ESG Scores Predict Stock Performance in the Technology Sector

DOI

10.2991/978-2-38476-585-0_24How to use a DOI?

Keywords

ESG scores; Stock return prediction; Technology sector

Abstract

Recently, Environmental, Social, and Governance (ESG) has become increasingly important in investment decisions. As a result, many investors are questioning whether ESG scores can help predict stock performance, especially in the technology sector. This study investigates whether ESG scores can enhance short-term return prediction for technology firms by using Bloomberg data from 2020 to 2025. This study applied Extreme Gradient Boosting (XGBoost) and Random Forest models to predict 20-day returns by combining ESG data with technical indicators. The results showed weak predictive performance with low accuracy and negative R2 values. Ordinary Least Squares (OLS) regression revealed some significant ESG components, but their effects were inconsistent. Shapley Additive Explanations (SHAP) analysis confirmed ESG features had minimal impact compared to past return data. These findings suggest that traditional ESG scores may lack short-term predictive value. Future research should explore sentiment-based ESG measures, avoid inconsistency between different rating agencies, and improve model design to capture more meaningful insights.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Jinglin Pei
PY  - 2026
DA  - 2026/06/18
TI  - ESG Scores Predict Stock Performance in the Technology Sector
BT  - Proceedings of the 2025 International Conference on Hybrid Commerce, Human Capital, and Economic Dynamics (ICHCH 2025)
PB  - Atlantis Press
SP  - 203
EP  - 212
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-2-38476-585-0_24
DO  - 10.2991/978-2-38476-585-0_24
ID  - Pei2026
ER  -