Machine learning style rotation – evidence from the Johannesburg Stock Exchange

Abstract

This study evaluates näive and advanced prediction models when applied to style rotation strategies on the Johannesburg Stock Exchange ('JSE'). We apply 1- and 3-month style momentum as näive predictors against three tree-based machine learning ('ML') algorithms (advanced predictors), namely Random Forest, XGBoost and LightGBM. Additionally, the study corrects for a shortcoming in the literature by incorporating trading costs into back-tested portfolio sorts. The results of the study are threefold. First, style rotation strategies based on advanced predictors achieve superior risk-adjusted returns when compared to näive momentum. Of the three ML models applied, XGboost is superior, followed by LightGBM, implying that gradient boosters are superior to less advanced ensemble methods (Random Forest) which are in-turn superior to style momentum. Second, short-term momentum results in the highest share turnover across style rotation strategies, resulting in the largest negative impact associated with trading costs. Third, contrary to similar studies, the incorporation of price momentum as an independent variable in factor spanning tests renders most time-series alphas statistically insignificant. This study considers the application of machine learning ("ML") for style rotation. The results indicate that ML based rotation signals generate excess performance relative to momentum based style rotation when applied on a cross-section of emerging market (South African) listed equities.