Abstract

Commercial banks occupy an important leading position in China’s banking industry, and their efficiency is of great reference value to the economy of China’s financial system, reflecting the current state of China’s economy. In this paper, the super‐efficiency DEA values of 19 commercial banks from 2016 to 2020 are calculated by employing the super‐efficiency method into the two‐stage network DEA model with constant returns to scale. Compared with the traditional two‐stage network DEA model, this method is better. The results show that the calculated values are more accurate than the DEA values measured directly by the two‐stage network, and the banks with an efficiency value of 1 can be further distinguished. According to the analysis of table data, the operating efficiency of ICBC is the highest, the operating efficiency of the entire banking sector is at a medium level, the second stage has a greater impact on the overall efficiency, and the loan side business needs to be improved.

Highlights

  • At present, there are many scholars in various fields at home and abroad to do academic research. e most common research method used by them is the data envelopment analysis (DEA) method

  • Cooper (1984) proposed the DEA model (BCC model) under the assumptions of variable returns to scale. e DEA method is a method of evaluating the objects, respectively, by calculating the relative efficiency decision units of the same type

  • From the data in the table, the efficiency average is relatively stable, but more than 50% of bank efficiency is lower than the average, indicating that the entire bank business efficiency is in the medium level, but more than half of the banks’ operating efficiency is lower than the average

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Summary

Introduction

There are many scholars in various fields at home and abroad to do academic research. e most common research method used by them is the data envelopment analysis (DEA) method. Ping An Bank, in particular, had the lowest efficiency value for two consecutive years, which was substantially lower than the banking industry as a whole

Jiangsu Bank
Findings
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