Beta Forecasting With Realized Beta Estimators and Machine Learning Algorithms

This paper applies machine learning algorithms to the modeling of realized betas for the purposes of forecasting stock systematic risk. Higher levels of beta forecast accuracy are demonstrated, relative to other studies in the literature. These improvements are also highly significant, both statistically and economically. The approaches include individual machine learning algorithms and forecast combinations with multiple machine learning algorithms and realized betas. Forecast combinations lead to the highest level of beta forecast accuracy. The machine learning algorithms employed are ridge regression, decision tree learning, adaptive boosting, gradient boosting, random forests, and neural networks.