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Link to original content: https://doi.org/10.1145/3383455.3422529

Quantifying ESG alpha using scholar big data | Proceedings of the First ACM International Conference on AI in Finance

research-article

Quantifying ESG alpha using scholar big data: an automated machine learning approach

Authors:

Xiao-Yang LiuAuthors Info & Claims

ICAIF '20: Proceedings of the First ACM International Conference on AI in Finance

Article No.: 41, Pages 1 - 8

https://doi.org/10.1145/3383455.3422529

Published: 07 October 2021 Publication History

Abstract

ESG (Environmental, social and governance) alpha strategy that makes sustainable investment has gained popularity among investors. The ESG fields of study in scholar big data is a valuable alternative data that reflects a company's long-term ESG commitment. However, it is considered a difficulty to quantitatively measure a company's ESG premium and its impact to the company's stock price using scholar big data. In this paper, we utilize ESG scholar data as alternative data to develop an automatic trading strategy and propose a practical machine learning approach to quantify the ESG premium of a company and capture the ESG alpha. First, we construct our ESG investment universe and apply feature engineering on the companies' ESG scholar data from the Microsoft Academic Graph database. Then, we train six complementary machine learning models using a combination of financial indicators and ESG scholar data features and employ an ensemble method to predict stock prices and automatically set up portfolio allocation. Finally, we manage our portfolio, trade and rebalance the portfolio allocation monthly using predicted stock prices. We backtest our ESG alpha strategy and compare its performance with benchmarks. The proposed ESG alpha strategy achieves a cumulative return of 2,154.4% during the backtesting period of ten years, which significantly outperforms the NASDAQ-100 index's 397.4% and S&P 500's 226.9%. The traditional financial indicators results in only 1,443.7%, thus our scholar data-based ESG alpha strategy is better at capturing ESG premium than traditional financial indicators.

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Cited By

Bhandari HPokhrel NRimal RDahal KRimal B(2024)Implementation of deep learning models in predicting ESG index volatilityFinancial Innovation10.1186/s40854-023-00604-010:1Online publication date: 8-Jul-2024
https://doi.org/10.1186/s40854-023-00604-0
Alonso-Robisco ABas JCarbo Jde Juan AMarques J(2024)Where and how machine learning plays a role in climate finance researchJournal of Sustainable Finance & Investment10.1080/20430795.2024.2370325(1-42)Online publication date: 30-Jun-2024
https://doi.org/10.1080/20430795.2024.2370325
Zou JLou JWang BLiu S(2024)A novel Deep Reinforcement Learning based automated stock trading system using cascaded LSTM networksExpert Systems with Applications10.1016/j.eswa.2023.122801242(122801)Online publication date: May-2024
https://doi.org/10.1016/j.eswa.2023.122801
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Index Terms

Quantifying ESG alpha using scholar big data: an automated machine learning approach
1. Computing methodologies
  1. Machine learning

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cover image ACM Conferences

ICAIF '20: Proceedings of the First ACM International Conference on AI in Finance

October 2020

422 pages

ISBN:9781450375849

DOI:10.1145/3383455

Conference Chair:
Tucker Balch
J.P. Morgan AI Research

Copyright © 2020 ACM.

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Published: 07 October 2021

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ICAIF '20: ACM International Conference on AI in Finance

October 15 - 16, 2020

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Cited By

Bhandari HPokhrel NRimal RDahal KRimal B(2024)Implementation of deep learning models in predicting ESG index volatilityFinancial Innovation10.1186/s40854-023-00604-010:1Online publication date: 8-Jul-2024
https://doi.org/10.1186/s40854-023-00604-0
Alonso-Robisco ABas JCarbo Jde Juan AMarques J(2024)Where and how machine learning plays a role in climate finance researchJournal of Sustainable Finance & Investment10.1080/20430795.2024.2370325(1-42)Online publication date: 30-Jun-2024
https://doi.org/10.1080/20430795.2024.2370325
Zou JLou JWang BLiu S(2024)A novel Deep Reinforcement Learning based automated stock trading system using cascaded LSTM networksExpert Systems with Applications10.1016/j.eswa.2023.122801242(122801)Online publication date: May-2024
https://doi.org/10.1016/j.eswa.2023.122801
Liu XXia ZYang HGao JZha DZhu MWang CWang ZGuo J(2024)Dynamic datasets and market environments for financial reinforcement learningMachine Language10.1007/s10994-023-06511-w113:5(2795-2839)Online publication date: 26-Feb-2024
https://dl.acm.org/doi/10.1007/s10994-023-06511-w
Lim T(2024)Environmental, social, and governance (ESG) and artificial intelligence in finance: State-of-the-art and research takeawaysArtificial Intelligence Review10.1007/s10462-024-10708-357:4Online publication date: 28-Feb-2024
https://doi.org/10.1007/s10462-024-10708-3
Panya TKhamkong M(2024)Deep Reinforcement Learning for Automated of Asian Stocks TradingApplications of Optimal Transport to Economics and Related Topics10.1007/978-3-031-67770-0_37(539-554)Online publication date: 10-Nov-2024
https://doi.org/10.1007/978-3-031-67770-0_37
Lee JKim M(2023)ESG information extraction with cross-sectoral and multi-source adaptation based on domain-tuned language modelsExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.119726221:COnline publication date: 1-Jul-2023
https://dl.acm.org/doi/10.1016/j.eswa.2023.119726
Liu XXia ZRui JGao JYang HZhu MWang CWang ZGuo JKoyejo SMohamed SAgarwal ABelgrave DCho KOh A(2022)FinRL-metaProceedings of the 36th International Conference on Neural Information Processing Systems10.5555/3600270.3600404(1835-1849)Online publication date: 28-Nov-2022
https://dl.acm.org/doi/10.5555/3600270.3600404
Lee OJoo HChoi HCheon M(2022)Proposing an Integrated Approach to Analyzing ESG Data via Machine Learning and Deep Learning AlgorithmsSustainability10.3390/su1414874514:14(8745)Online publication date: 18-Jul-2022
https://doi.org/10.3390/su14148745

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