A1142
Title: Equity market-neutral strategies using variable selection and regularized regression
Authors: Federico Severino - Universite Laval (Canada) [presenting]
Marzia Cremona - Universite Laval (Canada)
Charles-Edouard Sarault - Universite Laval (Canada)
Abstract: Equity market-neutral strategies are designed to feature no exposure to market risk. The implementation of such strategies relies upon the estimation of the beta coefficients. Unfortunately, traditional beta estimation methods used to implement these strategies often suffer from weak out-of-sample performance, leading to suboptimal ex-post neutrality. Therefore, it is explored how machine learning techniques, particularly variable selection and regularized regression methods, can address the issues faced in the traditional development of equity market-neutral strategies. A range of methods is tested, including ridge regression, lasso regression, and stepwise regressions, to construct portfolios that achieve better ex-post neutrality and lower transaction costs when compared to strategies based on traditional multiple regression models. The results demonstrate that the tested techniques enhance portfolio performance and help minimize trading costs by selecting an optimal number of risk factors to hedge. Research contributes to the academic literature on machine learning in asset pricing and offers practical insights for portfolio management.
