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China Merchants Bank Leads Mainland Lenders in Financial Performance: S&P Global Analysis

China Merchants Bank Leads Mainland Lenders in Financial Performance: S&P Global Analysis

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China Merchants Bank Leads Mainland Lenders in Financial Performance: S&P Global Analysis

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China Merchants Bank Leads Mainland Lenders in Financial Performance: S&P Global Analysis
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DBT Bureau

Pune, 4 July 2024

China Merchants Bank Co. Ltd. topped the inaugural S&P Global Market Intelligence ranking of mainland China-headquartered lenders based on financial performance. The Shenzhen-based midsize lender was followed by Ping An Bank Co. Ltd., also headquartered in Shenzhen, and Nanjing-based Bank of Jiangsu Co. Ltd. in the ranking of the 50 largest publicly traded banks in mainland China by assets as of Dec. 31, 2023, and covered by S&P Global Market Intelligence.

Key highlights from the analysis include:

Among the four biggest banks in mainland China by assets, China Construction Bank Corp. ranked 18th, Industrial and Commercial Bank of China Ltd. was 22nd, Bank of China Ltd. was 27th, and Agricultural Bank of China Ltd. ranked 36th.

Bank of Communications Co. Ltd. took the 34th place. Along with the big four, the lender is one of the five mainland Chinese lenders identified as global systemically important banks (G-SIBs) by the Financial Stability Board and the Basel Committee on Banking Supervision.
Overall 28 lenders had positive total scores that ranged from 1.10 for China Merchants Bank to China Zheshang Bank Co. Ltd. with 0.02 in the Market Intelligence analysis. Banks received a positive score if a performance metric was better than the top 50 average and a negative score if the metric underperformed.

In this analysis, companies were ranked according to seven weighted financial metrics: return on average equity (16%), net interest margin (16%), noninterest income/average assets (16%), cost-to-income (16%), problem loan ratio (16%), liquidity coverage ratio (10%) and common equity Tier 1 ratio (10%). The mean for each metric was identified across the sample, and standard deviations from these means were calculated for each metric for each institution and aggregated to determine a relative performance score. Scores for each metric were also capped to help normalize the data.

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