2021
DOI: 10.1007/s10479-021-04366-9
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An ensemble machine learning approach for forecasting credit risk of agricultural SMEs’ investments in agriculture 4.0 through supply chain finance

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Cited by 45 publications
(26 citation statements)
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References 71 publications
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“…5 We argue that for big data samples such as in our case, censoring or truncating efficient observations (with DEA scores greater than or equal to 1) from the prediction model may result in missing information, and their predictive power is accordingly weaker. We therefore support the ML literature (e.g., Belhadi et al, 2021;Tsai & Chen, 2010;Zhu et al, 2021) in confirming that the ML approach is superior to the econometric approach, and that our hybrid DEA-ML model is more efficient and more accurate than the traditional ones. Consequently, we conclude that LASSO regression is the best model of the ML approaches for predicting the efficiency of Vietnamese MSMEs.…”
Section: Second-stage Analytics: Predicting the Vietnamese Msmes' Per...supporting
confidence: 85%
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“…5 We argue that for big data samples such as in our case, censoring or truncating efficient observations (with DEA scores greater than or equal to 1) from the prediction model may result in missing information, and their predictive power is accordingly weaker. We therefore support the ML literature (e.g., Belhadi et al, 2021;Tsai & Chen, 2010;Zhu et al, 2021) in confirming that the ML approach is superior to the econometric approach, and that our hybrid DEA-ML model is more efficient and more accurate than the traditional ones. Consequently, we conclude that LASSO regression is the best model of the ML approaches for predicting the efficiency of Vietnamese MSMEs.…”
Section: Second-stage Analytics: Predicting the Vietnamese Msmes' Per...supporting
confidence: 85%
“…Regarding the DEA approach, one could apply the variable returns to scale assumption (Banker, 1984), the cost/profit measures (Ngo et al, 2019b;Pilar et al, 2018), the fuzzy approach (Boubaker et al, 2020;Nandy & Singh, 2021), or the Euclidean distance (Hammami et al, 2020) to measure the DEA efficiency in different settings. Regarding ML analytics, the ensemble approach (Belhadi et al, 2021) and other hybrid ML combinations (e.g., combining LASSO and NN) could also be used. Finally, the extension of this CSW-RA-DEA to other industries with big data, such as banking and finance (Tsai & Chen, 2010), healthcare (Misiunas et al, 2016), agriculture (Nandy & Singh, 2021), or energy (Khezrimotlagh et al, 2019), could help increase our understanding of the role of DEA in such fields.…”
Section: Discussionmentioning
confidence: 99%
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“…Machine learning has been used in various financial applications, such as behavioral prediction, price modeling, algorithmic trading, portfolio management, fraud detection, customer churn, investor sentiment analysis, and credit risk prediction (Dixon et al 2020;Renault 2020;Belhadi et al 2021). Bankruptcy prediction, cryptocurrency volatility prediction, clustering causes of death in insurance-related data (Bett et al 2022), predict farmers' uptake of crop insurance (Mare et al 2022), and business sustainability have also been modeled using machine learning techniques (Bouri et al 2021a;Barboza et al 2017;Kipkogei et al 2021).…”
Section: Applications Of Machine Learning In Related Areasmentioning
confidence: 99%
“…The form of supply chain management requires special consideration (Sharma, Shishodia, Kamble, Gunasekaran, & Belhadi, 2020;Belhadi, Kamble, Mani, Benkhati, & Touriki, 2021). The need for an approach in the corn supply chain in Central Java Province, which is expected to provide an overview of the availability of corn supply as a consideration for the management of the corn supply chain in delivering products from producers to consumers.…”
Section: Introductionmentioning
confidence: 99%