Bank efficiency estimation in China: DEA-RENNA approach

Antunes, Jorge; Hadi-Vencheh, A.; Jamshidi, Ali; Tan, Yong

doi:10.1007/s10479-021-04111-2

Cited by 44 publications

(19 citation statements)

References 104 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…e widely used efficiency measurement methods can be roughly divided into two categories, the accounting indicator analysis (AIA) method and the stochastic frontier analysis (SFA) method [1]. AIA has several problems in the process of selecting indicators, such as randomness, relevance, and collinearity.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Because the DEA method does need not to define the form of production function or estimate the parameters of the production function, it is more and more popular in estimating efficiency. Although the choice of different input and output variables will affect the efficiency estimation, empirical study shows that results obtained by using the DEA method are robust [1].…”

Section: Literature Reviewmentioning

confidence: 99%

“…In the banking industry, the DEA method is also widely used in the efficiency estimation. Antunes et al [1] proposed a new DEA model to estimate the efficiency of 39 Chinese commercial banks from 2010 to 2018. e study found that the efficiency level of Chinese banks in the sample continued to improve up to 2015; subsequently, the efficiency level decreases and then slightly fluctuates. Xie and He (2021) [22] established an efficiency evaluation model for commercial banks by using the DEA-Malmquist index, to analyze the financial data of 9 listed banks in China from 2011 to 2020, and found that there are differences exist in the efficiency of commercial banks.…”

Section: Literature Reviewmentioning

confidence: 99%

See 2 more Smart Citations

Evaluation of Innovation Efficiency on Chinese Commercial Banks under the Internet Finance: A DEA-Malmquist Index-Based Approach

Zhao

Zhang

2022

Security and Communication Networks

View full text Add to dashboard Cite

The development of the digital economy and internet finance greatly impacts the technological innovation of Chinese commercial banks. Therefore, most of the Chinese commercial banks have invested a lot of money in the new information technology applications to adapt to the impacts of internet finance. Consequently, whether these investments have achieved the expected effect and efficiency has been concerned by these banks. For these concerns, we have used relative data from 49 Chinese commercial banks from 2010 to 2020 period and used the DEA-BCC model and DEA-Malmquist index to analyze their innovation efficiency from both static and dynamic perspectives. Our study found that from the static perspective, the innovation efficiency greatly varies among the banks in the study, and most banks have room for improvement in pure technical efficiency and scale efficiency. From the dynamic perspective, the innovation efficiency of the banks in the study is generally on the rise. Changes in technical efficiency play a major role in improving their overall efficiency, and technological progress is the second driving force. Therefore, technological investment should be increased, and technological levels should be improved to improve innovation efficiency. Our study also shows that, according to the ownership types, different groups of banks have differently shown their innovation efficiencies. Joint-stock commercial banks have the highest innovation efficiency, while rural commercial banks have the lowest innovation efficiency. Our study suggests that all Chinese commercial banks should overcome their own shortages in information technological innovation capability to elevate their innovation efficiency, under the background of internet finance.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Evaluation of Innovation Efficiency on Chinese Commercial Banks under the Internet Finance: A DEA-Malmquist Index-Based Approach

Zhao

Zhang

2022

Security and Communication Networks

View full text Add to dashboard Cite

show abstract

“…Many studies employ DEA to analyze bank performance compared to their peers (Mahmoudabadi & Emrouznejad, 2019 ; Ouenniche & Carrales, 2018 ; Yang, 2014 ). There are also some studies combining other methods to measure banks’ efficiency, such as DEA with network analysis (Antunes et al, 2021 ; Tan et al, 2021 ), or bootstrap methods (Dia et al, 2020 ) etc.. Stochastic Frontier Analysis (SFA), as a parametric production efficiency frontier analysis tool, is also widely used to evaluate the operating efficiency of banks (Ngo & Tripe, 2017 ). SFA can also be used to measure bank failure, for instance in Sanchez González et al ( 2020 ), who applied the SFA and Bayesian approach to commercial banks in the USA to estimate the effect of inefficiency on bank failure.…”

Section: Literature Reviewmentioning

confidence: 99%

Bank efficiency and failure prediction: a nonparametric and dynamic model based on data envelopment analysis

Chen

Tang

2022

Ann Oper Res

View full text Add to dashboard Cite

For decades, the prediction of bank failure has been a popular topic in credit risk and banking studies. Statistical and machine learning methods have been working well in predicting the probability of bankruptcy for different time horizons prior to the failure. In recent years, bank efficiency has attracted much interest from academic circles, where low productivity or efficiency in banks has been regarded as a potential reason for failure. It is generally believed that low efficiency implies low-quality management of the organisation, which may lead to bad performance in the competitive financial markets. Previous papers linking efficiency measures calculated by Data Envelopment Analysis (DEA) to bank failure prediction have been limited to cross sectional analyses. A dynamic analysis with the updated samples is therefore recommended for bankruptcy prediction. This paper proposes a nonparametric method, Malmquist DEA with Worst Practice Frontier, to dynamically assess the bankruptcy risk of banks over multiple periods. A total sample of 4426 US banks over a period of 15 years (2002–2016), covering the subprime financial crisis, is used to empirically test the model. A static model is used as the benchmark, and we introduce more extensions for comparisons of predictive performance. Results of the comparisons and robustness tests show that Malmquist DEA is a useful tool not only for estimating productivity growth but also to give early warnings of the potential collapse of banks. The extended DEA models with various reference sets and orientations also show strong predictive power.

show abstract

“…We use personnel expenses, equity capital and fixed assets as the inputs in the first stage following Sturn and Williams ( 2004 ) and Antunes et al, ( 2021 ). The proposal of using deposits, deposit-based market power, loans as well as loan-based market power is in line with Fukuyama and Tan ( 2020 ).…”

Section: Modelmentioning

confidence: 99%

Investigating into the dual role of loan loss reserves in banking production process

Fukuyama

Tan

2021

Ann Oper Res

Self Cite

View full text Add to dashboard Cite

This paper considers the use of loan loss reserves (LLRs) in the banking production process and treats it as one variable with a dual role. We establish a three-stage network Data Envelopment Analysis model to address this issue. Using a sample of 43 Chinese commercial banks over the period 2011–2019, the results show that the banks with the ratio between LLRs and total loans less than 1% have higher level of efficiency compared to the ones holding the ratio greater than 1%. The results show that when excluding LLRs in the production process, the efficiency scores are significantly inflated. We find that small and medium sized banks are more efficient than their big counterparts, however, the results show that big banks hold more than enough amounts of LLRs than the one required by the regulatory authority. When LLRs are excluded from the production process, it shows that big banks perform better than small and medium sized banks. Our findings show that less liquid banks perform better than the ones with higher levels of liquidity no matter in which way LLRs are treated. Finally, we find that lower capitalized banks, compared to the ones with high levels of capitalization, are less efficient. however, it shows that higher capitalized banks consistently keep more than 1% LLRs out of total loans.

show abstract

Bank efficiency estimation in China: DEA-RENNA approach

Cited by 44 publications

References 104 publications

Evaluation of Innovation Efficiency on Chinese Commercial Banks under the Internet Finance: A DEA-Malmquist Index-Based Approach

Evaluation of Innovation Efficiency on Chinese Commercial Banks under the Internet Finance: A DEA-Malmquist Index-Based Approach

Bank efficiency and failure prediction: a nonparametric and dynamic model based on data envelopment analysis

Investigating into the dual role of loan loss reserves in banking production process

Contact Info

Product

Resources

About