Big Data-Driven ESG Quantitative Investment Strategy

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.