Efficiency measurement of the banking sector in the presence of non-performing loan

Hamid, Nurhayati; Ramli, Noor Asiah; Hussin, Siti Aida Sheikh

doi:10.1063/1.4972145

Cited by 16 publications

(12 citation statements)

References 17 publications

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“…This inefficiency was mainly caused by high non-performing loans because of adverse borrower selection. Our results were found to be consistent with the findings of Hamid et al (2017); Zago and Dongili (2011).…”

Section: Resultssupporting

confidence: 92%

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Incorporating Credit Quality in Bank Efficiency Measurements: A Directional Distance Function Approach

Qayyum

Riaz

2018

JRFM

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The objective of the study was to measure the risk-adjusted efficiency of banks in 24 emerging economies for the period of 1999-2013. A two-stage network data envelopment analysis (DEA), with separate deposit mobilization and loan financing stages was used. Efficiency was measured using directional distance functions with DEA, featuring non-performing loans as undesirable outputs. The distributions of efficiency scores were different when credit quality was taken into account. The distribution of efficiency scores varied systematically with accumulation of non-performing loans across regions. The financial crisis of 2007-2008 impacted more adversely the regions that had higher proportions of non-performing loans in banks' portfolios. The results of a follow-on non-parametric regression showed that smaller, better capitalized, and private banks were more efficient. The conditions conducive for high levels of technical efficiency by banks were found to be characterized by economic growth and low inflation.Most previous studies used either the production or the intermediation approach to model bank efficiency.

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Section: Resultssupporting

confidence: 92%

“…selection. Our results were found to be consistent with the findings of Hamid et al (2017); Zago and Dongili (2011). The measured efficiency at the deposit mobilization stage was 93%, which indicates that, at this stage of operations, banks were highly efficient, possibly reflecting managerial performance metrics that reward deposit creation.…”

Section: Resultssupporting

confidence: 88%

Incorporating Credit Quality in Bank Efficiency Measurements: A Directional Distance Function Approach

Qayyum

Riaz

2018

JRFM

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“…Nonparametric methods such as Data Envelopment Analysis (DEA) have been suggested in the literature [6][7][8][9][10][11][12][13][14][15][16][17][18] as a good measure of banks' efficiency and performance. For instance, [19] through the DEA model evaluated the marginal benefits of IT using 36 DMUs financial institution's data.…”

Section: Related Workmentioning

confidence: 99%

Predicting Bank Operational Efficiency Using Machine Learning Algorithm: Comparative Study of Decision Tree, Random Forest, and Neural Networks

Appiahene

Missah

Najim

2020

Advances in Fuzzy Systems

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The financial crisis that hit Ghana from 2015 to 2018 has raised various issues with respect to the efficiency of banks and the safety of depositors’ in the banking industry. As part of measures to improve the banking sector and also restore customers’ confidence, efficiency and performance analysis in the banking industry has become a hot issue. This is because stakeholders have to detect the underlying causes of inefficiencies within the banking industry. Nonparametric methods such as Data Envelopment Analysis (DEA) have been suggested in the literature as a good measure of banks’ efficiency and performance. Machine learning algorithms have also been viewed as a good tool to estimate various nonparametric and nonlinear problems. This paper presents a combined DEA with three machine learning approaches in evaluating bank efficiency and performance using 444 Ghanaian bank branches, Decision Making Units (DMUs). The results were compared with the corresponding efficiency ratings obtained from the DEA. Finally, the prediction accuracies of the three machine learning algorithm models were compared. The results suggested that the decision tree (DT) and its C5.0 algorithm provided the best predictive model. It had 100% accuracy in predicting the 134 holdout sample dataset (30% banks) and a P value of 0.00. The DT was followed closely by random forest algorithm with a predictive accuracy of 98.5% and a P value of 0.00 and finally the neural network (86.6% accuracy) with a P value 0.66. The study concluded that banks in Ghana can use the result of this study to predict their respective efficiencies. All experiments were performed within a simulation environment and conducted in R studio using R codes.

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“…Numerous studies have been conducted in the past in relation to efficiency or/and effectiveness in measuring the performance of the banking system. Recently Hamid et al (2017) acknowledged nonperforming loans as being undesirable outputs while measuring efficiency. The Directional Distance Function (DDF) approach which extended from the Data Envelopment Analysis (DEA) framework is one of the enhancement efficiency approaches to handle a situation when there is a joint production of the desirable and undesirable outputs.…”

Section: Literature Reviewmentioning

confidence: 99%

Performance Measurement of Islamic and Conventional Banking in Malaysia Using Two-stage Analysis of DEA Model

Ramli¹,

Khairi²,

Razlan³

2018

IJARBSS

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Efficiency and effectiveness are two factors that have been considered in measuring the overall performance of any institution. The objective of this study is to measure the technical efficiency scores as well as the effectiveness of the Malaysian banking sector, and thus, determine their overall performance. The study analyzes the performance of the Malaysian banking sector, which comprises two types of bank, namely commercial banks and Islamic banks. The study covers the period of 2011 to 2015. A two-stage analysis of Data Envelopment Analysis (DEA) has been used in this study. The results of the analysis show that the average efficiency scores are greater than the average effectiveness scores for both types of bank for the years 2011 until 2015, while conventional banks perform better than Islamic banks. An efficient bank does not always equate to it being more effective. Together, these two factors reflect the overall performance of a bank. An analysis on the correlation between efficiency, effectiveness, and overall performance scores indicates that a positive and strong correlation exists between effectiveness and performance, whereas there is a negative correlation between efficiency and effectiveness scores.

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Efficiency measurement of the banking sector in the presence of non-performing loan

Cited by 16 publications

References 17 publications

Incorporating Credit Quality in Bank Efficiency Measurements: A Directional Distance Function Approach

Incorporating Credit Quality in Bank Efficiency Measurements: A Directional Distance Function Approach

Predicting Bank Operational Efficiency Using Machine Learning Algorithm: Comparative Study of Decision Tree, Random Forest, and Neural Networks

Performance Measurement of Islamic and Conventional Banking in Malaysia Using Two-stage Analysis of DEA Model

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