Indices & Benchmarks - April 02, 2013

Smart Beta 2.0 - an interview with NoŽl Amenc

On the occasion of the publication of its Smart Beta 2.0 research, EDHEC-Risk Institute wishes to draw the attention of investors to sound management of the risks of investing in smart beta indices. Interview with NoŽl Amenc, Professor of Finance, Director of EDHEC-Risk Institute, CEO of ERI Scientific Beta and co-author of the Smart Beta 2.0 position paper.

NoŽl Amenc

Smart beta is a very popular concept with institutional investors today. What is the nature of this market?

NoŽl Amenc: In recent years, in both the United States and Europe, there has been increasing talk of the pre-eminence of beta in asset management. As such, in two studies published by EDHEC-Risk Institute analysing new index offerings and investor reactions in both Europe1 and North America2, these investors provide evidence of their increasing appetite for passive investment and their interest in new forms of indexation referred to as advanced or smart beta. More than 40% of investors have already adopted alternative weighting schemes and over 50% see their current cap-weighted indices as problematic.

From our viewpoint, this dual interest 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 managersí capacity to justify their management fees by producing significant and consistent alpha.

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. For one thing, it is easy to observe that cap-weighted indices have no equivalent when it comes to representing market movements; for another, it is equally plain to see that they remain the simple reference understood by all investors and stakeholders in the investment industry. In the end, even the biggest critics of cap-weighted indices constantly refer to cap-weighted indices to evaluate the performance of their new 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 investorís strategic allocation. In other words, they do not constitute a starting point (for active investment) or an end point (for passive investment) that offers, through its diversification, a fair reward for the risks taken by the investor. Alternative beta, also known as advanced beta or smart beta is therefore a response from the market to a question that forms the basis of modern portfolio theory since the work of the Nobel Prize winner Harry Markowitz: How is an optimally diversified portfolio constructed?

Why then talk of Smart Beta 2.0? What is the meaning of this idea of creating a distinction within the smart beta approach?

NoŽl Amenc: From the moment that smart beta indices are marketed for their outperformance with respect to cap-weighted indices and are often, as we have observed in our surveys, used by the end institutional investors as substitutes for benchmarked active managers, it seems fairly logical to pose the question of their risk of underperformance compared to these cap-weighted indices.

And these risks of underperformance exist. In Spring 2012, we published research in the Journal of Portfolio Management3 which showed that popular smart beta indices could have maximum relative drawdown compared to their cap-weighted equivalent of more than 10% and periods of relative underperformance of longer than one year.

Indeed, as with any technique or any model, implementation of these new forms of benchmarks is not risk-free. In order to justify why cap-weighted indices are no longer considered good benchmarks, smart beta promoters raise their risks of concentration, and rightly so, but it is also necessary to grasp the risks to which investors are exposed when they adopt alternative benchmarks.

Talking about the superiority of smart beta indices over the long term is totally legitimate, but it is also perfectly legitimate to discuss the sources of this outperformance, the risks of the outperformance not being robust, or even the conditions of underperformance in the short or medium term.

This is one of the objectives of what EDHEC-Risk Institute calls the Smart Beta 2.0 approach. This new vision of smart beta investment, which over the past three years has been subject to a considerable research effort on the part of the Institute, ultimately aims to allow investors to control the risk of investment in smart beta indices so as to benefit fully from their performance.

How can the risks of these smart beta indices be qualified?

NoŽl Amenc: Like any portfolio construction strategy, smart beta indices are exposed to systematic risks, which correspond to the fact that the security selection and/or weighting method proposed lead to distancing from the risks of the cap-weighted index in favour of pronounced exposure to other risk factors. By construction, every strategy for deconcentrating the benchmark with respect to a cap-weighted index, whose principle is to take account solely of the generally free-float-adjusted capitalisation, in favour of a scheme relying on other weighting criteria, will naturally lead to exposing the portfolio to size or liquidity factors. With the same logic, systematically selecting and weighting stocks on the basis of micro-economic characteristics such as the value of dividends, earnings or book value, which often characterise value-investing-type approaches, is bound to expose the smart beta index to a value risk factor.

