Risk Reduction in Style Rotation

We investigate the potential improvement in the implementation of style rotation strategies by techniques addressing estimation errors. We select two approaches that have recently stood out in the statistics and econometric literature and have been applied to portfolio construction literature. One builds on regularization methods which address estimation error by focusing on the weights of the constructed portfolios. And a second method that uses pooled forecasts obtained across different observation windows. Thus it focuses on minimizing estimation error in the moments of the return distribution that may arise due to structural breaks. We conclude that overall there are benefits from departing from naïve approaches which can be as significant as an improvement in the Information Ratio of about 54%, i.e. from 0.65 (naïve) to about 1 (dynamic)