NoÃ«l Amenc of the Edhec-Risk Institute examines risk in smart beta indices and argues that not enough information is made available for investors to assess products.
There has been more talk about the role of beta in asset management recently.
From our viewpoint, this is part of an evolution in asset management that perhaps goes further than the growing momentum towards passive investment for cost reasons or doubts over active managers’ fees.
The success of smart beta with institutional investors largely outstrips the initial framework that was established for it, namely that of replacing the natural passive investment reference represented by cap-weighted indices.
The reason behind the new indices for the vast majority of investors, and doubtless their promoters, is probably the superiority of their performance compared to traditional cap-weighted indices. Everyone agrees that while cap-weighted indices are the best representation of the market, they do not necessarily constitute an efficient benchmark that can be used as a reference for an informed investor’s strategic allocation.
Alternative beta, advanced beta and smart beta are responses from the market to a question that has formed the basis of modern portfolio theory since the work of the Nobel Prize winner Harry Markowitz: how to construct an optimally diversified portfolio.
Each solution contains risks – systematic risks and specific risks.
EXPOSED TO BIAS
Systematic risks come from the fact that new indices or benchmarks can be more or less exposed to particular risk factors depending on the methodological choices presiding over their construction.
For example, a construction scheme that favours indicators of the firm’s economic size leads more often than not to the index being exposed to style biases; in the same way, a scheme that favours low volatility stocks will lead to the benchmark being overexposed to some sectors.
In the area of systematic risk, much thought has been given in recent years to the quality of construction of smart beta benchmarks.
The first generation of smart beta benchmarks, and notably the indices constructed from the stocks’ economic characteristics, such as fundamental indices, are embedded solutions which do not distinguish the stock picking methodology from the weighting methodology. As such, they oblige the investor to be exposed to particular systematic risks which represent the very source of their performance.
The second generation of smart beta clearly distinguishes between the stock selection and weighting phases. In doing so, it enables investors to choose the risks they want or do not want to be exposed to.
This choice of risk is expressed firstly by a very specific and controlled definition of the investment universe. An investor wishing to avail of a better diversified benchmark than a cap-weighted index but little inclined to take on liquidity risk can decide to apply this scheme solely to a very liquid selection of stocks.
In the same way, an investor who does not want the diversification of his benchmark to lead him to favour stocks with a value bias can absolutely decide that the diversification method that he chooses will only be applied to growth – or at least not strictly value – stocks.
In recent research, we have been able to show that the distinction between the selection and weighting phases, which can be made for most smart beta construction methods, could add value both in terms of performance and in controlling the investment risks.
Moreover, if the goal of investment in smart beta is to outperform cap-weighted market indices, we should note that this goal is exactly the same as that of a benchmarked active manager.
In this approach, it seems logical to take account of the tracking error of these new benchmarks with regard to cap-weighted indices, not only to establish an equivalent comparison between them, but also to control the risk of these new benchmarks under-performing the market indices, and all the more so in that this risk of underperformance has been shown to exist.
Edhec-Risk has shown that owing to very different exposures to the main equity risk factors, smart beta indices could significantly underperform market indices in the short term, and sometimes even in the medium term.
Consequently, investors in smart beta indices will have to be sure to have not only perfect information on the different risk factors to which the strategy is exposed, but also the implementation tools to ensure that these risk factors are controlled.
In this context, controlling the tracking error and the choice of stocks in the indices is ultimately an essential tool for managing the systematic risks of these new indices.
The second type of risk to which an investor is exposed when he uses a benchmark is the risk that is specific to the construction of that benchmark.
Whatever the weighting scheme envisaged, it relies on modelling and on the parameter estimation, which obviously always leads to the risk of a lack of out-of-sample robustness.
Any investor who strays from a weighting scheme for which the assumptions that determine the construction are highly criticisable and not proven, and whose outputs are hardly compatible with the definition of a well-diversified portfolio, will probably take a good risk in the sense that there is a strong probability of doing better in the long term.
However, by moving away from the consensus, from the default option constituted by the cap-weighted indices, this investor will be questioned on the relevance of the new model chosen and the evaluation of the robustness of the past performance that will probably underpin his choice to a large degree.
In this sense, like in the area of systematic risk, every informed smart beta investor will have to be clear-sighted and carry out sound due diligence to evaluate the specific risk and weigh the past performance of the index against that of the model with its robustness.
Ultimately, we observe that investing in smart beta springs from the same logic and the same approach as choosing a manager.
Investment in smart beta presupposes measurement of the systematic risk factors and their integration, not only in absolute terms to evaluate the real risk-adjusted performance created by better diversification of the benchmark, but also in relative terms to limit the tracking error risk and therefore the risk of underperformance in comparison with the cap-weighted index.
These statistical analyses should then be completed by thorough due diligence on the specific risk represented, not by the manager here, but by the diversification model and the implementation rules
In our opinion, this similarity in the investment processes poses a major question about the minimal level of information that the smart beta investor should possess in order to evaluate genuine performance and risks. In the area of smart beta indices one is forced to conclude that the situation is currently inadequate.
In a forthcoming study, Edhec shows that access to data on the performance, composition and risk of smart beta indices is restricted or costly. As far as the model and the implementation rules are concerned, the desire to protect proprietary know-how and the profusion of ad-hoc schemes whose conditions of optimality and robustness are highly inexplicit makes the analysis of the specific risks of smart beta difficult, not to say impossible.
Moreover, while research traditionally helps to increase the knowledge of market participants, in the area of smart beta, and despite the abundance of publications, we consider that this research is not currently contributing to an improvement in the efficiency of the smart beta market.
Even worse, most of the articles that are positioned as objective comparisons of different forms of smart beta are written by index promoters or asset managers who sell one strategy, or a very small number of strategies. Unsurprisingly, these comparison articles conclude on the objective superiority of the solution(s) marketed by the authors.
Here too, smart beta market practices are no different from those of active managers who like to choose the periods of comparison with their peers.
Noël Amenc is professor of finance, Edhec Business School, and director, Edhec-Risk Institute
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