits ratio to be in the range of 75% to 90%.

5.2.8. Dummy variable

Besides the proportion of bank shares hold by each type of shareholders, the dummy variables will be employed as an alternative to identify the nature of the absolute owner of a bank. GCO is a dummy variable to indicate the bank is in absolute control of the government. If the government holds more than 50% of the bank’s total share, the GCO is then set to be 1; otherwise, it will be 0. SOECO is a dummy variable to indicate the bank is in absolute control of state-owned enterprises. SOECO equals 1 if the state-owned enterprises hold more than 50% of the bank’s total share, it will be 0otherwise. FINCO is a dummy variable to indicate the bank is in absolute control of financial institutions. FINCO equals 1 if the financial institutions hold more than 50% of the bank’s total share, it will be 0 otherwise.

5.3. Descriptive statistics

Table2. Summary sample statistics for the variables

Variable Mean Median Maximum Minimum Std. Dev.

Z-score 39.443 34.436 92.189 4.247 20.455 Ln(Z-score) 3.534 3.539 4.524 1.446 0.557 ROA 1.091 1.110 1.750 0.15 0.275 Total Asset 3681743 1775031 20609953 42429.3 4573498 Ln(TA) 14.359 14.389 16.841 10.656 1.354 HHI 0.24 0.159 0.562 0.044 0.168 CAR 12.387 12.000 30.140 3.710 3.494 NPLs ratio 1.296 0.96 7.99 0.34 1.068 Loan growth 20.52 17.78 66.82 5.06 9.154 Loan/deposit 70.424 71.880 85.160 47.430 6.845 CR 0.530 0.413 0.972 0.159 0.257 GCO 0.207 0 1 0 0.407 SOECO 0.074 0 1 0 0.263 FINCO 0.030 0 1 0 0.170 Govern 21.476 10.8 70.880 0 25.549 SOE 14.210 5.300 67.200 0 17.865 PE 12.810 12.410 36.290 0.000 11.250 Fin 17.979 17.080 64.510 0.000 12.531

Note: all variables are defined in the Appendix.

commercial banks are around 75% on average, which suggests the LTD ratio of CCBI is currently in a low level when compared the ratio with US commercial banks.

Regarding the ownership variables, the average number of the Government controlled banks (GCO), state-owned enterprises controlled banks (SOECO), and financial institution controlled banks (FINCO) are 0.207, 0.074, and 0.03 respectively. This means that the government has absolutely control of about 20.7% of the observations in the sample, 7.4% of the observations are under absolute control of state-owned enterprises, and 3% of the observations are under absolute control of financial institutions. The mean and median of the Herfindahl index (HHI) is 0.24 and 0.159 respectively, indicating a moderate ownership concentration in CCBI.

6. Regression results and analysis

Table 3 reports the results of the regressions. Since all the banks have certain specific characteristics, and each year may have unique events that affect the banks in an observable or unobservable ways, so it is recommended to controlling for unobserved heterogeneity when the individual specific effects are correlated with the independent variables. Therefore, all the regressions are estimated using fixed cross-section and fixed period panel techniques. Robust standard errors are used to correct potential heteroskedasticity and autocorrelation within the time series and the banks.

positively significant which means banks with higher CAR are less risky. This result is much in line with the previews literature.

To test the impact of ownership concentration on banks’ profitability, one can use the regression equation 2 with return on assets ratio (ROA) as the dependent variable (see Regression 2). As the result shows in table, the coefficient of HHI is still insignificant. Thus, ownership concentration does not have a significant effect on the bank’s profitability measured by ROA. The coefficient of bank size (Ln(TA)) is negatively significant indicating that although Chinese commercial banks are growing fast, banks with large asset are becoming less profitable. The main cause for this situation may be lack of certain improvement in terms of organization and management. These problems can lead to inefficiency which increases the operational costs and reduce the banks’ profits.