Obviously, the exposure to systematic risks subjects the first-generation smart beta indices to performance variations that often mean that the live performances can be quite different from the simulated performance when the index is launched. It is not really a question of the robustness of the index construction method, but simply the consequences of an exceedingly natural phenomenon on the markets, namely the result of the variation in risk premia. As such, for example, the Value style will not always outperform the Growth style; in high-momentum markets the contrarian effects of equally-weighted approaches are not rewarded: when there is stress on liquidity or an increase in credit risk, small capitalisations underperform large capitalisations, etc. It is only by measuring and controlling these exposures to risk factors that investors can be assured of the robustness of the performance of the smart beta index.

Another risk, well documented in the academic and practitioner literature on portfolio management, but fairly infrequently mentioned in the documentation associated with smart beta indices, is the one that corresponds to the very quality of the weighting of the stocks, which we can call the specific risk of the smart beta strategy under consideration.

Whatever the weighting scheme envisaged, it relies on modelling and on parameter estimation, which obviously always leads to a risk of a lack of out-of-sample robustness. Any investor who strays from a weighting scheme such as capitalisation weighting, for which the assumptions that determine the construction are largely open to criticism and not proven, and whose outputs are hardly compatible with the definition of a well-diversified portfolio, will probably take a well-rewarded risk, in the sense that there is a strong probability of doing better in the long term4. 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 on the robustness of the past performance that will probably underpin their 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 risks rather than rely only on an assessment of the past performance of the index.

We believe that the specific risk dimension should be better taken into account in the choices that investors make in the area of smart beta. Too often investors stop at performances that are composed over fairly short periods, or are the fruit of simulated track records. There is no reason to criticise this situation in itself, because firstly the track records are often limited by the availability of data, and secondly, since they were created recently, smart beta indices cannot exhibit live performance over the long term. Nonetheless, this weakness in the statistics should logically lead investors to analyse the robustness conditions of the performance displayed.

In the area of specific risk, two competing effects, namely parameter estimation risk and optimality risk, should be taken into account.

The first effect is related to the fact that, naturally, any estimation of a parameter is dependent on the sample observation of the parameter. All the progress in financial econometrics and portfolio management in the last thirty years has made the estimation of these parameters more robust through statistical techniques and factor models, but the fact remains that a risk of poor estimation of the parameters exists. It may be that, in order to simplify the marketing of the index, no real precaution is taken to control the sensitivity of the stock weightings, and therefore the future performance of the benchmark, to the estimation of the parameters. For example, we have shown in our research that the first-generation fundamentally-weighted indices are highly sensitive to the choice of measure of the accounting parameters used in the construction methods. In the same way, trying to estimate the non-convergent parameters like the expected returns, as is the case in the naÔve approaches to the construction of Max Sharpe Ratio benchmarks, can only lead to disappointment with the out-of-sample performance of these indices.

In this context, one first natural approach to addressing the concern over sensitivity to errors in parameter estimates consists of improving parameter estimates, typically by imposing some structure on the statistical problem so as to alleviate the reliance on pure sample-based information. It is in this area that the research in financial econometrics has led to the most progress, whether it involves reducing the dimensionality of the set of parameters to be estimated (robust estimation of the variance-covariance matrices) or having less sample-dependent estimators to take account of the dynamics of their variation (GARCH model for example). In particular, expected returns and risk parameters can be inferred from an asset pricing model such as Sharpe's (1964) CAPM or Fama and Frenchís (1993) three factor model. In this context, one needs to estimate the sensitivity to each asset with respect to the systematic factors, as well as the expected return and volatility of the factors, which typically involves (for parsimonious factor models and large portfolios) a dramatic reduction in the number of parameters to estimate, and consequently an improvement in the accuracy of each parameter estimate. The key trade-off, however, is between model risk, namely the risk of using the wrong asset pricing model, e.g., using a single-factor model while the true data generating process originates from a multi-factor model, and sample risk involved in purely relying on sample-based information with no prior on the prevailing asset pricing model.

