Scientific Beta

This ‘Scientific Beta’ special issue presents research that has been developed by ERI Scientific Beta, an EDHEC-Risk Institute entity that aims to help investors understand and invest in advanced beta equity strategies, notably looking at smart beta diversification indices, the risks of smart beta indices, smart beta allocation, and the conditional performance and robustness of smart beta strategies.

One of the key points in the choice of investors to call cap-weighted indices into question as an investment benchmark is their poor diversification. It therefore seems logical for this first edition of the Scientific Beta supplement devoted to smart beta to begin with smart beta diversification indices.

Indeed, diversification strategy indices address the limitations of cap-weighted indices, such as their high concentration levels (in weight or risk contributions) or inefficient return-to-risk profiles. In our first article we examine five such diversification strategy indices (maximum deconcentration, diversified risk parity, maximum decorrelation, efficient minimum volatility and efficient maximum Sharpe) and draw the relevant conclusions for investors.

Since the performance of any investment cannot be dissociated from the risks taken, we then address the question of the risks of smart beta indices and the customisation of those risks. Clearly, alternative weighting schemes carry significant risks, in absolute terms and relative to their cap-weighted reference index. Since departing from the traditional cap-weighting portfolio construction will lead to different risk return profiles, as well as a specific set of risk exposures, investors need to be aware of the risks they bear when making the smart beta choice.

We subsequently turn to the subject of smart beta allocation. Although the asset management industry has traditionally been divided into passive and active management, this distinction has been fading away recently and smart beta indices can be applied in both areas. We particularly focus on the benefits to be gained from a diversified allocation to a variety of different smart beta benchmarks. This diversification can be a genuine added-value contribution from asset managers, who too often see smart beta as a threat and not as an opportunity for active management.

We then analyse the conditional performance of smart beta strategies. Apart from showing that portfolios that perform best in bull markets are riskier and have lower Sharpe ratios than portfolios that perform best in bear markets, our study demonstrates that the contrasting characteristics of portfolios that perform best in bull/bear markets could work in favour of the investors if they are mixed equally into a single portfolio.

An analysis of the specific risks of diversification strategies looks specifically at the strategies that were presented in the article on diversification strategy indices. Again we see that a combination of these different strategies will allow the risks that are specific to each strategy to be diversified by exploiting the imperfect correlation between the different strategies’ parameter estimation errors and the differences in their underlying optimality assumptions.

Finally, we examine the robustness of smart beta strategies. Since these strategies are for the most part recent, there is little in the way of live historical track records. Informing investors of the risk and return drivers of the strategies, ie, the risk factors they are exposed to, will allow them to control for risk exposures that drive returns in order to extract the substance of the chosen weighting scheme instead of having inconsistent results through time.