The enhanced benefits of ESG in portfolios: A multi-factor model perspective based on LightGBM

Xiaomin Gong, Fei Xie, Zhongsheng Zhou and Chenyang Zhang

Pacific-Basin Finance Journal, 2024, vol. 85, issue C

Abstract: This paper explores the enhanced benefits of ESG in portfolio models from the perspective of a multi-factor prediction model. Firstly, using the traditional Barra multi-factor model as a basis, the Fama-Macbeth test is employed to evaluate the explanatory power of the multi-factor model after incorporating the ESG factor. Considering the non-linear nature of ESG factors, the Light Gradient Boosting Machine (LightGBM) model is utilized for feature identification and optimization, thereby enhancing the accuracy of predicted returns in the multi-factor model. Secondly, two portfolio models, namely Barra+ESG+MV and Barra+ESG+BL models, are constructed by introducing the predicted returns based on Barra and ESG factors into Mean-Variance (MV) and Black-Litterman (BL) models, respectively. This facilitates a further analysis of the benefits of ESG in asset portfolios. Finally, the advanced BPZ model is used to examine the robustness of the gain effect attributed to the environmental (E), social (S), and corporate governance (G) factors in ESG from a non-linear perspective. The findings reveal that ESG significantly improves portfolio performance, particularly in terms of reducing systematic risk. Furthermore, the E, S, and G factors bring universal enhancement effect to the multi-factor model. This study provides new avenues for research on ESG in portfolio management.

Keywords: ESG; LightGBM model; Black-Litterman model; BPZ method (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0927538X24001161
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:pacfin:v:85:y:2024:i:c:s0927538x24001161

DOI: 10.1016/j.pacfin.2024.102365

Access Statistics for this article

Pacific-Basin Finance Journal is currently edited by K. Chan and S. Ghon Rhee

More articles in Pacific-Basin Finance Journal from Elsevier
Bibliographic data for series maintained by Catherine Liu ().