A second approach to the challenge posed by sensitivity of portfolio optimisation procedures to errors in parameter estimates consists of ignoring parameter estimates by using an objective different from Sharpe ratio maximisation that requires fewer, if any, parameter estimates. For example, one may decide to use a cap-weighted portfolio or an equally-weighted portfolio, which requires no information about the risk and return characteristic of the portfolio constituents.

Other strategies such as global minimum variance, risk parity or maximum diversification strategies, to name a few, solely rely on risk parameter estimates, thus avoiding the risk of using improper estimates for expected returns. In order for the methodologies proposed by the promoters of indices to be robust, i.e., to allow long-term outperformance over cap-weighted indices, the selection or weighting model that it describes must not be dictated by an in-sample choice but correspond to realistic assumptions, or at least not overly depend on a determined period, but have an explanatory value out-of-sample. Within the framework of modern portfolio theory, the realism or relevance of the assumptions that underlie the model are often appreciated through the concept of optimality. In that case, it involves understanding how a particular portfolio diversification weighting scheme is situated in comparison with the optimal portfolio constituted by the Maximum Sharpe Ratio (MSR) and we wish to stress the importance for investors of paying attention to the assumption of optimality of the weighting model proposed.

In other words, giving up on (some) parameter estimates, as opposed to trying to improve parameter estimates, implies an efficiency cost related to the use of a portfolio that is a priori suboptimal, since it only coincides with the MSR portfolio under what are sometimes heroic assumptions. This is what we call optimality risk. For example, a Global Minimum Variance (GMV) portfolio will coincide with an MSR portfolio if expected returns happen to be identical for all stocks, which is hardly a reasonable assumption.

On the other hand, ignoring parameter estimates might intuitively be a reasonable approach in the presence of an overwhelming amount of estimation risk even for investors using improved parameter estimates. For example, DeMiguel et al. (2007) argue that mean-variance optimisation procedures do not consistently outperform, from an out-of-sample Sharpe ratio perspective, naÔve equally-weighted portfolio strategies. Similarly, GMV portfolios typically outperform MSR portfolios based on sample-based parameter estimates from an out-of-sample risk-adjusted perspective. However, the risk remains for the investor of selecting the model or investment strategy with a substantial efficiency cost.

Here too the Smart Beta 2.0 approach aims to measure and control the specific risk components of these new benchmarks. In the area of specific risk it is important for investors to appreciate and validate the value of the trade-off constituted by both the choice of non-optimal benchmark construction methods, which reduces the scope of the parameters to be estimated in comparison with what would be required to construct an optimal MSR portfolio (optimality risk), and the parameter estimation risks avoided through this choice of benchmark construction method.

Ultimately, informed investors who adopt the Smart Beta 2.0 approach can very clearly limit the specific risk of their smart beta indexation not only through an enlightened choice of benchmark construction method but also through a capacity to diversify this specific risk when it is well documented, as we show in our research.

In concrete terms, how are the risks of smart beta strategies controlled?

NoŽl Amenc: The starting point for the Smart Beta 2.0 approach is to distinguish the smart beta strategy from its representation in an index. When we talk about the specific risks of a smart beta strategy, we often confuse the index that was designed by the index provider and the portfolio construction method that underlies the index. If an index provider decides to expose their index to risk factors that were probably well rewarded in the past, it is a choice, and not inevitable. As such, there is no obligation for a smart beta index based on a GMV or equal-weighted (EW)-type method to be exposed to Value or liquidity risk. It is a choice, or sometimes something that the index provider lets the investor do. The Smart Beta 2.0 approach clearly distinguishes the choice of systematic risks from the choice of portfolio construction method, and therefore of the specific risk of the smart beta index.

In the area of the choice, and therefore the control, of systematic risks, we can distinguish between two types of complementary methods.