The result shows that total loans to total deposits ratio (LTD) is negatively significant in this case. Thus, high loan to deposit ratio increases riskiness for the Chinese commercial banks and does not lead to higher profits. As it is showed earlier, the average loans to deposits ratio of the sample is lower than the same ratio of US commercial banks. Therefore, there is still room for the Chinese commercial banks to expand their loan business. However, rapid loan expanding without responding countermeasure management (such as raise capital reserve and decrease financial leverage) could pose serious potential risks to the bank. Therefore, it takes great caution to make the trade-off between the control of risk-taking level and earning an optimal return.

to have been through a non-performing loan wave. In March 2004, the official sector-wide non-performing loans ratio of the CCBI is 16.6%. The non-performing loans ratios of state-owned commercial banks and joint-stock commercial banks are 19.2% and 7.1%, respectively. However, there are researchers believe the official non-performing loans ratio of the CCBI is still lower than the actual ratio back then. Whalley (2003) makes an unofficial estimation about the non-performing loans ratio of CCBI whose result suggests that the non-performing loans ratio could be 50% or even higher. During the first non-performing loan wave, the Chinese government had to set up state-run asset management companies (AMCs) to deal with these non-performing loan problems specifically. The four biggest state-owned commercial banks transferred huge amounts of non-performing loans to four newly created AMCs (Each of the four state-owned commercial banks had a corresponding AMC). The Chinese commercial banks sell their non-performing loans to the AMCs, the AMCs then make certain debt restructuring, and sell the non-performing loans in the capital market again.

very important to add the non-performing loan ratio as a control variable.

By using the regression equation 3, we can examine the impact of different shareholder nature on the risk-taking behavior of the banks (see Regression 3). As the result shows in table 3, one can notice that the coefficient of state-owned enterprises shareholding (SOE) is negatively significant, while coefficient of financial institution shareholding (FIN) is positively significant. Therefore, the different natures of the shareholders can affect the riskiness of the Chinese commercial banks. Particularly, banks controlled by the state-owned enterprises are more risky, and banks controlled by financial institutions are less risky. The coefficients of the control variables are basically in line with the results in Regression 1.

Table 3. Primary regression results

Regression 1 Regression 2 Regression 3 Regression 4

Dependent

variable Ln(Z) ROA Ln(Z) ROA

Constant 8.3071*** (3.4695) 7.6503*** (4.5404) 8.4041*** (3.9768) 6.9010*** (3.9432) Ln (TA) -0.3132** (-2.1785) -0.4376*** (-4.2220) -0.3332*** (-2.6907) -0.4089*** (-3.7268) CAR 0.0396*** (2.9394) 0.0324*** (4.7874) 0.0389*** (2.9435) 0.0300*** (4.8704) NPLs 0.0361 (1.4661) 0.0400 (1.4220) 0.0183 (0.9482) 0.0453 (1.5648) LTD -0.0127*** (-2.6323) -0.0109*** (-2.652) -0.0094** (-2.0965) -0.0097** (-2.4544) HHI 0.3226 (1.1104) 0.1606 (0.4057) Govern 0.0061 (1.5985) 0.0031 (1.1361) SOE -0.0114** (-2.3609) 0.0012 (0.3888) PE -0.0006 (-0.1233) 0.0093** (2.0074) Fin 0.0062* (1.8556) 0.0064* (1.8667) Adjusted R2 0.9083 0.7798 0.9316 0.7901 F-Statistic 50.1424 18.5751 61.8129 17.8167

Notes: This table reports the results from fixed cross-section and fixed period panel techniques. Numbers in parentheses are t-statistics, robust standard errors is used in the regression. Total number of observation is 135. *Indicates estimations that are significant at 10% level. **Indicates estimations that are significant at 5% level. ***Indicates estimations that are significant at 1% level.

7. Effect consistency & robustness checks

7.1. Effect consistency

efforts to increase the domestic investments and consumptions to stimulate the economy. Under such circumstances, the Chinese commercial banks also changed some of their operation strategies because they were encouraged to provide credit supports to local government projects, enterprise investments, and individual loans (mortgages). Therefore, it is expected that the effects of ownership structure on riskiness and profitability of the Chinese commercial banking industry may vary during different time periods between 2006 and 2014.

