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Timing the Factor Zoo via Deep Learning: Evidence from China
... As noted above, the vast majority of studies, 19, were conducted in US markets, followed by Global with 5, see Figure 7. More recently, two studies have been dedicated to China by Ma et al. (2023) andLi et al. (2023), which have become seminal studies for this region. Figure 8 shows the databases used by the 25 studies. ...
This study evaluates naïve 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 naïve 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 naïve 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.
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