In the first place, a very simple method based on stock selection, which avoids the stock universe on which the weighting scheme is going to be applied, containing undesired risks. As such, a smart beta index using an EW method which is applied to a very liquid selection of stocks cannot by construction be exposed to liquidity risk, but an index created from a fairly large universe using the same weighting scheme would be.

This type of approach, which is very simple to implement and to understand, was the subject of research published in Fall 2012 in the Journal of Portfolio Management5. We show there that the control, or even the removal, of systematic risk factors by restricting the universe is not an obstacle to the performance of smart beta indices. The benefits of good benchmark diversification mean that cap-weighted indices can be outperformed without taking relatively excessive systematic risks.

A second risk control method consists of integrating arithmetic or statistical constraints in the very framework of the weighting scheme implementation in the form of a constrained optimisation. This approach, which is well known to all portfolio managers, enables for example absolute or relative neutrality to be guaranteed with respect to the cap-weighted indices for certain risk factors. It is highly complementary to the first method. If we imagine guaranteeing the liquidity of an index through an optimisation constraint, then we can also imagine ensuring style neutrality by defining a constraint within the framework of the optimisation programme.

Whatever the techniques for controlling systematic risks and the methods that aim to reduce specific risks, the risk of underperforming cap-weighted indices, even if it is reduced, remains, and all the more so in that controlling the risk factors does not necessarily mean that the systematic risk factors are aligned with those of the cap-weighted index. One can therefore envisage protecting the smart beta investment against a risk of extreme tracking error. At first sight, since it involves an index, it seems curious to speak of tracking error with respect to another index, but given that a large number of investors use their smart beta to outperform cap-weighted indices, it seems fairly logical to implement explicit control of the risk of underperformance. In this area too, the research conducted shows that this type of approach gives good results, notably in terms of improving the regularity of the outperformance of the smart beta benchmark. Here too, the results that we published in the Journal of Portfolio Management in Spring 2012 are interesting. It is absolutely possible to respect a tracking error budget and to outperform the reference cap-weighted index.

These techniques drawn from the portfolio management world are not new, so why are we speaking about new index construction approaches?

NoŽl Amenc: What is new is not indeed the implementation of these well-known, proven techniques which are sometimes partly present in the Smart Beta 1.0 index offerings. What is innovative, or even revolutionary, is that the risks to which the Smart Beta 2.0 indices are exposed are no longer the result of the index providerís decision (or non-decision), but of the investorís choice.

Implementation of these choices leads to smart beta index risk customisation, which constitutes genuine progress in implementing informed investing for smart beta strategies.

Moreover, itís because this revolution needs to be done that EDHEC-Risk Institute has decided to launch in 2013 an index platform constructed on the principles of the Smart Beta 2.0 approach, called Scientific Beta. It involves enabling investors to construct their own benchmark, i.e. to avail of smart beta indices that correspond to the choice of risks that they have made. We hope that this initiative will be genuinely stimulating for the index market because it was difficult to imagine that while passive investment was becoming more and more popular, nothing was being offered to manage the risks of investing in these new forms of indices in satisfactory transparency and cost conditions. From now on, with the Scientific Beta platform that is opening on April 15, what seemed to us to be an anomaly is in the process of being resolved.

When EDHEC-Risk Institute talks about smart beta, it only relates to equity, even though numerous smart beta offerings are being developed in the fixed-income space. What explains this relative silence?

NoŽl Amenc: Research that we have conducted has clearly demonstrated the limits of traditional debt-weighted index offerings, which are not justified by any genuine economic thinking or model. Whether for sovereign or corporate debt, EDHEC-Risk Institute considers that there is room for improvement.

To date, the weighting schemes available on the market or documented in practitioner journals have not convinced us of their robustness and pertinence.

Several topics need to be highlighted in relation to smart beta indices, concerning both the methods based on accounting or economic data, and those based on more quantitative approaches.