In order to investigate the consistency of the effects detected in previews section, the original sample will be divided into three subsamples based on three different time periods. The years from 2006 to 2008 is the period before the financial crisis; from 2009 to 2011 is the time period during the financial crisis; and from 2012 to 2014 is the post financial crisis period7.

By using regression equation 1 and 2, the impact of ownership concentration on the banks’ riskiness and profitability can be identified for each subsample respectively. The results are presented in table 4.

From the results, one can notice that the coefficients of ownership concentration (HHI) are insignificant, except in Regression 6 (which is positively significant). This suggests that ownership concentration has no effect on Chinese commercial banks’ profitability. However, banks with higher ownership concentration are less risky between 2009 and 2011 (during the financial crisis). Ownership concentration does not affect banks’ riskiness before and after the financial crisis.

7

Table 4. Consistency tests for effects of ownership concentration on riskiness and profitability of Chinese commercial banks

Regression 5 Regression 6 Regression 7 Regression 8 Regression 9 Regression 10 Dependent variable 2006-2008 2009-2011 2012-2014 2006-2008 2009-2011 2012-2014

Ln(Z) Ln(Z) Ln(Z) ROA ROA ROA

Constant 6.1453 (1.4663) 13.0830*** (4.0074) 8.2765* (1.8903) 6.1898** (2.5876) 11.5274*** (4.0504) 6.4718 (0.8742) Ln (TA) -0.3123 (-0.9489) -0.6124*** (-2.9593) -0.3346 (-1.2008) -0.2553 (-1.5763) -0.6808*** (-3.6097) -0.3285 (-0.7105) CAR 0.0481 (1.4058) 0.0286 (1.5864) 0.0396*** (3.0939) 0.0152 (0.9953) 0.0158 (1.1244) -0.0175 (-0.4346) NPLs 0.0389 (0.9516) -0.0947 (-0.5898) 0.0319 (0.4957) 0.0971 (2.2636) -0.02494 (-0.1817) -0.3050* (-1.9372) LTD 0.0131 (0.6902) -0.0184** (-2.5972) -0.0005 (-0.1411) -0.0313** (-2.4931) -0.01077** (-2.1456) 0.0060 (1.0448) HHI -0.1796 (-0.2195) 1.3807* (1.9322) -0.4618 (-0.8425) 0.1737 (0.9627) -0.0508 (-0.1546) -1.1997 (-1.6683) Adjusted R2 0.9090 0.9613 0.9941 0.8840 0.8824 0.7971 F-Statistic 21.9357 53.0500 353.8540 16.9706 16.7286 9.2314

Note: This table reports the results from fixed cross-section and fixed period panel techniques. Numbers in parentheses are t-statistics. Robust standard errors are used in the regression. Each subsample regression contains 45 observations. Regression 5, Regression 6 and Regression 7 examine the effects of ownership concentration on riskiness of Chinese commercial banks during 2006-2008, 2009-2011, and 20112-2014 time periods respectively. Regression 8, Regression 9 and Regression 10 examine the effects of ownership concentration on profitability of Chinese commercial banks during 2006-2008, 2009-2011, and 20112-2014 time periods respectively. *Indicates estimations that are significant at 10% level. **Indicates estimations that are significant at 5% level. ***Indicates estimations that are significant at 1% level.

In terms of riskiness, table 5 shows that the coefficients of government shareholding proportion (Govern) are negatively significant during 2009 to 2011 (see Regression 12), which means that banks state-owned banks are more risky during the financial crisis. Considering the fact that state-owned banks have the responsibility to share the social and political objectives of the government, it is more likely that state-owned banks have to finance risky government projects and private investments that no other banks would have done. Therefore, the requirement to fulfill the goals of stimulating economy under severely depressed situations makes the state-owned banks takes more risks during the financial crisis. Regarding to the profitability, the coefficient of government shareholding proportion (Govern) is negatively significant during 2012 to 2014 (see Regression 16), which indicating that state-owned banks are less profitable in the post financial crisis time period. It is believed that the low profitability of state-owned banks after the financial crisis is caused by the large amount of cumulated non-performing loans during the financial crisis.

