The Degree of Bank Risks in the Chinese Banking Industry

The Degree of Bank Risks in the Chinese Banking Industry
Approach
This section employs data from five banks to estimate the degree of bank risks in the Chinese banking industry. The risks studied include bank-specific risks, market risks, and macro-economic risks such as inflation and growth in national GDPs. The common types of bank-specific risks in the banking sector include credit risks, capital risks, liquidity risks, and insolvency risks. The present analysis utilizes accounting ratios to measure credit risks and other bank risks in the Chinese banking sector. For instance, the ratio of non-performing loans (NPL) to total loans provides an estimate of credit risks of banks, such that the higher the ratio, the higher the credit risk. Secondly, the ratio of liquid assets to total assets estimates the liquidity risk of a bank and the higher this ratio, the lower the liquidity risk. The third risk that accounting ratios estimate is the total regulatory capital ratio, where lower capital risk is associated with higher total regulatory capital ratio. While Z-scores and accounting ratios have traditionally been used in measuring the three types of risks: credit, liquidity, and capital risks, Tan (2018) note that they fail to reflect the potential banks’ stabilities. Instead, an alternative of using translog specification offers accurate and robust estimates of stability inefficiency of banks. This approach is used to determine the impact or degree of risks that competition and other market-specific risks may have on banks’ performance and profitability. However, as Tan (2018) notes, the China Banking Regulatory Commission (CBRC) has since 2003 reduced the degree of risks such as credit risks, non-performing loan ratios, and capital risks. Despite these observations, market and credit risks continue to impact on performance of banks as demonstrated in several studies (Tan et al, 2017). The present paper analyzes the degree of risks that credit risks, market risks, and other factors have on commercial banks in China using Z-scores in different periods between 2017 and 2020. In measuring market risks such as competition, the Boone indicator, a method proposed by Boone (2008) states that the competition improves the performance of efficient firms while weakening the inefficient firms. In general, the determinants of bank profitability, which are potential bank risks, are grouped into three categories, including bank-specific, industry-specific, and macro-economic variables. Bank-specific variables include credit risk, capital risk, liquidity risk, bank size, insolvency risks, bank diversification, and overhead cost. Industry-specific variables include the stock-market development, competition in the market, and the development of the banking sector in general. Macro-economic variables such as inflation and GDP are beyond a bank’s control and differ across different countries.
In studying the impact of bank risks, it is assumed that the values of the studied variables remain constant regardless of time differences. This stationarity, a property of time series, does not account for the variation of time as a factor affecting the value of a variable. This approach aids in the construction of time-varying z-score measures. Studies such as Li, Tripe, and Malone (2017) have used rolling mean and standard deviation or the different between the minimum and maximum ROA to measure Australian bank risks through z-scores. While working on the Z-scores, Li, Tripe and Malone combine the ROA measures with current values of equity-to-asset ratios. To understand the impact of studied variables on credit risks of China’s commercial banks, it is important to ensure that data from dependent variables, including ROA, ROE, and NPL is stationary, otherwise, results obtained will be biased based on time differences. This approach of considering the time series is known as the de-trending data and avoidance of seasonality. Factors such as inflation and Gross domestic product (GDP) are beyond the banks’ control unlike capital adequacy ratio (CAR), liquidity coverage ratio (LCR), NPL ratio, net stable funding ratio (NSFR), total assets, total equity, return on assets (ROA), and return on equity (ROE). Macro-economic determinants of economic growth such as inflation and GDP significantly affect the performance and profitability of commercial banks (Yong & Christos, 2012). This analysis examines inflation and GDP as macro-economic variables in addition to bank-specific factors that determine the bank risks of China’s commercial banks.
