ESG Performance Prediction and Driver Factor Mining for Listed Companies Based on Machine Learning: A Multi-Source Heterogeneous Data Fusion Analysis
Year: 2025
Type: article
Abstract: With the acceleration of global economic integration and the growing focus on sustainable development, Environmental, Social, and Governance (ESG) factors have become key standards for evaluating a company's long-term value and risk. However, accurately measuring the ESG performance of listed companies and identifying the underlying driving factors remains a significant challenge. This paper proposes a Transformer-based multi-source heterogeneous data fusion model, MSformer, which analyzes diverse data, including financial reports, news, social media comments, and government announcements. It categorizes the data into three types: time-series structured data, time-series structured mapped data, and textual data. The model enhances feature extraction using the Spatial Frequency-coordinated Attention Mechanism (SFHA) and employs Support Vector Regression (SVR) for prediction. Experimental results show that MSformer outperforms other advanced models, achieving an outstanding 87.4% multi-class accuracy and 0.517 average prediction error, proving its effectiveness and advantage in ESG prediction.
DOI: 10.70088/tmzjct41
Author: Weiyan Tan
Language: English
Subfield: Strategy and Management
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W4409258968