Table 5. Consistency tests for effects of different shareholder natures on riskiness and profitability of Chinese commercial banks

Regression 11 Regression 12 Regression 13 Regression 14 Regression 15 Regression 16 Dependent variable 2006-2008 Ln(Z) 2009-2011 Ln(Z) 2012-2014 Ln(Z) 2006-2008 ROA 2009-2011 ROA 2012-2014 ROA Constant 7.6939* (1.3200) 13.7608*** (3.7583) 10.3252** (2.7445) 3.4043 (1.174536) 12.0206*** (4.0210) 12.7611* (1.9955) Ln (TA) -0.3403 (-0.9161) -0.7307*** (-2.9176) -0.4791* (-2.0115) -0.1449 (-0.8745) -0.7188*** (-3.5333) -0.6058 (-1.4783) CAR 0.0437 (1.1706) 0.0241 (1.1730) 0.0176 (1.4548) 0.0202 (1.3541) 0.0132 (0.7994) -0.0760*** (-3.5926) NPLs 0.0187 (0.4213) 0.0813 (0.6039) -0.0397 (-0.5239) 0.1152 (3.1417) -0.0078 (-0.0511) -0.2801* (-1.9648) LTD 0.0022 (0.1220) -0.0036 (-0.6201) 0.0017 (0.4465) -0.0223 (-1.6653) -0.0088 (-1.1833) 0.0038 (0.6669) Govern 0.0014 (0.3680) -0.0235*** (-4.8298) -0.0086 (-1.4467) 0.0022 (1.0259) -0.0008 (-1.3838) -0.0498*** (-5.3502) SOE -0.0135*** (-3.4077) 0.0174*** (3.5406) 0.0040 (0.6859) 0.0028 (0.9856) 0.0042 (1.1009) -0.0010 (-0.1095) PE 0.0110 (0.5589) 0.0107 (1.5975) 0.0089* (1.9955) 0.0459*** (3.4241) 0.0003 (0.0410) -0.0236*** (-2.9278) Fin -0.0155 (-0.9455) 0.0199*** (6.6387) 0.0088** (2.2157) 0.0011 (0.1648) 0.0019 (0.5272) -0.0088 (-1.1770) Adjusted R2 0.9352 0.9818 0.9961 0.9018 0.8729 0.8842 F-Statistic 27.47136 100.0815 470.4542 17.8441 13.5955 14.9949

7.2. Robustness checks

Based on the regression equation 5 and 6, the robustness check will be performed by using the ownership concentration ratio (CR) as alternative measurement besides the Herfindahl index (HHI) to check the effect of the ownership concentration on the banks’ riskiness and profitability respectively (denoted as Regression 17 and Regression 18). The ownership concentration ratio is the sum of (not reweighted) percentage of shares owned by the top three shareholders of a bank.

LN(Z)i,t = α + ϐ1 CRi,t+ ϐ2 Ln(TA)i,t + ϐ3 CARi,t + ϐ4 NPLi,t + ϐ5 LTDi,t+

μ

τ

ε

ROAi,t = α + ϐ1 CRi,t+ ϐ2 Ln(TA)i,t + ϐ3 CARi,t + ϐ4 NPLi,t + ϐ5 LTDi,t+

μ

τ

ε

By using the dummy variables, regression equation 7 and 8 test the effect of different shareholder natures on the banks’ riskiness and profitability respectively (denoted as Regression 19 and Regression 20). GCO, SOECO and FINCO in the equations are dummy variables to identify the dominate shareholder (Holding more than 50% of the bank’s shares) of a bank. Due to the fact that Chinese private enterprises rarely have the interest nor the capability to hold more than 50% of the bank’s shares, thus, the situation that private enterprise as the dominate shareholder is not be investigated in this paper.

