Is Factors Timing Overrated

Abstract

In this paper we investigate two factor rotation approaches, performed directly on the MSCI smart beta indices that are Value, Quality, Momentum, Low Volatility and Size, over US and European Markets. Both approaches use the same indicators built on a macroeconomic signal (PMI), a market sentiment signal based on (VIX, credit spreads), and a momentum signal (time-series, cross-sectional). While the first approach is rule-based and mostly inspired by already known factor rotation frameworks, our work explores those by using our own specifications and it also seeks to check whether a style rotation works at the indices level. Our results show that our framework outperforms a simple equal-weight factor exposure in spite of application of transaction costs. On a stand-alone basis the PMI based rotation fol- lowed by the time-series momentum exhibit the strongest returns. Then we explore if machine learning techniques (tree-based) outperform equal-weight and the rule based strategies particularily after counting for transaction costs.