论文标题
通过可解释的机器学习来剖析ESG功能在股权回报，大写和年度的股权回报上的解释能力
Dissecting the explanatory power of ESG features on equity returns by sector, capitalization, and year with interpretable machine learning
论文作者
论文摘要
我们系统地研究了欧洲股票市场的价格回报与环境，社会和治理（ESG）分数之间的联系。使用可解释的机器学习，我们检查了ESG分数是否可以解释经典股权因素（尤其是市场）所无法解释的价格回报的一部分。我们提出了一个跨验证方案，具有随机的公司验证，以减轻ESG数据的数量和质量的相对初始缺乏，这使我们能够使用大多数最新和最佳数据来训练和验证我们的模型。梯度增强模型成功地解释了市场因素未考虑的年度价格收益的一部分。我们使用基准功能来核对ESG数据，与仅基本基本功能相比，ESG数据的价格回报要好得多。最相关的ESG得分编码争议。最后，我们发现更好的ESG分数对小型和大型资本化公司的价格回报的相反影响：更好的ESG分数通常与后者的价格回报较大，而对前者则相反。
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.