LN(Z)i,t = α + ϐ1GCOi,t + ϐ2SOECOi,t + ϐ3FINCOi,t + ϐ4Ln(TA)i,t + ϐ5NPLsi,t + ϐ6CARi,t +

ϐ7FUNDi,t +

μ

τ

ε

ROAi,t = α + ϐ1GCOi,t + ϐ2SOECOi,t + ϐ3FINCOi,t + ϐ4Ln(TA)i,t + ϐ5NPLsi,t + ϐ6CARi,t +

The regression results presented in table 6 shows that the coefficients of ownership concentration ratio (CR) are insignificant in both Regression 17 and Regression 18. Therefore, the ownership concentration has no effect on riskiness and profitability of the banks.

Table 6. Robustness checks with alternative variable measurements

Regression 17 Regression 18 Regression 19 Regression 20

Dependent

variable LNZ ROA LNZ ROA

Constant 7.8468*** (3.2691) 7.4062*** (4.7428) 8.9810*** (3.9945) 7.4816*** (3.8975) Ln (TA) -0.2959** (-2.0861) -0.4296*** (-4.5517) -0.3521*** (-2.6338) -0.4260*** (-3.5803) CAR 0.0340** (2.4913) 0.0290*** (4.3085) 0.0358** (2.5226) 0.03290*** (5.0297) NPLs 0.0329 (1.4047) 0.0386 (1.5696) 0.0277 (1.4010) 0.0362 (1.1761) LTD -0.0125** (-2.5836) -0.0108*** (-2.7123) -0.0112** (-2.5612) -0.0097** (-2.4357) CR 0.6637 (1.4591) 0.3895 (1.3126) GCO -0.0967 (-1.2218) -0.1249** (-2.0798) SOECO -1.0098*** (-10.5362) -0.2584*** (-3.9915) FINCO 0.3307*** (3.3724) 0.0677 (0.3620) Adjusted R2 0.9140 0.7882 0.9361 0.7851 F-Statistic 53.7287 19.4674 68.6683 17.8839

Notes: This table reports the robustness tests results from fixed cross-section and fixed period panel techniques. Numbers in parentheses are t-statistics. Robust standard errors are used in the regression. Sample contains 135 observations in total.

*Indicates estimations that are significant at 10% level. **Indicates estimations that are significant at 5% level. ***Indicates estimations that are significant at 1% level.

8. Limitations of the study

selection bias, and the results showed in this paper may be less representative when considering the non-listed banks. Third, since the development of Chinese economy is not consistent among different regions, banks with more operations in less developed regions will always have to take more risks. Therefore, endogeneity bias and omitted variables maybe still exist in the regressions which require better treatment to account for them. This paper acknowledges these limitations and further research may be needed to properly address them.

9. Conclusion

The reform in the commercial banking sector in China takes a lot effects on the ownership structure in order to improve the stability and profitability, as well as reduce the riskiness of Chinese banks. This paper examines how ownership structure influences the risk-taking behavior and profitability of Chinese commercial banks through its two dimensions (ownership concentration and unique natures of different shareholders). Based on the type of controlling shareholder, the major Chinese commercial banks are classified into state-owned commercial banks, state-owned enterprises controlled banks, private enterprises controlled commercial banks, and financial institutions controlled banks

.

In terms of the profitability of the banks, this paper finds some evidences that banks controlled by the private enterprises and financial institutions are more profitable. The government as an ordinary shareholder does not necessarily affect the bank’s profitability. Only when the government is the dominate shareholder of a bank, could the bank’s profitability be undermined. Having the state-owned enterprises as the dominate shareholder can damage the banks’ profitability.

Reference

Aghion P, Tirole J. (1997). Formal and real authority in organizations. Journal of political economy, 55, 1-29.

Alexander, P. (2014). Top 1000 World Banks 2014: Back on track? The Banker magazine.

Angkinand, A., &Wihlborg, C. (2010). Deposit insurance coverage, ownership, and banks' risk-taking in emerging markets. Journal of International Money and Finance, 29(2), 252–274.

