This study has two primary objectives. Firstly, it enhances the reliability and transparency of machine-learning-based models for predicting future changes in environmental, social and governance (ESG) performance. Secondly, it explores the relationship between ex-ante expected changes in ESG and future stock returns. This study collects 3258 STOXX Europe 600 firm-year observations. In the ESG prediction phase, two machine learning algorithms (logistic regression and random forest) are utilised to develop ESG forecasting models. Hyperparameter optimisation and walk-forward validation techniques are employed to address issues of underestimation and information leakage. The machine-learning-based ESG forecasting models are evaluated using three metrics: accuracy, area under the curve (AUC) and area under the precision-recall curve (AUPR). Subsequently, this study investigates the relationship between ex-ante expected ESG changes and future stock returns using the predicted ESG changes. A positive correlation is found between ex-ante expected ESG changes and future stock returns. The supplementary tests also reveal that this positive relationship is highly and statistically significant among large firms and after the COVID-19 pandemic. Moreover, this study introduces a robust and transparent approach for constructing effective machine-learning-based ESG forecasting models using hyperparameter optimisation and walk-forward validation. Additionally, traditional regression analyses are modernised by incorporating machine-learning-predicted independent variables. Furthermore, the findings provide empirical support for stakeholder, agency and resource-based theories. Finally, practical insights are provided to facilitate ESG-focused investment portfolio decision making.

中文翻译：

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基于机器学习的 ESG 和未来股票回报的事前预期变化


这项研究有两个主要目标。首先，它提高了基于机器学习的模型的可靠性和透明度，用于预测环境、社会和治理（ESG）绩效的未来变化。其次，探讨ESG事前预期变化与未来股票回报之间的关系。本研究收集了 3258 份 STOXX Europe 600 公司年度观察结果。在ESG预测阶段，利用两种机器学习算法（逻辑回归和随机森林）来开发ESG预测模型。采用超参数优化和前向验证技术来解决低估和信息泄漏问题。基于机器学习的 ESG 预测模型使用三个指标进行评估：准确性、曲线下面积 (AUC) 和精确召回曲线下面积 (AUPR)。随后，本研究利用预测的 ESG 变化来调查事前预期 ESG 变化与未来股票回报之间的关系。事前预期 ESG 变化与未来股票回报之间存在正相关关系。补充测试还表明，这种正相关关系在大公司中以及在 COVID-19 大流行之后具有高度的统计显着性。此外，本研究引入了一种稳健且透明的方法，使用超参数优化和前向验证构建有效的基于机器学习的 ESG 预测模型。此外，通过纳入机器学习预测的自变量，传统的回归分析变得现代化。此外，研究结果为利益相关者、机构和基于资源的理论提供了实证支持。最后，提供实用的见解，以促进以 ESG 为中心的投资组合决策。