Browsing by Author "Boubaker S"

Now showing 1 - 8 of 8
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    An MCDA composite index of bank stability using CAMELS ratios and shannon entropy
    (Springer Science+Business Media, LLC, 2024-05-09) Boubaker S; Ngo T; Samitas A; Tripe D
    This study uses the multi-criteria decision-analysis (MCDA) approach to construct a composite performance index (CPI) directly from the CAMELS financial ratios. The CPI has several promising characteristics, such as (i) being an absolute measure of performance that allows for adding or removing data without affecting the existing scores; (ii) employing CAMELS ratios directly in its calculation without the need for normalization or imputation of positive values; (iii) employing the dynamic weighting system of data envelopment analysis (DEA); (iv) providing more robust insights on the Vietnamese banking system under the Shannon entropy approach; and (v) can be an alternative measure of bank stability, compared to the CAMELS ratings and z-scores. Based on a rich dataset of 45 Vietnamese banks spanning from 2002 to 2020, our findings suggest that the proposed CPI could offer an overall view consistent with other approaches for measuring banking sector performance and stability and identifying specific strengths and weaknesses of banks.
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    Efficiency in Vietnamese banking: A meta-regression analysis approach
    (MDPI (Basel, Switzerland), 2021-09) Ho TH; Nguyen DT; Ngo T; Le TDQ; Balvers R; Boubaker S
    This study explains the differences and variances in the efficiency scores of the Vietnamese banking sector retrieved from 27 studies published in refereed academic journals under the frame-work of meta-regression analysis. These scores are mainly based on frontier efficiency measurements, which essentially are Data Envelopment Analysis (DEA) and Stochastic Frontier Analysis (SFA) for Vietnamese banks over the period of 2007–2019. The meta-regression is estimated by using truncated regression to obtain bias-corrected scores. Our findings suggest that only the year of publication is positively correlated with efficiency, whilst the opposite is true for the data type, and sample size.
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    Fintech Credit and Bank Efficiency: International Evidence
    (MDPI (Basel, Switzerland), 2021-08-17) Le TDQ; Ho TH; Nguyen DT; Ngo T; Boubaker S
    The expansion of fintech credit around the world is challenging the global banking system. This study investigates the interrelationships between the development of fintech credit and the efficiency of banking systems in 80 countries from 2013 to 2017. The findings indicate a two-way relationship between them. More specifically, a negative relationship between bank efficiency and fintech credit implies that fintech credit is more developed in countries with less efficient banking systems. Meanwhile, a positive impact of fintech credit on the efficiency of banking systems suggests that fintech credit may serve as a wake-up call to the banking system. Therefore, fintech credit should be encouraged by the authorities around the world.
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    ICT as a Key Determinant of Efficiency: A Bootstrap-Censored Quantile Regression (BCQR) Analysis for Vietnamese Banks
    (MDPI (Basel, Switzerland), 2022-06-16) Le TDQ; Ngo T; Ho TH; Nguyen DT; Boubaker S
    There is evidence that ICT developments can improve bank efficiency and performance. Previous studies often employ data envelopment analysis (DEA) to first examine bank performance and then use a second-stage regression to explain the influences of other environmental factors, including ICT, on such efficiency. Since DEA efficiency scores are bounded between the (0, 1] intervals, Tobit and truncated regressions are commonly used in this stage. However, none has accounted for the skewness characteristic of DEA efficiency. This paper applied a bootstrap-censored quantile regression (BCQR) approach to triply account for the issues of a small sample (via bootstrap), bounded intervals (via censored regression), and skewness (via quantile regression) in DEA analysis. We empirically examined the efficiency and performance of 27 Vietnamese commercial banks in the 2007–2019 period. The efficiency scores derived from our first stage revealed that they are skewed and thus, justify the use of the BCQR in the second stage. The BCQR results further confirmed that ICT developments could enhance bank efficiency, which supports the recent policy to restructure the Vietnamese banking sector toward innovation and digitalization. We also examined the impacts of other factors such as bank ownership, credit risk, and bank size on efficiency.
