A DEA and random forest regression approach to studying bank efficiency and corporate governance

Thaker, Keyur B.; Charles, Vincent; Pant, Abhay; Gherman, Tatiana

doi:10.1080/01605682.2021.1907239

Cited by 49 publications

(40 citation statements)

References 54 publications

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“…In summation, board composition plays an essential role in influencing efficiency and productivity. These corroborate the findings of Adams and Mehran (2012) and Thaker et al (2021). However, banks must attempt to reduce the cost of hiring independent directors, as a more friendly board may create cost inefficiencies.…”

Section: Resultssupporting

confidence: 82%

“…The result aligns with the free-rider hypothesis, implying that larger, independent and more diverse boards do not necessarily translate into increased efficiency. These findings contrast with Sarkar et al (2012) and Thaker et al (2021), both of which report a positive influence of board governance on performance. As far as TFP change is concerned, we report a positive coefficient of the supervisory board index and the audit index, implying that sound governance and audit mechanisms enhance the TFP.…”

Section: Resultscontrasting

confidence: 74%

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A non-parametric framework for evaluating governance–efficiency–productivity associations in commercial banking

Prakash

Singh

Sharma

2021

JES

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PurposeAgainst the backdrop of an Indian banking sector that finds itself entangled in the triple deadlock of increasing competition, technological changes and strict regulatory compliance, the study aims to examine the need for reinforcing stringent corporate and risk governance mechanisms as an instrument for improving efficiency and productivity levels.Design/methodology/approachThe authors construct three separate indices, namely, supervisory board index, audit index and risk governance index to measure the governance practices of commercial banks. A slacks-based data envelopment analysis technical efficiency (TE) measure, a variable returns to scale cost efficiency model and Malmquist productivity index are employed to determine TE, cost efficiency and productivity change, respectively. A two-step system-generalized method of moments estimation accounts for the dynamic relationship between governance and efficiency.FindingsThe authors show that strict audit and risk governance mechanisms are associated with better efficiency and productivity levels. However, consistent with the free-rider hypothesis, large, independent and diverse boards lead to cost inefficiencies. Strict risk governance structures circumvent the negative effects of high regulatory capital and improve efficiency and total factor productivity. However, friendly boards do not perform efficiently in the presence of regulatory capital, implying that incentives arising from maintaining high levels of equity capital make them more susceptible to risk-taking, and board composition is unable to sidestep this behaviour.Originality/valueThe paper contributes to the literature that explores the linkages between governance, efficiency and productivity. The inferences hold relevance in the post-COVID world, as regulators try to circumvent the additional stress on the banking system by adopting sound corporate and risk governance mechanisms.

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Section: Resultssupporting

confidence: 82%

Section: Resultscontrasting

confidence: 74%

A non-parametric framework for evaluating governance–efficiency–productivity associations in commercial banking

Prakash

Singh

Sharma

2021

JES

View full text Add to dashboard Cite

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“…39 In contrast to traditional regression techniques, RF has higher accuracy and a higher tolerance for outliers and noise. 40,41 Machine learning techniques such as RF outperform traditional regression techniques in solving the multi-collinearity problem. 40 Besides, the disadvantages of this method are that RFs are computationally intensive and storage demanding, 42 and variable importance measures can be skewed if the predictors are correlated.…”

Section: Resultsmentioning

confidence: 99%

“…High‐dimensional and highly correlated data sets can be efficiently processed, and information on the importance of each input variable for the model is provided 39 . In contrast to traditional regression techniques, RF has higher accuracy and a higher tolerance for outliers and noise 40,41 . Machine learning techniques such as RF outperform traditional regression techniques in solving the multi‐collinearity problem 40 .…”

Section: Resultsmentioning

confidence: 99%

Prediction of geoid undulations: Random forest versus classic interpolation techniques

Akar

Konakoǧlu

Akar

2022

Concurrency and Computation

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Local geoid determination studies are commonly carried out today to establish the relationship between the ellipsoidal height (h)$$ (h) $$ determined by satellite geodesy methods and the orthometric height (H)$$ (H) $$ found using geoid undulation (N)$$ (N) $$. The aim of this study was to determine a local geoid using the kriging, local polynomial (LP), and inverse distance to a power (IDP) interpolation methods along with the random forest (RF) regression method and to compare the performance. For the application, 193 Global Navigation Satellite System (GNSS)/leveling points homogeneously distributed over the study area were selected as reference data, and 70 GNSS/leveling points were selected as test data. The results were compared in terms of accuracy using well‐known performance metrics, namely, the root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2) values. According to the results, the highest R2 and the lowest RMSE and MAE values were obtained with the RF regression method. These findings demonstrated the superiority of the RF regression over the classic interpolation methods applied in the local geoid determination for the study area.

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“…Aydin and Yurdakul [38] separated countries in groups via clustering and then calculated the efficiency of how countries responded to COVID-19 in each cluster with DEA. Finally, Thaker et al [39] employed DEA to evaluate the efficiency of Indian banks and then used Random Forest Regression to analyze the impact of corporate governance (and other bank characteristics) on the calculated efficiencies. Consequently, combining DEA with ML offers an alternative approach to the issue of inputs and outputs selection.…”

Section: Introductionmentioning

confidence: 99%

An Exploratory DEA and Machine Learning Framework for the Evaluation and Analysis of Sustainability Composite Indicators in the EU

2022

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One method that has been proposed for the measurement of sustainability is Data Envelopment Analysis (DEA). Despite its advantages, the method has limitations: First, the efficiency of Decision-Making Units is calculated with weights that are favorable to themselves, which might be unrealistic, and second, it cannot account for different perceptions of sustainability; since there is not an established and unified definition, each analyst can use different data and variations that produce different results. The purpose of the current paper is twofold: (a) to propose an alternative, multi-dimensional DEA model that handles weight flexibility using a different metric (an alternative optimization criterion) and (b) the inclusion of a computational stage that attempts to incorporate different perceptions in the measurement of sustainability and integrates machine learning to explore country sustainability composite indices under different perceptions and assumptions. This approach offers insights in areas such as feature selection and increases the trust in the results by exploiting an inclusive approach to the calculations. The method is used to calculate the sustainability of the 28 EU countries.

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A DEA and random forest regression approach to studying bank efficiency and corporate governance

Cited by 49 publications

References 54 publications

A non-parametric framework for evaluating governance–efficiency–productivity associations in commercial banking

A non-parametric framework for evaluating governance–efficiency–productivity associations in commercial banking

Prediction of geoid undulations: Random forest versus classic interpolation techniques

An Exploratory DEA and Machine Learning Framework for the Evaluation and Analysis of Sustainability Composite Indicators in the EU

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