A Multifactored Approach To Smart Beta

June 30, 2014

Taking factor investing to the next level

[This article originally appeared in the July/August issue of Journal of Indexes.]

Sophisticated institutional investors have increasingly started to review factor-based equity investment strategies. For example, the parliament of Norway, which acts as a trustee for the Norwegian Oil Fund,1 commissioned a report on the investment returns of the fund. This report was requested after the fund's performance fell short of the performance of popular equity market benchmarks. The resulting report (Ang, Goetzmann and Schaefer [2009]) showed that the returns relative to a cap-weighted benchmark of the fund's actively managed portfolio can be explained by exposure to a set of well-documented alternative risk factors. After taking into account such exposures, active management did not have any meaningful impact on the risk and return of the portfolio. The authors argue that such exposures can be obtained through purely systematic strategies without a need to rely on active management. Therefore, rather than simply observing the factor tilts brought by active managers ex-post, investors may consider which factors they wish to tilt toward and make explicit decisions on these tilts. This discussion of active managers' sources of outperformance has naturally led to factor indexes being considered as a more cost-efficient, straightforward and transparent way of implementing such factor tilts. Investors need to ask three main questions when considering such factor-based equity investing strategies.

The first question investors face when wanting to benefit from factor investing is to determine which factors to select. To avoid the pitfalls of nonpersistent factor premia and achieve robust performance, investors should keep the following checks in mind. First, they should require a sound economic rationale for the existence and persistence of a positive premium. Second, due to the risks of data-mining, investors would be well advised to stick to simple factor definitions that are widely used in the literature rather than rely on complex and proprietary factor definitions (Van Gelderen and Huij [2013]).

However, having access to a proxy for a factor is hardly relevant if the investable proxy only gives access to a fraction of the fair reward per unit of risk to be expected from the factor exposure because of the presence of unrewarded risks—due to excessive concentration, for instance. A second relevant question is thus how to best extract the premium for a factor in an efficient way. Amenc et al. [2014a] address this question in detail. The authors present how the smart beta 2.0 approach (Amenc et al. [2013])—the main idea of which is to apply a smart-weighting scheme to an explicit selection of stocks—allows factor indexes to be built that are not only exposed to the desired risk factors but also avoid being exposed to unrewarded risks. This approach, referred to as "smart factor indexes" can be summarized in a nutshell as follows: The explicit selection of stocks provides the desired tilt, e.g., the beta, while the smart-weighting scheme addresses concentration issues and diversifies away specific and unrewarded risks.

A third question is how to allocate across a number of different risk factors to come up with an overall allocation that suits the investor's objectives and constraints. While it is beyond the scope of this paper to provide an exhaustive framework for factor allocation, we illustrate the use of factor indexes in two different allocation contexts, one aiming at improving absolute risk-adjusted returns, and one targeting relative risk objectives.

In what follows, we provide practical illustrations of multifactor allocations drawing on smart-factor indexes, representing a set of four well-documented and popular risk factors: value; momentum; low volatility; and size. To be more specific, we will use the diversified multistrategy approach,2 which combines five different diversification-based weighting schemes in equal proportions so as to diversify away unrewarded risks and parameter estimation errors (Kan and Zhou [2007], Amenc et al. [2012a]).3

 

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