Big Data-Driven ESG Quantitative Investment Strategy

Bingxing Wang

doi:10.70393/6a6574626d.323837

OPEN ACCESS |Research Article ||17 April 2025

Big Data-Driven ESG Quantitative Investment Strategy

Bingxing Wang

Shanghai Jingzhuo Investment Management Co., Ltd.

stellawang6262@foxmail.com

Shanghai Jingzhuo Investment Management Co., Ltd., China.

* Corresponding Author¹: Bingxing Wang, E-Mail: stellawang6262@foxmail.com

Publication

Accepted 2025 April 15 ; Published 2025 April 17

Journal of Economic Theory and Business Management, 2025, 2(2), 3006-4953.

Abstract

With sustainability becoming more important worldwide, investors are looking more closely at environmental, social, and governance (ESG) factors. This paper looks at how big data could help investors use ESG information effectively in quantitative investing. It discusses how the use of big data techniques can lead to more accurate and transparent ESG analyses. Using regression models, the study identifies a positive relationship between companies' ESG scores and their expected stock returns. It also illustrates how detailed big data analysis can enrich the evaluation of corporate ESG performance. Despite these advantages, the practical use of such methods still faces several significant hurdles. Data quality issues, a lack of standardized ESG metrics, and dynamic market conditions can undermine model accuracy and stability. To address these obstacles, we propose improved data cleaning procedures, the promotion of industry-wide ESG standards, and enhancements to model adaptability. Looking forward, new technologies such as artificial intelligence and blockchain are likely to help ESG investing become simpler and more efficient. Using these tools can make processing ESG data faster and clearer, giving investors stronger support when making decisions. In general, applying big data in ESG investing can help promote sustainability in financial markets and may also offer steady, long-term returns. Yet, there’s still uncertainty about how easily these technologies can actually be used in practice.

Keywords

Big Data , ESG Investment , Quantitative Investment Strategy , Regression Analysis , Machine Learning , Data Analysis , Sustainable Investment .

Metadata

DOI:

10.70393/6a6574626d.323837

ARK:

ark:/40704/JETBM.v2n2a02

Pages: 8-13

References: 15

Disciplines: Finance

Subjects: Investment Banking

Cite This Article

APA Style

Wang, B. (2025). Big data-driven esg quantitative investment strategy. Journal of Economic Theory and Business Management, 2(2), 8-13. https://doi.org/10.70393/6a6574626d.323837

Acknowledgments

The authors thank the editor and anonymous reviewers for their helpful comments and valuable suggestions.

FUNDING

Not applicable.

INSTITUTIONAL REVIEW BOARD STATEMENT

Not applicable.

DATA AVAILABILITY STATEMENT

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

INFORMED CONSENT STATEMENT

Not applicable.

CONFLICT OF INTEREST

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

AUTHOR CONTRIBUTIONS

Not applicable.

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Copyright © 2025 The Author(s). Published by Southern United Academy of Sciences.
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