Inefficiency and bank failure: A joint Bayesian estimation method of stochastic frontier and hazards models

Gonzalez, Jim Sánchez; Restrepo-Tobón, Diego A.; Hassan, Andrés Ramírez

doi:10.1016/j.econmod.2020.03.002

Cited by 5 publications

(3 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Researchers have studied the factors that have an impact on corporate failure. For example, González et al ( 2021 ) proposed a Bayesian one-stage approach to estimate the effect of inefficiency on the time to failure (bankruptcy) of U.S. commercial banks. The result obtained show that their proposal outperforms the two-stage maximum likelihood approach traditionally used in the literature.…”

Section: Related Literature Workmentioning

confidence: 99%

The role of artificial intelligence in developing a banking risk index: an application of Adaptive Neural Network-Based Fuzzy Inference System (ANFIS)

2023

View full text Add to dashboard Cite

Banking risk measurement and management remain one of many challenges for managers and policymakers. This study contributes to the banking literature and practice in two ways by (a) proposing a risk ranking index based on the Mahalanobis Distance (MD) between a multidimensional point representing a bank’s risk measures and the corresponding critical ratios set by the banking authorities and (b) determining the relative importance of a bank’s risk ratios in affecting its financial standing using an Adaptive Neuro-Fuzzy Inference System. In this study, ten financial ratios representing five risk areas were considered, namely: Capital Adequacy, Credit, Liquidity, Earning Quality, and Operational risk. Data from 45 Gulf banks for the period 2016–2020 was used to develop the model. Our findings indicate that a bank is in a sound risk position at the 99%, 95%, and 90% confidence level if its Mahalanobis distance exceeds 4.82, 4.28, and 4.0, respectively. The maximum distance computed for the banks in this study was 9.31; only five out of the forty-five banks were below the 4.82 and one below the 4.28 and 4.0 thresholds at 3.96. Sensitivity analysis of the risks indicated that the Net Interest Margin is the most significant factor in explaining variations in a bank’s risk position, followed by Capital Adequacy Ratio, Common Equity Tier1, and Tier1 Equity in order. The remaining financial ratios: Non-Performing Loans, Equity Leverage, Cost Income Ratio, Loans to Total Assets, and Loans to Deposits have the least influence in the order given; the Provisional Loans Ratio appears to have no influence.

show abstract

Section: Related Literature Workmentioning

confidence: 99%

The role of artificial intelligence in developing a banking risk index: an application of Adaptive Neural Network-Based Fuzzy Inference System (ANFIS)

2023

View full text Add to dashboard Cite

show abstract

“…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. Unlike parametric productivity analytical tools such as SFA, since DEA is a nonparametric method, it is more advantageous than SFA in terms of the free distribution of variables and multiple outputs, though it is SFA which has attracted more interest in the investigation of bank performance (Behr, 2010 ; Casu et al, 2004 ; Lampe & Hilgers, 2015 ).…”

Section: Literature Reviewmentioning

confidence: 99%

“…Avkiran and Cai ( 2014 ) used a non-oriented DEA super-SBM model to flag the bank holding companies, and the results supported both the DEA’s discriminant and predictive power. Scholars also compared the DEA method with the Logit model in failure prediction performance, and proved that DEA has higher estimation accuracy (Premachandra et al, 2009 ; Sanchez González et al, 2020 ).…”

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

A spatial one-sided error model to identify where unarrested criminals live

Puerta-Cuartas,

Ramírez-Hassan

2025

Economic Modelling

View full text Add to dashboard Cite

Inefficiency and bank failure: A joint Bayesian estimation method of stochastic frontier and hazards models

Cited by 5 publications

References 26 publications

The role of artificial intelligence in developing a banking risk index: an application of Adaptive Neural Network-Based Fuzzy Inference System (ANFIS)

The role of artificial intelligence in developing a banking risk index: an application of Adaptive Neural Network-Based Fuzzy Inference System (ANFIS)

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

A spatial one-sided error model to identify where unarrested criminals live

Contact Info

Product

Resources

About