摘要翻译：
我们使用监督学习来识别预测美国股票市场最大减持横截面的因素。我们的数据从1980年1月到2018年6月，我们的分析包括普通最小二乘、惩罚线性回归、基于树的模型和神经网络。我们发现最重要的预测因子在模型之间趋于一致，非线性模型比线性模型有更好的预测能力。平静期的预测能力高于压力期，环境、社会和治理指标增强了非线性模型的预测能力。
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英文标题：
《Sustainable Investing and the Cross-Section of Maximum Drawdown》
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作者：
Lisa R. Goldberg and Saad Mouti
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最新提交年份：
2019
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分类信息：

一级分类：Quantitative Finance        数量金融学
二级分类：Statistical Finance        统计金融
分类描述：Statistical, econometric and econophysics analyses with applications to financial markets and economic data
统计、计量经济学和经济物理学分析及其在金融市场和经济数据中的应用
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一级分类：Economics        经济学
二级分类：Econometrics        计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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英文摘要：
  We use supervised learning to identify factors that predict the cross-section of maximum drawdown for stocks in the US equity market. Our data run from January 1980 to June 2018 and our analysis includes ordinary least squares, penalized linear regressions, tree-based models, and neural networks. We find that the most important predictors tended to be consistent across models, and that non-linear models had better predictive power than linear models. Predictive power was higher in calm periods than stressed periods, and environmental, social, and governance indicators augmented predictive power for non-linear models.
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PDF链接：
https://arxiv.org/pdf/1905.05237