The outperformance of dynamic smart beta portfolios over equal-weighted index : emphasis on Value, Momentum and Low Volatility risk factors

Abstract

The latest advancements in factor investing and the plethora of academic literature have clearly implied the existence of factor risk premia over time. Since the early 00’s, factor investing findings have been offered the possibility to get admission to the desired factor tilt with effective diversification through what are called today “smart beta indices”. These cost-effective vehicles allowed many investors to access easily risk factors and hence diversify their portfolios with different risk exposures. This affected in part active portfolio managers that demand higher fees for their active investment strategies and therefore these alternative vehicles could be perceived as direct competitors for active managers. To struggle against these cost-effective vehicles, active managers have shown a growing interest in timing these strategies, not to compete directly against smart betas, but to use a dynamic allocation and create extra abnormal return by actively allocate to these smart beta indices.

The purpose of this thesis is to recreate a Macroeconomic model (valuation spread), a Fundamental data model (economic variables) and a Statistical factor model (machine learning) for value, momentum and low volatility smart beta portfolios over a European equal-weighted index in a 10-year period starting in 2006 to verify which strategy could better enhance performances by timing the performances as well as accurately diminish drawdown and volatility.

The results confirm the success of the active allocation to smart beta factors portfolios, which provided enhanced results proving it is profitable to allocate dynamically these factors based on conditional assumptions. Momentum risk factor showed in average the best performances over the three methods. While among the different approaches, the statistical method proved to be the approach with the higher risk-adjusted performances when compared to the other methods but still has to be confirmed since turnover costs were not computed and performances could be affected.