Browsing by Author "Manita R"

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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 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.