Seminar 217, Risk Management: Sustainable Responsible Investing and the Cross-Section of Return and Risk

Seminar | February 19 | 11 a.m.-12:30 p.m. | 1011 Evans Hall

 Speakers: Saad Mouti, UC Berkeley

 Consortium for Data Analytics in Risk

The identification of factors that predict the cross-section of stock returns has been a focus of asset pricing theory for decades. We address this challenging problem for both equity performance and risk, the latter through the maximum drawdown measure. We test a variety of regression-based models used in the field of supervised learning including penalized linear regression, tree-based models, and neural networks. Using empirical data in the US market from January 1980 to June 2018, we find that a number of firm characteristics succeed in explaining the cross-sectional variation of active returns and maximum drawdown, and that the latter has substantially better predictability. Non-linear models materially add to the predictive power of linear models. Finally, environmental, social, and governance impact enhances predictive power for non-linear models when the number of variables is reduced.