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    Managing bank performance under COVID-19: A novel inverse DEA efficiency approach
    (John Wiley and Sons Ltd on behalf of International Federation of Operational Research Societies, 2023-09) Boubaker S; Le TDQ; Ngo T
    The evolution of the COVID-19 pandemic is highly unpredictable; however, its impacts are limited to neither a single sector nor a single country. This study evaluates the performance and efficiency of 49 Islamic banks across 10 countries during 2019-2020 to assess how those banks can preserve their performance and remain resilient in the aftermath of the COVID-19 pandemic. Using the conventional inverse data envelopment analysis (InvDEA) approach, we show that because of reductions in their outputs, 31 out of the 49 banks studied would need to reduce their inputs so that their efficiency can remain unchanged. However, we show that only 10 banks need to make such adjustments to maintain their efficiency levels using our proposed InvDEA efficiency model. The adjustment for those 10 banks would help in reducing more inputs, suggesting more cost savings, and improving the overall efficiency of the examined banks, compared with the other 31 banks.
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    Predicting the performance of MSMEs: a hybrid DEA-machine learning approach
    (Springer Science+Business Media, LLC, 2023-02-14) Boubaker S; Le TDQ; Ngo T; Manita R
    Micro, small and medium enterprises (MSMEs) dominate the business landscape and create more than half of employment worldwide. How we can apply big data analytical tools such as machine learning to examine the performance of MSMEs has become an important question to provide quicker results and recommend better and more reliable solutions that improve performance. This paper proposes a novel method for estimating a common set of weights (CSW) based on regression analysis for data envelopment analysis (DEA) as an important analytical and operational research technique, which (i) allows for measurement evaluations and ranking comparisons of the MSMEs, and (ii) helps overcome the time-consuming non-convexity issues of other CSW DEA methodologies. Our hybrid approach used several econometric and machine learning techniques (such as Tobit, least absolute shrinkage and selection operator, and Random Forest regression) to empirically explain and predict the performance of more than 5400 Vietnamese MSMEs (2010‒2016), and showed that the machine learning techniques are more efficient and accurate than the econometric ones. Our study, therefore, sheds new light on the two-stage DEA literature, especially in terms of predicting performance in the era of big data to strengthen the role of analytics in business and management.
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    The rise of common state ownership and corporate environmental performance
    (Elsevier Ltd, 2024-03-13) Liu X; Boubaker S; Liao J; Yao S
    This study assesses the effect of common state ownership on corporate environmental performance. Using a large sample of Chinese listed firms, we find that state-owned common ownership leads to significantly enhanced corporate environmental performance. Our mechanism analysis indicates that state-owned common owners promote environmental-friendly practices through resource allocation mechanisms that alleviate corporate financial constraints. In addition, these owners play a leadership role in fostering corporate green innovation and enhancing the overall performance of the industry. Specifically, common state ownership leads to higher industry's green total factor productivity and profitability. Moreover, we observe that the positive relationship between common state ownership and corporate environmental performance is more pronounced in firms without politically connected CEOs/chairpersons and in privately owned firms.
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    The trade-off frontier for ESG and Sharpe ratio: a bootstrapped double-frontier data envelopment analysis
    (Springer Science+Business Media, LLC, 2023-07-24) Boubaker S; Le TDQ; Manita R; Ngo T
    The trade-off between the returns and the risks associated with the stocks (i.e., the Sharpe ratio, SR) is an important measure of portfolio optimization. In recent years, the environmental, social, and governance (ESG) has increasingly proven its influence on stocks’ returns, resulting in the evolvement from a two-dimensional (i.e., risks versus returns) into a multi-dimensional setting (e.g., risks versus returns versus ESG). This study is the first to examine this setting in the global energy sector using a (slacks-based measures, SBM) ESG-SR double-frontier double-bootstrap (ESG-SR DFDB) by studying the determinants of the overall ESG-SR efficiency for 334 energy firms from 45 countries in 2019. We show that only around 11% of our sampled firms perform well in the multi-dimensional ESG-SR efficient frontier. The 2019 average (in)efficiency of the global energy sector was 2.273, given an efficient level of 1.000. Besides the differences in the firm’s input/output utilization (regarding their E, S, G, and SR values), we found that the firm- (e.g., market capitalization and board characteristics) and country-level characteristics (e.g., the rule of law) have positive impacts on their ESG-SR performance. Such findings, therefore, are essential not only to the (responsible) investors but also to managers and policymakers in those firms/countries.