Proposing an Integrated Approach to Analyzing ESG Data via Machine Learning and Deep Learning Algorithms
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Cited by:
- Jérémi Assael & Laurent Carlier & Damien Challet, 2023.
"Dissecting the Explanatory Power of ESG Features on Equity Returns by Sector, Capitalization, and Year with Interpretable Machine Learning,"
JRFM, MDPI, vol. 16(3), pages 1-22, March.
- J'er'emi Assael & Laurent Carlier & Damien Challet, 2022. "Dissecting the explanatory power of ESG features on equity returns by sector, capitalization, and year with interpretable machine learning," Papers 2201.04393, arXiv.org, revised Apr 2023.
- Jérémi Assael & Laurent Carlier & Damien Challet, 2023. "Dissecting the explanatory power of ESG features on equity returns by sector, capitalization, and year with interpretable machine learning," Post-Print hal-03791538, HAL.
- Archana Saxena & Rajesh Singh & Anita Gehlot & Shaik Vaseem Akram & Bhekisipho Twala & Aman Singh & Elisabeth Caro Montero & Neeraj Priyadarshi, 2022. "Technologies Empowered Environmental, Social, and Governance (ESG): An Industry 4.0 Landscape," Sustainability, MDPI, vol. 15(1), pages 1-17, December.
- Xuesong Tian & Yuping Zou & Xin Wang & Minglang Tseng & Hua Li & Huijuan Zhang, 2022. "Improving the Efficiency and Sustainability of Intelligent Electricity Inspection: IMFO-ELM Algorithm for Load Forecasting," Sustainability, MDPI, vol. 14(21), pages 1-19, October.
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Keywords
ESG; machine learning; deep learning; COVID-19; data analysis; data science;All these keywords.
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