China’s smaller banks emerge as star performers
While China boasts many of the world’s biggest banks by Tier 1 capital, a deeper dive into performance metrics shows that it is often the country’s smaller banks that perform better. Kimberley Long reports.
The Best-performing Banks ranking for China has yielded an important insight into the state of the country’s financial sector: while Chinese institutions once again dominate The Banker’s Top 1000 World Banks list by Tier 1 capital, it is some of the country’s smaller banks, further down the table, that are showing the highest growth rates, operational efficiency and quality of assets.
Applying the methodology over a larger sample size significantly changes our perception of many a bank's status. While China Minsheng Bank was ranked as the best-performing bank in China’s top 10 banks by Tier 1 capital, a very different picture begins to emerge when the methodology is extended to cover the top 100 Chinese banks.
For example, Foshan Rural Commercial Bank took the top spot in the best-performing banks ranking for China, thanks to leading in growth, asset quality, soundness and leverage. The bank also ranked in seventh place for return on risk. This is a remarkable result for the bank from the central Guangdong Province, which comes 92nd out of the biggest 100 banks by Tier 1 capital. However, in the operational efficiency category of the best-performance ranking, the bank was close to the bottom in 95th place.
Second-placed Guangdong Nanhai Rural Commercial Bank similarly defies easy categorisation. Despite being in 51st spot out of the top 100 by Tier 1 capital, the bank ranked first for both profitability and return on risk, and fifth for asset quality. However, it showed weakness in the growth category, in 70th place, and operational efficiency, in 74th spot. And third-placed Chengdu Rural Commercial Bank displayed divergent results, such as coming sixth in profitability but 99th in growth. The bank also ranked highly for soundness, leverage and return on risk, coming in second, fourth and 10th respectively.
Overall, the rankings highlight an inconsistency among China’s banks across the different performance metrics. For example, overall fifth-placed best-performing Bank of Langfang took second and third place in the leverage and soundness categories, respectively, but a lowly 79th place for profitability.
Comparatively, Industrial and Commercial Bank of China (ICBC), which has held the top spot in the Top 1000 World Banks ranking since 2013, could only manage 20th place in overall performance. ICBC's highest ranking was 13th for return on risk, while it was way down the pack in 77th place for growth. The result puts it one place behind Tianjin Binhai Rural Commercial Bank, which placed 88th by Tier 1 capital.
China Construction Bank, the world's second-largest bank by Tier 1 capital, fared slightly worse than ICBC, ranking 31st in overall performance. Its highest rank was 11th for return on risk, and its lowest placing was 79th for growth.
China Minsheng Bank, which came in first place when the methodology was applied only to the biggest 10 Chinese banks, comes in 29th with a wider application. The bank’s most successful result was for operational efficiency, where it came in eighth. However, it underlines the significant disparity between the growth rates of the smaller and the very largest banks. While China Minsheng ranked highest for growth and soundness among the top 10, it comes in just 37th and 33rd, respectively, in the top 100. Liquidity was the bank’s poorest result, where it ranked in 69th place.
Looking at the different metrics individually shows categories in which some banks are far exceeding their overall performance. Jinshang Bank, for example, which came in 15th place for best performance, took the top spot for liquidity. Meanwhile, Bank of Tianjin, which came first for operational efficiency, could manage just 26th place overall.
Methodology
The Banker’s global and regional rankings are the industry standard measures of bank size by Tier 1 capital. While the current rankings include some additional data to give an overall impression of bank performance, they use only a fraction of the very detailed analysis undertaken by our research team.
Knowing which bank is biggest, or has grown fastest, is useful, but what people really want to know is: “Which bank is the best performer?”
We have developed a model that scores and ranks banks in eight key performance categories, using 17 ratios, and assigns an overall best-performing bank score and ranking.
The key requirement of the model was that it could be used to identify the best performers in any sample group, be it an existing global, regional or country ranking, or a custom peer group such as global systemically important banks.
The model only uses performance ratios, and year-on-year percentages and basis points (bps) changes, so the size of a bank has no influence on its best-performance ranking.
The performance categories and indicators are:
Growth Annual percentage growth in assets, loans, deposits and operating income.
Profitability Return on assets, return on equity, profit margin, asset utilisation (and annual bps changes in these ratios).
Operational efficiency Cost-to-income ratio (and annual bps change in this ratios).
Asset quality Allowance for loan losses to gross total loans, non-performing loans, impairment charges to total operating income (and annual bps change in these ratios).
Return on risk Return on risk-weighted assets (and annual bps change in this ratio).
Liquidity Loans-to-assets ratio, loans-to-deposits ratio (and annual bps changes in these ratios).
Soundness Capital assets ratio (and annual bps change in this ratio).
Leverage Total liabilities to total assets (and annual bps change in this ratio).
When the peer group data is imported, the model assigns a score for each indicator based on the relative distribution of values. Thus a bank that significantly outperforms on a particular indicator will receive a proportionately higher score. The maximum possible score for each category is 10 points and the maximum overall score is 80 points.
The model is neutrally weighted so that the underlying ratios and annual bps changes are of equal significance. Each performance category receives equal weighting. We plan to produce an online version of the benchmarking tool, which will allow users to assign data point and category weights according to their own preferences.
