通过可解释机器学习按行业、市值和年份剖析ESG特征对股票收益的解释力
Dissecting the explanatory power of ESG features on equity returns by sector, capitalization, and year with interpretable machine learning
AI总结：
本研究利用可解释机器学习分析欧洲股票市场，发现ESG评分能显著解释市场因子未覆盖的价格收益，且对大小市值公司的收益影响相反。
摘要：
我们系统地研究了欧洲股票市场中价格收益与环境、社会和治理（ESG）评分之间的联系。使用可解释机器学习，我们检验了ESG评分能否解释经典股票因子（尤其是市场因子）未能解释的那部分价格收益。我们提出了一种基于公司随机验证的交叉验证方案，以缓解ESG数据在数量和质量上相对不足的问题，这使我们能够使用大部分最新且最优的数据来训练和验证模型。Gradient boosting模型成功解释了市场因子未能解释的年度价格收益部分。我们通过基准特征检验发现，与仅使用基础基本面特征相比，ESG数据对价格收益的解释效果显著更好。最相关的ESG评分编码了争议事件。最后，我们发现更好的ESG评分对大市值和小市值公司的价格收益具有相反的影响：更好的ESG评分通常与大市值公司更高的价格收益相关，而对小市值公司则相反。
英文摘要：
We systematically investigate the links between price returns and Environment, Social and Governance (ESG) scores in the European equity market. Using interpretable machine learning, we examine whether ESG scores can explain the part of price returns not accounted for by classic equity factors, especially the market one. We propose a cross-validation scheme with random company-wise validation to mitigate the relative initial lack of quantity and quality of ESG data, which allows us to use most of the latest and best data to both train and validate our models. Gradient boosting models successfully explain the part of annual price returns not accounted for by the market factor. We check with benchmark features that ESG data explain significantly better price returns than basic fundamental features alone. The most relevant ESG score encodes controversies. Finally, we find the opposite effects of better ESG scores on the price returns of small and large capitalization companies: better ESG scores are generally associated with larger price returns for the latter and reversely for the former.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
