Smart Beta or Alternative Beta, one of the hottest topics in recent years, has been drawing investors’ attention and confronting them at the same time with a flow of information which is often difficult to analyse. After almost ten years on the rise for smart beta products, stepping back from this major shift in the industry towards investment methodologies documented in academia appears essential in order to distinguish future needs and advances.
Cap-weighted indices have long been a reference for investors, but certain inefficiencies have propelled smart beta indices into the fore as an alternative over this reference. The reasons for this are two-fold: 1) the fact that smart beta indices can capture a desired risk premium through smart stock selection and/or 2) the fact that they diversify away idiosyncratic risk by using an adequate weighting scheme. Therefore, these indices represent an attractive substitute to standard cap-weighted indices and are hence exploited by the asset management industry. From this arises a multitude of investment possibilities, making investing in smart beta products challenging and raising the questions of what should be taken into account when investing in smart beta products and what can be done to further improve investment solutions.
Since Fama and French have introduced their three factor model in 1993, research on factors has been growing tremendously, defining hundreds of factors that could be rewarded and explain equity performance. Motivated by these findings, the industry has started providing tradable smart factor products, such as ETFs, in order to answer investors’ growing demand.
However, only few factors receive a wide consensus from academic researchers. In particular, there are debates on some specific factors, questioning the sources of their risk premia. As an illustration Keim (1983) has shown that the size premium is actually a calendar anomaly, this premium being weak except during the month of January. Fama and French’s five factor model (2015) raises another notable case, since adding profitability and investment to the initial three factor model leads to the conclusion that value is redundant, whereas it was defined as a key factor in their three factor model. Thus, a fundamental conclusion is that investors should ignore controversial factors and focus on smart factors with both clear economic rationale and empirical support.
Moreover, depending on how a factor index is defined, its targeted risk premium can be more or less significantly captured. As a result the risk premia described in the literature are not easily reached in practice. Cazalet and Roncalli (2014) list reasons for this discrepancy. One is the fact that factors used in the literature often do not take into account capacity constraints or rebalancing frequencies observed in the real investment world. These constraints imply costs that can alter the risk premium, as explained by the authors. They also point out the fact that, in practice, small stocks are not held in portfolio because they bear a liquidity risk that managers cannot afford. Consequently factors compositions and their premia differ from those of the theoretical factors. As Cazalet and Roncalli (2014) further explain, another source of difference is the weighting scheme used to concretely build factor indices, as it can greatly impact their returns and volatilities.
One more issue raised by the authors is the fact that long-short factors, on which most empirical research evidence is based, and long-only factors, which are commonly used in practice, are not equivalent and result in factors which may differ on some aspects. This is reflected by the work of Israel and Moskowitz (2013) who show that long-only value and momentum portfolios capture only part of these factors’ risk premia. These issues have to be addressed by index providers, but they also matter to investors who should distinguish between theory and practice when choosing the right smart beta product.
Finally, as Lo (2015) noticed “smart beta is often accompanied by dumb sigma”, highlighting that investors often forget about risk management when they invest in smart beta products. Smart beta indices are not a perfect solution providing high returns and low volatility worldwide. They differentiate themselves from cap-weighted indices by their periods of outperformance, which are known to occur under specific market conditions. But they also experience periods of underperformance, which investors should be aware of in order to manage the associated risk.
To this effect Bender, Briand, Melas, Subramanian and Subramanian (2013), Amenc, Goltz, Lodh and Martellini (2014) and Blitz, Huij, Lansdorp and Van Vliet (2014) introduce multi-beta portfolios, which combine different factors into one index in order to achieve unconditional performance. Such studies pave the way to a large number of investment possibilities that can be tailored to the specific needs of investors.
In the same vein Lo (2015) proposes a dynamic index fund which controls the volatility level by dynamically allocating monies to an index and cash, where the index can be a smart factor. Such products prove that it is possible to combine passive investing in smart betas and active risk management.
This application can be transformed into an investment in a multi-beta portfolio and a second asset selected according to investor-specific preferences. For investors seeking absolute risk control, the second asset should be cash, whereas it could be a benchmark index for investors looking for relative risk control with respect to a specific index. To sum up, associating smart beta to risk management aims to access both performance and risk control in a transparent and easily adjustable way.
Amenc, N., Goltz, F., Lodh, A., & Martellini, L. (2014). Towards Smart Equity Factor Indices: Harvesting Risk Premia without Taking Unrewarded Risks. Journal of Portfolio Management, 40(4).
Bender, J., Briand, R., Melas, D., Subramanian, R. A., & Subramanian, M. (2013). Deploying multi-factor index allocations in institutional portfolios. MSCI Research Insights.
Blitz, D., Huij, J., Lansdorp, S., & van Vliet, P. (2014). Efficient Factor Investing Strategies. Robeco.
Cazalet, Z., & Roncalli, T. (2014). Facts and fantasies about factor investing. Available at SSRN 2524547.
Fama, E. F., & French, K. R. (1993). Common risk factors in the returns on stocks and bonds. Journal of Financial Economics, 33(1), 3-56.
Fama, E. F., & French, K. R. (2015). A five-factor asset pricing model. Journal of Financial Economics, 116(1), 1-22.
Israel, R., & Moskowitz, T. J. (2013). The role of shorting, firm size, and time on market anomalies. Journal of Financial Economics, 108(2), 275-301.
Keim, D. B. (1983). Size-related anomalies and stock return seasonality: Further empirical evidence. Journal of Financial Economics, 12(1), 13-32.
Lo, Andrew W. (2015). What is an Index? Available at SSRN 2672755