The demand for transparent, rules-based investing has been one of the secular trends in the asset management industry over recent decades. Within the passive investment arena, alternatively weighted—or smart beta—strategies have witnessed remarkable growth and have amassed over USD 497 billion1 of assets in exchange-traded products alone.
In their broadest sense, smart beta strategies can refer to a swath of strategies that are designed to provide access to a wide array of returnenhancing risk premia (or risk factors). Usually, return derived from these factors is unaccounted for by the basic capital asset pricing model, which embraces market beta as the sole measure of compensated risk. As of yet, there is no agreement on a definitive list of these risk premia in academia or in industry, and this remains the object of continued research and discussion. We have therefore elected to focus only on those factors for which empirical evidence exists to suggest that the factors may provide risk-adjusted returns in multiple markets and over the long run; namely, volatility, momentum, quality, growth, value, dividend yield, and size.
One may reasonably expect that methodologies for different indices that target the same factors would be similar, and that any divergence between indices would elicit interest in only academic circles rather than from practitioners or investors. Yet, index construction techniques can be poles apart. Where some techniques select a subset of securities with the most demonstration of the desired factor, others elect to vary only the weighting of the constituents within the entire universe so as to reflect the degree of the targeted exposure the companies satisfying the factor criteria exhibit.
Weighting schemes can also be dissimilar. Some approaches emphasize tradability by associating the weighting scheme with market capitalization, while others weight by the intended factor exposure alone. These seemingly minute distinctions in index construction can lead to considerably distinguishable portfolios that have differential drivers of risk and return and unequal exposures to factor and sector biases. They can likewise have an effect on the macroeconomic environments in which the portfolios perform, and this is a matter of particular importance in multi-factor portfolios where a number of factors are blended.
In the light of this, we will review some typical strategies that seek to track the more common factors (i.e., those mentioned above) in the U.S. market in order to understand better the characteristics of these strategies. With the aid of a risk model, Section 1 begins by examining both the primary and secondary exposures, the source of active historical return, and the risk of smart beta strategies used to access the seven factors mentioned above. Then, Section 2 analyzes the macroeconomic environments in which the various strategies outperformed. Finally, Section 3 undertakes the foregoing analysis on four stylized multi-factor portfolios that are constructed based on a number of weighting schemes.