Based on the available data, the study will analyze specific variables that influence banks’ credit risks including total assets, total equity, return on assets (ROA), and return on equity (ROE) using the Z-score estimator. Total assets and total equity of banks, affect the equity to assets ratio (EAR) and is determined by the amount of NPL banks have in total assets. These variables also affect CAR, which is a measure of the available capital expressed as a percentage of credit risk exposure of commercial banks. Both ROA and ROE are important credit management indicators (Abubakar, Shaba, Ezeji, & Ahmad, 2016). ROA is the efficiency measure to determine how well a bank utilizes scarce resources in generating profits. Based on ROA measures, higher ratios indicate better performance of banks and lower risks, while lower ratios are an indication of higher risks due to poor performance (Isanzu, 2017). ROE is the ratio of a bank’s net income to the equity of shareholders during a given year and it measures profitability of shareholders’ investments. ROA and ROE are proxies to measure financial performance and profitability while factors such as NPLs indicate credit risks of banks (Ekinci & Poyraz, 2019). The natural logarithm of Z-score is used to measure the banks’ insolvency risks. In this measure, a higher Z-score denotes banks with lower risks of insolvency while lower Z-scores are common among banks with poor credit management (Lepetit & Strobel, 2015). The results section presents the Z-score analysis using data obtained from annual reports of the five commercial banks in China to determine the degree of each risk studied.
Z-scores for Risk Measures
This analysis considers the Z-score for five commercial banks in China computed individually to indicate the risk-taking of the banks. Z-score is calculated by adding ROA and equity-to-asset ratio divided by the ROA’s standard deviation. This can be expressed mathematically using the equation below. Generally, ROA is expressed as the net profit after taxes divided by the average total of all assets. To associate bank’s capitalization with the ROA and risk, only shareholders’ equity is used while excluding subordinated debt. This ensures that the value of standard deviation of asset returns has to decline before bank’s insolvency. Through this approach, the Z-score will represent the gap between the bank’s current status and insolvency. Thus, the higher the Z-score value, the greater the stability of the bank. Constructing time-varying Z-scores, the use of rolling windows is meaningful as the lending patterns of banks and risk profiles are bound to change with time. This approach of using rolling window is effective in capturing bank risks. Besides, since the banks’ ROA may present some lagged effects on performance, mean value of ROA, either mean value or rolling mean of the available data, is used to compute the Z-score. Based on moments of the ROA distribution, it is recommended to estimate both the mean and standard deviation of ROA as time varying for different periods of observations.
Z-score=ROA- (Equity/Assetσ(ROA)
Measuring market risk using Z-scores requires the construction of an aggregate Z-score and minus-one or leave-one-out (LOO) bank Z-score. Loo Z-score has been used by Li et al (2020) in estimating systemic risks of financial institutions to predict their weaknesses. Aggregate Z-score can then be derived by aggregating data from the five banks in this study. Minus one bank Z-score refers to the aggregate derived minus one bank Z-score. In the present study, market risk potential is measured using joint-risk taking of the five banks and the aggregate Z-score is computed using consolidated accounting data sourced from the individual banks. This aggregate can then be used as a proxy for the potential market risk. It is important to note that minus one bank Z-score is derived from the concept of leave-one-out methodology and the different between one banking system’s performance and the general system’s performance while excluding one bank reflects the impact of the bank to market risk. While applying the leave-one-out approach, minus one bank Z-score is calculated by leaving out or excluding one entity/bank at a time from the analysis. Therefore, minus one bank Z-scores are generally lower compared to the aggregate Z-scores, which is denotes higher risks. Z-scores may also be decomposed in two different approaches into additive components. The first approach involves the separation of Z-scores into equity-to-asset ratio, ROA, and the standard deviation of ROA. The other approach is to divide the Z-score into two individual components: the leverage and the ROA parts. The leverage part takes the equity-to-asset ratio divided by the standard deviation of ROA, which the ROA part takes the ROA divided by the standard deviation of ROA. ROA measures the risk of a bank’s portfolio since it takes into account both the volatility of returns and the level of returns. Leverage part of the Z-score indicates the coverage capacity of bank capital for a defined risk level and will therefore measure the leverage risk of a bank. The two components effectively measure the insolvency risk of a bank.