Athanasoglou, P., Delis, M., & Staikouras, C. (2006). Determinants of bank profitability in the South Eastern European region. Working paper Series.

Ayadi, R., & Pujals, G. (2005). Banking mergers and acquisitions in the EU: Overview, assessment and prospects[M]. SUERF.

Barry, T. A., Lepetit, L., & Tarazi, A. (2011). Ownership structure and risk in publicly held and privately owned banks. Journal of Banking & Finance, 35, 1327–1340. Basel Committee on Banking Supervision (2010). Principles for enhancing sound corporate governance. Basel: BIS.

Beck, T., Demirgüç-Kunt, A., & Levine, R. (2009). Financial institutions and markets across countries and over time-data and analysis. World Bank Policy Research Working Paper Series.

Berger, A. N., Clarke, G. R. G., Cull, R., Klapper, L., & Udell, G. F. (2005). Corporate governance and bank performance: A joint analysis of the static, selection, and dynamic effects of domestic, foreign, and state ownership. Journal of Banking & Finance, 29, 2179–2221.

Bhattacharyya, A., Lovell, C. A. K., & Sahay, P. (1997). The impact of liberalisation on the productive efficiency of Indian commercial banks. European Journal of Operational Research, 98(2), 332–345.

Bonin, J. P., Hasan, I., &Wachtel, P. (2005). Bank performance, efficiency and ownership in transition countries. Journal of Banking & Finance, 29, 31–53.

Borio, C. E. V., & Lowe, P. W. (2002). Asset prices, financial and monetary stability: exploring the nexus.

Evidence from financial services. Journal of Banking & Finance, 37(5), 1590-1601. Boyd, J. H., & Graham, S. L. (1986). Risk, regulation, and bank holding company expansion into nonbanking. Federal Reserve Bank of Minneapolis Quarterly Review, 10(2), 2–17.

Burkart, M., Gromb, D., & Panunzi, F. (1997). Large shareholders, monitoring and the value of the firm. Quarterly Journal of Economics, 112, 693-728.

Chen, G., Firth, M., & Xu, L. (2009). Does the type of ownership control matter? Evidence from China's listed companies. Journal of Banking & Finance, 33(1), 171– 181.

China Banking Regulatory Commission (CBRC) (2003). CBRC annual report. China Banking Regulatory Commission (CBRC) (2011). CBRC annual report. China Banking Regulatory Commission (CBRC) (2015). CBRC quarterly report.

Clarke, G.R.G., Cull, R., & Shirley, M.M. (2005). Bank privatization in developing countries: A summary of lessons and findings. Journal of Banking & Finance, 29, 1905–1930.

Dam, L., & Koetter, M. (2012). Bank bailouts and moral hazard: Evidence from Germany[J]. Review of Financial Studies, 25(8): 2343-2380.

Delios, A., Wu, Z. J., & Zhou, N. (2006). A new perspective on ownership identities in China's listed companies. Management and Organization Review, 2(3), 319–343. Demsetz, H., & Villalonga, B. (2001). Ownership structure and corporate performance. Journal of Corporate Finance, 7, 209–233.

Dong, Y., Meng, C., Firth, M., & Hou, W. (2014). Ownership structure and risk-taking: Comparative evidence from private and state-controlled banks in China. International Review of Financial Analysis, 36, 120-130.

Edwards, J., & Nibler, M. (2000). Corporate governance in Germany: The role of banks and ownership concentration. Economic Policy, 15(31), 237–267.

Faccio, M., Masulis, R. M., & McConnell, J. J. (2006). Political connections and corporate bailouts. Journal of Finance, 61(6), 2597–2635.

banks in 15 post-communist countries. Journal of Banking & Finance, 29(1), 55–81. Fu, X., & Heffernan, S. (2007). Cost X-efficiency in China's banking sector. China Economic Review, 18(1), 35–53.

Fu, X., & Heffernan, S. (2010). Determinants of financial performance in Chinese banking. Applied Financial Economics, 20, 1585–1600.

