The Correlation of ESG Ratings and Abnormal Returns: An Event Study Using Machine Learning

Dominic Strube, Christian Daase

2023

Abstract

This short study uses machine learning (ML) to investigate whether the inclusion of sustainability ratings in the training data can improve the estimated accuracy of the prediction of a company’s abnormal returns. For this purpose, we examined 72 companies that are listed in the indices EURO STOXX 50 ® or/and EURO STOXX 50 ® ESG or/and EURO STOXX® ESG LEADERS 50. We found out that the mean-adjustment model used to estimate returns produces more accurate results than with adding MSCI’s sustainability ratings. The preliminary results suggest that sustainability ratings are currently inappropriate for estimating expected or abnormal returns and their inclusion in the training data interferes the algorithm behind the ML approach. By extension, this leads to the assumption that the relation between ESG ratings and a business’ success are suitably irregular to significantly decrease an ML models quality.

Paper Citation

in Harvard Style

Strube D. and Daase C. (2023). The Correlation of ESG Ratings and Abnormal Returns: An Event Study Using Machine Learning. In Proceedings of the 5th International Conference on Finance, Economics, Management and IT Business - Volume 1: FEMIB, ISBN 978-989-758-646-0, SciTePress, pages 76-81. DOI: 10.5220/0011841700003494

in Bibtex Style

@conference{femib23,
author={Dominic Strube and Christian Daase},
title={The Correlation of ESG Ratings and Abnormal Returns: An Event Study Using Machine Learning},
booktitle={Proceedings of the 5th International Conference on Finance, Economics, Management and IT Business - Volume 1: FEMIB,},
year={2023},
pages={76-81},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011841700003494},
isbn={978-989-758-646-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 5th International Conference on Finance, Economics, Management and IT Business - Volume 1: FEMIB,
TI - The Correlation of ESG Ratings and Abnormal Returns: An Event Study Using Machine Learning
SN - 978-989-758-646-0
AU - Strube D.
AU - Daase C.
PY - 2023
SP - 76
EP - 81
DO - 10.5220/0011841700003494
PB - SciTePress