Analysis of Chinese Commercial Banks’ Risk Management Efficiency Based on the PCA-DEA Approach

Abstract

As a special type of enterprises with high risks, Chinese commercial banks’ risk management plays an important role in banks’ business process. Measuring and improving the risk management efficiency of the Chinese commercial banking system has recently attracted increasing interest. Previous studies analyze the business performance of commercial banks from the perspective of the overall management level of banks, and few articles focus on the risk management ability of banks. This paper evaluates the technical efficiencies of Chinese commercial banks’ risk management by the DEA-BCC model with window analysis to come up with some recommendations for policy makers. The technical efficiency is then decomposed into pure technology efficiency and scale efficiency. According to the banking risk supervision indicators released by the China Banking Regulatory Commission, we choose the indicators of 26 commercial banks’ risk management during the period of 2011 to 2019. Principal component analysis (PCA) is applied to delete redundant input indicators. The paper gives a dynamic evaluation of technology efficiency, pure technology efficiency, and scale efficiency. The main empirical results are as follows: (1) the technical efficiency of Chinese commercial banks’ risk management is low, and the differences among three different types of banks are large. (2) The pure technology inefficiency of Chinese commercial banks’ risk management has become a key factor restricting the improvement of the risk management of the Chinese banking industry. (3) The Chinese commercial banks’ risk management faces a serious problem which is economies of scale. (4) The technical efficiencies of Chinese commercial banks’ risk management fluctuate greatly, and management capabilities need to be enhanced urgently.

Data-Driven Evaluation and Optimisation of Livelihood Improvement Efficiency

“…Compared with the existing literature [36,[49][50][51][52], this study can reduce the dimensionality of the complex evaluation index system while retaining the information of the original variables, overcome the limitation of the number of indicators in the DEA evaluation method, and improve the accuracy of the evaluation results. Moreover, in contrast to the existing literature [41][42][43][44][45][46][47], this study used the Tobit method to analyse the influencing factors of LIE. This contributes to the completeness of research on LIE evaluation and optimisation, improves the scientificity of the judgment of the influencing factors, and lays the foundation for policy recommendations.…”

“…Poldaru et al used PCA-DEA to evaluate the quality of life of residents of Estonian counties [43], whereas Sarra et al applied this method to evaluate the well-being of residents of Italian cities [44]. In addition, the combination of the two methods has been widely used in efficiency evaluation in other fields, such as the operational efficiency of transportation companies [45], the management efficiency of banks [46], and the work performance of governments [47]. When the dependent variable has data truncation characteristics, ordinary least-squares calculations can result in biased parameter estimates; thus, Tobin proposed a restricted dependent variable model in 1958 [48].…”

Risk Analysis with the Dempster–Shafer Theory for Smart City Planning: The Case of Qatar

“…The PCA method is a multivariate analysis technique used to analyze data variations when there are highly correlated variables and a large number of independent variables [37]. The PCA method is used for risk analysis in different fields such as machine learning and artificial intelligence [38], banking risk management.…”

A novel algorithm for complete ranking of DMUs dealing with negative data using Data Envelopment Analysis and Principal Component Analysis: Pharmaceutical companies and another practical example

“…Those that employ the PCA and use its outputs as inputs of the DEA. These models are not able to provide any benchmarks or reference points for inefficient DMUs [4][5][6][7][8][9][10][11][12][13][14][15].…”