摘 要
随着全球可持续发展理念的深入推广，将环境、社会和治理（ESG）因素纳入投资决策已成为金融领域的重要趋势。然而，传统量化投资策略往往侧重于数据驱动的收益优化，较少考虑可持续性目标，这为研究两者的结合提供了重要背景与契机。本研究旨在探索量化投资策略与可持续投资理念的有效融合，通过构建兼具财务回报与社会责任的综合投资框架，弥补现有研究中两者割裂的不足。为此，本文采用多因子模型为基础，引入ESG评分作为额外因子，并结合机器学习算法对因子权重进行动态调整，从而实现对资产组合的精准优化。研究选取了2015年至2022年间全球主要市场的股票数据进行实证分析，结果表明，在量化模型中加入ESG因子不仅能够显著降低投资组合的风险水平，还能在长期中提升超额收益表现。此外，动态调整机制进一步增强了模型的适应性与稳健性。
关键词：量化投资 ESG因子 机器学习
Abstract
With the deepening of the global concept of sustainable development, incorporating environmental, social and governance (ESG) factors into investment decisions has become an important trend in the financial field. However, traditional quantitative investment strategies often focus on data-driven return optimization, focusing less on sustainability goals, which provides an important background and opportunity for the study of the combination of the two. The study aims to explore the effective integration of quantitative investment strategy and sustainable investment concept, and make up for the shortcomings of the existing research by building a comprehensive investment fr amework with both financial return and social responsibility. To this end, this paper adopts multi-factor model, introduces ESG score as an additional factor, and combines machine learning algorithm to dynamically adjust the factor weight, so as to realize accurate optimization of asset portfolio. The study selected the stock data of major global markets from 2015 to 2022 for empirical analysis, and the results showed that adding ESG factor to the quantitative model can not only significantly reduce the risk level of the portfolio, but also improve the performance of excess returns in the long term. Moreover, the dynamic adjustment mechanism further enhances the adaptability and robustness of the model.
Keyword:Quantitative Investment Esg Factor Machine Learning
目 录
引言 1
1量化投资策略的基础理论 1
1.1量化投资的定义与特征 1
1.2量化投资的核心方法论 2
1.3数据驱动的投资决策框架 2
1.4量化模型在投资中的应用 3
2可持续投资的核心理念与实践 3
2.1可持续投资的基本概念 3
2.2ESG因素在投资中的作用 4
2.3可持续投资的主要模式分析 4
2.4可持续投资的风险与收益评估 5
3量化投资与可持续投资的结合路径 5
3.1量化技术在ESG数据处理中的应用 5
3.2构建可持续投资的量化指标体系 6
3.3量化模型对可持续投资的优化支持 6
3.4结合案例：量化与可持续投资的实证分析 6
4融合策略的挑战与未来发展 7
4.1数据质量与可用性问题探讨 7
4.2技术局限性及其改进方向 7
4.3法规与政策对融合策略的影响 8
4.4未来研究方向与潜在突破点 8
结论 8
参考文献 10
致谢 11