Fungáčová Z, Solanko L, & Weill L. (2010). Market power in the Russian banking industry. International Economics, 124, 127-145.

Garcia-Herrero, A., Gavila, S., & Santabarbara, D. (2009). What explains the lowprofitability of Chinese banks? Journal of Banking & Finance, 33, 2080–2092. Garcia-Marco, T., & Robles-Fernández, M. D. (2008). Risk-taking behaviour and ownership in the banking industry: The Spanish evidence. Journal of Economics and Business, 60(4), 332–354.

Iannotta, G., Nocera, G., & Sironi, A. (2007). Ownership structure, risk and performance in the European banking industry. Journal of Banking & Finance, 31, 2127–2149.

Iannotta, G., Nocera, G., & Sironi, A. (2013). The impact of government ownership on bank risk. Journal of Financial Intermediation, 22(2), 162–176.

Isik, I., & Hassan, M. K. (2002). Cost and profit efficiency of the Turkish banking industry: An empirical investigation. The Financial Review, 37(2), 257–279.

Jia, C. (2009). The effect of ownership on the prudential behavior of banks — the case of China. Journal of Banking & Finance, 33(1), 77–87.

John, K., Litov, L., & Yeung, B. (2008). Corporate governance and risk-taking. Journal of Finance, 63, 1679–1728.

Laeven, L., & Levine, R. (2009). Bank governance, regulation, and risk taking. Journal of Financial Economics, 93, 259–275.

Lepetit, L. & Strobel, F. (2013). Bank insolvency risk and time-varying Z-score measures. Journal of International Financial Markets, Institutions and Money, 25, 73-87.

Okazaki, K. (2007). Banking system reform in China: The challenges of moving toward a market-oriented economy. RAND Corporation.

Shehzad, C. T., de Haan, J., & Scholtens, B. (2010). The impact of bank ownership concentration on impaired loans and capital adequacy. Journal of Banking & Finance, 34(2), 399–408.

Sheshinski, E., & Lopez-Calva, L. F. (2003). Privatisation and its benefits: Theory and evidence. CESinfo Economic Studies, 49(3), 429–459.

Shirley, M., & Nellis, J. (1991). Public enterprise reform: The lessons of experience. World Bank discussion papers, no. WDP 119.

Shleifer, A. (1998). State versus private ownership. Journal of Economic Perspectives, 12(4), 133–150.

Shleifer, A., & Vishny, R. W. (1986). Large shareholders and corporate control. Journal of Political Economy, 94, 461–488.

Shleifer, A., & Vishny, R. W. (1994). Politicians and firms. Quarterly Journal of Economics, 109(4), 995–1025.

Shleifer, A., & Vishny, R. W. (1997). A survey of corporate governance. Journal of Finance, 52, 737–783.

Whalley, J. (2003), Liberalization in China's key service sectors following WTO accession: some scenarios and issues of measurement. National Bureau of Economic Research, No. w10143.

http://data.worldbank.org/country/armenia Bankscope database.

http://www.federalreserve.gov/

Appendix

Variable Definition

Z-score Measurement of riskiness of a bank Ln(Z-score) Natural logarithm of the Z-score

ROA Return on assets ratio, measurement of profitability of a bank Total Asset Measurement of bank size

Ln(TA) Natural logarithm of the bank’s total asset

HHI Herfindahl index based on the ownership held by the ten largest shareholders of the bank. Measurement of ownership concentration CAR Risk-weighted capital adequacy ratio

NPLs ratio Total non-performing loans divided by the gross loans Loan/deposit Ratio of total loans to total funding

CR The sum of percentage of shares owned by the top three shareholders GCO A dummy variable equal to 1 if the government holds more than 50% of

the bank’s shares and 0 otherwise.

SOECO A dummy variable equal to 1 if state-owned enterprises hold more than 50% of the bank’s shares and 0 otherwise.

FINCO A dummy variable equal to 1 if financial institutions hold more than 50% of the bank’s shares and 0 otherwise.

Govern The percentage of a bank’s shares holding by the government