Scientific Beta

There are concerns that, as Smart Beta strategies gain popularity, flows into these strategies will ultimately cancel out their benefits. However, such claims are rarely based on solid empirical evidence. The academic literature has not only documented risk premia for the standard factors but has also provided theoretical explanations for persistence, notably if factors are compensation for taking on additional types of risk. Moreover, precautions against crowding risks can be taken by proper implementation of factor investing and Smart Beta indices. In particular, the best precaution against crowding seems to be diversification.

There are concerns that, as Smart Beta strategies gain popularity, flows into these strategies will ultimately cancel out their benefits. However, such claims are rarely based on solid empirical evidence. The academic literature has not only documented risk premia for the standard factors but has also provided theoretical explanations for persistence, notably if factors are compensation for taking on additional types of risk. Moreover, precautions against crowding risks can be taken by proper implementation of factor investing and Smart Beta indices. In particular, the best precaution against crowding seems to be diversification.

Of course, it is possible that Smart Beta and factor strategies can be subject to adverse effects due to a wide following but one can only conclude that this is the case if there is evidence for it. Losses in a given strategy, meanwhile, are not evidence of crowding. Periodic underperformance may be due to normal fluctuations in prices. In fact, claiming that there must be crowding in a factor because it suffers from losses completely ignores the nature of risk premia. A risk premium corresponds to a higher average return that is the compensation for taking on additional risk. Therefore, losses to any factor strategy over any particular period do not imply that the long-term premium has disappeared because of “crowding”. Such losses may simply suggest that the reward for holding the factor comes with associated risk. Indeed, evidences from the academic literature show that the risk premia of these strategies have not disappeared after publication. Moreover, the effect of increased investments on price-impact costs can be significantly reduced by adopting proper investability rules.

The confusion about factor crowding can have negative consequences for investors, leading them to invest in novel exotic factors which, in the end, are not rewarded and expose them to heightened data-mining risks. In fact, exotic new factors are typically justified on the basis of short-term data and use proprietary complex scoring approaches to claim that such factors are less replicable and therefore less prone to crowding. A necessary consequence is that these factors are often over-fitted and will not be robust out of sample.