Evaluation and evolution of bank efficiency considering heterogeneity technology: An empirical study from China

Yin, Zhujia; Yu, Yantuan; Huang, Jianhuan

doi:10.1371/journal.pone.0204559

Cited by 10 publications

(4 citation statements)

References 65 publications

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“…According to Yin et al [11], the overall efficiency, productivity, efficiency, and profitability efficiency of Chinese commercial banks showed significant heterogeneity. Nevertheless, Fungáčová et al [12] found that the big five Chinese banks suffer from low average cost efficiency.…”

Section: Efficiency Overviewmentioning

confidence: 99%

Prediction of Banks Efficiency Using Feature Selection Method: Comparison between Selected Machine Learning Models

Assous

2022

Complexity

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This study aims to examine the main determinants of efficiency of both conventional and Islamic Saudi banks and then choose the best fit model among machine learning prediction models (i.e., support vector machine (SVM), Chi-squared automatic interaction detector (Chaid), linear regression, and neural network (NN)). The data were collected from the annual financial reports of Saudi banks from 2014 to 2018. The Saudi banking sector consists of 11 banks, 4 of which are Islamic. In this study, the major financial ratios are subgrouped into the profitability ratios, managerial practices, asset and loans, capital adequacy ratios, and liquidity. First, regression analysis is implemented with efficiency ratio as a dependent variable and the proxies of banks’ profitability, liquidity, asset quality, management ratios, and capital adequacy ratios as independent variables. Next, the feature selection is applied for different prediction models. Subsequently, 4 prediction models (i.e., SVM, CHAID, linear regression, and a neural network) were developed to choose the best fit. The performance metrics have also been evaluated. Regression results exhibit that the efficiency of both conventional and Islamic banks is highly affected by profitability, liquidity, and managerial practices. Finally, we choose the best prediction model with the highest R2 in the training and the testing phases with/out feature selection that is the CHAID model. The best predictors of cost efficiency for Saudi banks are the capital ratios, namely, CAR total and CAR tier 1. Findings are theoretically and practically important to academics, investors, and policymakers. Policymakers can benefit from the novelty of this study in building an early warning system using the CHAID model to predict different financial distress scenarios.

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Section: Efficiency Overviewmentioning

confidence: 99%

Prediction of Banks Efficiency Using Feature Selection Method: Comparison between Selected Machine Learning Models

Assous

2022

Complexity

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“…[ 46 ] revealed that conventional banks have better management and asset quality compared to Islamic banks who have better CAR and liquidity ratios. [ 47 ] found that the big five Chinese banks suffer from low average cost efficiency while [ 48 ] found that a significant heterogeneity in Chinese commercial banking efficiency for overall efficiency, productivity, and profitability efficiency.…”

Section: Literature Reviewmentioning

confidence: 99%

Key determinants of deposits volume using CAMEL rating system: The case of Saudi banks

AL-Najjar

Assous

2021

PLoS ONE

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CAMEL is considered one of the well-known banking rating systems used to build a proper bank ranking. In our paper, we investigate the CAMEL rating for Saudi banks, which is considered the second largest banking sector in GCC. The Saudi banking sector consists of 11 banks and is the leading sector in the Saudi stock index (TASI). In this research, we aim to determine the ranking of Saudi banks according to CAMEL composite and CAMEL overall ratings and explore the effects of these ratings on banks’ total deposits for the period from 2014 to 2018. The methodology involves four phases. In the first phase, we calculate the key financial ratios of CAMEL’s composites for each bank. In the second phase, we rank the banks from 1 to 11 to each one of CAMEL’s composites for each bank per year. In the third phase, we rank Saudi banks according to CAMEL composite and CAMEL overall. Finally, in the fourth phase, we run a regression model using CAMEL financial ratios rank as independent variable and banks’ total deposits as a dependent variable. Using the stepwise regression method, the results indicated that the best regression model has an adjusted R2 of 73.4% and a standard error of around 0.58. The results further indicated that capital measured by CAR, management as an efficiency ratio, earning with ROE proxy, and liquidity as loans to deposits have positive effects on banks’ total deposits. Meanwhile, earnings as net interest income to net revenue and liquidity calculated by CASA have a negative effect on banks’ total deposits. Finally, asset quality ratios and the rest of the ratios have no significant effect on banks’ total deposits.

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“…In China, intuitively evaluating bank efficiency has an important role in providing policy implications to managers and decision makers. Problems related to banking efficiency have long been emphasized in the literature, where financial ratio analysis is one of the factors that has been applied to find production or cost limits and analyze productivity efficiency (Yin et al, 2018). Even, a production can be considered efficient when using a certain amount of input can produce maximum output or by using minimal input (Beck et al, 2013).…”

Section: Literature Review Efficiencymentioning

confidence: 99%

Analysis of Macroeconomic Fluctuations Impact on Efficiency and Islamic Banking Quality 2015-2019

Yudha

Indrawan

Syarifudin³

2021

JEBIS

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This study aims to analyze the impact of macroeconomic shocks, such as economic growth, inflation and the rupiah exchange rate on Islamic banking efficiency. This study uses the two-stage least squares (TSLS) method, which is a method with two endogenous variables, namely the non-performing financing (NPF) ratio as a representation of the quality of Islamic banking financing and the operating expense operating income (BOPO) ratio as a representation of Islamic banking efficiency. So that the TSLS method aims to purify these endogenous variables against stochastic disturbances. This study uses monthly data from January 2010 to January 2020, or as many as 121 observations. The observation of data is obtained from Islamic Banking Statistic that published by Financial Services Authority (OJK). The efficiency of Islamic banks is influenced by the number of sharia banking office networks, capital adequacy ratios, and NPF ratios. The NPF ratio itself is affected by macroeconomic conditions (economic growth and appreciation of the rupiah exchange rate) as well as the efficiency of Islamic banking. Based on the above findings, a number of recommendations were given, including the urgency of simultaneously increasing efficiency and quality of Islamic banking financing accompanied by monitoring Indonesia's macroeconomic conditions, so that it is hoped that Islamic banks will be more resilient to macroeconomic shocks and competitive in the national financial services industry.

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Evaluation and evolution of bank efficiency considering heterogeneity technology: An empirical study from China

Cited by 10 publications

References 65 publications

Prediction of Banks Efficiency Using Feature Selection Method: Comparison between Selected Machine Learning Models

Prediction of Banks Efficiency Using Feature Selection Method: Comparison between Selected Machine Learning Models

Key determinants of deposits volume using CAMEL rating system: The case of Saudi banks

Analysis of Macroeconomic Fluctuations Impact on Efficiency and Islamic Banking Quality 2015-2019

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