Analysis
Sample data
Data from the five Chinese banks collected between 2017 and 2021 will be analyzed. The Chinese banking sector is dominated by state-owned commercial banks (SOBS) although other joint-stock commercial banks (JSCBs) and City Commercial Banks (CCBs) are also key players in the industry. This analysis derives data from five different listed banks in China, including the Industrial and Commercial Bank of China Limited (ICBC), Agricultural Bank of China (ABC), Bank of China Limited (BCL), China Merchants Bank (CMB), and Bank of Communications (BoC). Founded on 1 January 1984 and restructured to a joint-stock limited company on 28 October 1984, the Industrial and Commercial Bank of China is listed on both the Stock Exchange of Hong Kong and the Shanghai Stock (ICBC, 2021). The Agricultural Bank of China is also a joint stock company fully incorporated in the People’s Republic of China with limited liability in January 2009. Like ICBC, the bank is listed on both the Hong Kong Stock Exchange and the Shanghai Stock Exchange (Agricultural Bank of China, 2021). The Bank of China has one of the longest history of operation in China and since its establishment in February 1912. The bank has served as both a specialized international trade bank and an international exchange bank (Bank of China, 2021). The China Merchants Bank was established in 1987 and has its headquarters in Shenzhen China. Serving as a commercial bank, China Merchant Bank has a distribution network in major cities in mainland China and other international financial centers in Hong Kong, Singapore, New York, London, Sydney, and Luxembourg. The bank was listed on the Shanghai Stock Exchange in April 2002 and later on the Hong Kong Stock Exchange in September 2006 (China Merchants Bank, 2021). Finally, the Bank of Communications is also one of the longest note-issuing banks in modern China and was founded in 1908 to become the first state-owned joint-stock commercial bank in the country. The bank is listed on both the Hong Kong Stock Exchange and the Shanghai Stock Exchange (Bank of Communications, 2021). The annual reports of the above five banks between 2017 and 2021 will provide data on accounting ratios that will aid in estimating the bank risks in the Chinese commercial banking sector.
Variables
Specific variables examined in this analysis are risks facing commercial banks in China, including credit, market, operational, and liquidity risks. Banks often take prudent measures to manage the risks and improve profitability while sustaining fewer losses on investments. For instance, banks can lower these risks by diversifying investments, improving operational systems, and employing effective management practices.
Credit risk
Credit risk is one of the biggest risks that banks face and often occurs when borrowers fail to honor their obligations to service loans as specified in contracts. Borrowers may, for example fail to pay the principle or interest amount on money loans. Such defaults may also occur on credit cards, mortgages, or fixed income securities. Although banks are not fully protected from such risks, they can significantly lower their credit risk exposure through diversification. This approach can lower risks during a credit downturn. Reducing credit risks demands that banks only loan individuals with outstanding credit histories of those with collaterals to serve as securities for loans. For banks, credit risks or the potential for insolvency are always measured through credit ratings, which offer an estimate that banks will default on their debts. As earlier illustrated, Z-scores mainly indicate the distance a bank is from insolvency or approaching bankruptcy. This measure will be used to determine the credit risk of the five Chinese banks in this study.
Z-score=ROA- (Equity/Assetσ(ROA)
Market risk
The second factor is the market risk, which mostly arises from banks’ activities in the capital markets. Unpredictability of commodity prices, equity markets, credit spreads, and interest rates are common causes of market risks. A bank would be more exposed to this form of risk if it were involved in capital market investments or activities such as trading and sales. Commodity prices may also play a significant role since banks may invest in commodity-producing companies. A change in commodity price results in a change in the value of investment, thus increasing the market risk. Commodity prices may be caused by a shift in demand and supply, which are in most times difficult to predict. Banks may face different types of market risks, including interest rate risks, commodity risks, currency risks, and country risks. Absolute or percentage dispersion/volatility in prices can effectively measure market risks. However, the commonly used approach is the Value at Risk (VaR) modeling, which identifies losses through statistical risk management. However, since VaR depends on assumptions, its accuracy is questionable. Interest risk rate (IRR) analysis is also used to analyze the market risk for banks as illustrated by the Federal Deposit Insurance Corporation (2022), the body that supervises and examines financial institutions for their soundness, safety, and consumer protection. The common forms of IRR analysis approaches include gap analysis, duration analysis, earnings simulation analysis, earnings-at-risk, capital-at-risk, and economic value of equity. For this present analysis,...