Predicting ESG ratings: a comparative analysis of machine learning models

Gaier Ribeiro, Joao Pedro (A.A. 2022/2023) Predicting ESG ratings: a comparative analysis of machine learning models. Tesi di Laurea in Machine learning for finance, Luiss Guido Carli, relatore Megha Patnaik, pp. 71. [Master's Degree Thesis]

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Abstract/Index

Literature review. CSR influence on firm performance. CSR role in company resilience. CSR impact in strategy and competitive advantage. Predicting ESG scores using companies’ financial information. Data. ESG ratings. Construction of sample and variables. Summary statistics. Methodology. Generalized linear mixed effects. Random forest. XGboost. Support vector machine.

References

Bibliografia: pp. 66-71.

Thesis Type: Master's Degree Thesis
Institution: Luiss Guido Carli
Degree Program: Master's Degree Programs > Master's Degree Program in Economics and Finance (LM-56)
Chair: Machine learning for finance
Thesis Supervisor: Patnaik, Megha
Thesis Co-Supervisor: Morelli, Giacomo
Academic Year: 2022/2023
Session: Extraordinary
Deposited by: Alessandro Perfetti
Date Deposited: 09 Jul 2024 08:08
Last Modified: 09 Jul 2024 08:08
URI: https://tesi.luiss.it/id/eprint/39214

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