Style Rotation Revisited

Style rotation strategies have enjoyed growing interest in the academic and practitioner communities over the last decades. This study investigates the ability of innovative modeling approaches to effectively forecast equity style performance. Single - multifactor Logit models and several machine learning techniques are employed to generate directional style spread forecasts. Their efficacy is assessed both in a statistical and economic evaluation context. The analysis reveals that certain univariate Logit models and machine learning techniques, such as Naïve Bayes, Bagging, Bayes GLM, Discriminant Analysis models, and KNN, enhance the accuracy of the generated forecasts and lead to profitable investment strategies.