The so-called fundamental-weighting approaches aggravate a risk that is well-known in all ad hoc approaches that are not supported by academic consensus, that of the ex post choice of weighting criteria which enhances simulated performance whilst providing no guarantee of the out-of-sample robustness of the performance.

Furthermore, although credit risk is a risk evaluated by the market, by not using the information available and instead simply trusting accounting information, this presents a strong backward-looking bias, and it could appear to be a paradox to criticise rating schemes for their lack of credit risk predictability whilst trusting a historical average of an accounting value to correct the bias.

The difficulties of optimisation approaches in the field of fixed-income should not be under-estimated either. It is surprising to note that numerous smart beta fixed-income providers use models that were first used in the equity universe even though the context of use of these models is very different. For example, the estimation of a co-variance matrix in the fixed-income universe is particularly difficult due to the non-stationarity of the return series. In the same way, popular approaches in the equity universe, such as equal-weighting and minimum variance, should be seriously questioned due to the optimality assumption that they contain. To presuppose equal returns in the bond universe is particularly inconsistent with the very idea of the term structure of interest rates.

Finally, bonds do not only constitute an asset class that forms part of investorsí performance-seeking portfolios but they are also the preferred investment support for liability-hedging portfolios. This aspect implies reconciling the focus on interest rate risk management with performance optimisation in bond portfolios. This is seldom taken into account in the first generation of smart beta fixed-income indices which, as with smart beta equity indices, sell themselves solely on the basis of their performance.

In this area, we think it is essential to underline that here too the choice of risks of indices should be the responsibility of investors. Facing strong fluctuations in duration when investing in smart beta corporate bond indices, or even taking significant implicit interest rate bets as part of sovereign bond index offerings using minimum variance weighting, which leads to securities with very short duration being overweighted, is in our opinion not very compatible with the risk management culture that prevails in the fixed-income world.

All of these difficulties are the current focus of EDHEC-Risk Instituteís research teams and the results are not expected before the end of 2013.

This scientific uncertainty that needs to be resolved meant that we have not envisaged offering smart beta fixed-income indices in the short term.


  1. NoŽl Amenc, Felix Goltz, Lin Tang, October 2011, EDHEC-Risk European Index Survey 2011, EDHEC-Risk Institute Publication

  2. NoŽl Amenc, Felix Goltz, Lin Tang, Vijay Vaidyanathan, April 2012, EDHEC-Risk North American Index Survey 2011, EDHEC-Risk Institute Publication

  3. NoŽl Amenc, Felix Goltz, Ashish Lodh, Lionel Martellini, Spring 2012, Diversifying the Diversifiers and Tracking the Tracking Error: Outperforming Cap-Weighted Indices with Limited Risk of Underperformance, Journal of Portfolio Management, Volume 38, Number 3

  4. It is often argued that cap-weighting can be justified by Sharpe's (1964) Capital Asset Pricing Model (CAPM). It should be recognised that not only the many assumptions underlying the CAPM are highly unrealistic (e.g., the presence of homogenous expectations and the absence on non tradable assets to name just a few), but also that the CAPM predicts that the true market portfolio, as opposed to any given cap-weighted equity index, is an efficient portfolio. In fact, it is internally inconsistent for the unobservable (Roll's (1977) critique) cap-weighted true market portfolio to be efficient and for a cap-weighted equity portfolio extracted from the whole investment universe to be also efficient. This is because the design of an efficient equity portfolio taken in isolation from the rest of the investment universe ignores the correlation of selected stocks with the rest of the investment universe, while these correlations are taken into account in the design of an efficient portfolio for the whole investment universe. In other words, if it was the true asset pricing model the CAPM would predict that a cap-weighted equity portfolio cannot be an efficient portfolio since it instead predicts that the true market portfolio is an efficient portfolio.

  5. NoŽl Amenc, Felix Goltz and Ashish Lodh, Fall 2012, Choose Your Betas: Benchmarking Alternative Equity Index Strategies, Journal of Portfolio Management, Volume 39, Number 1

About NoŽl Amenc

NoŽl Amenc is professor of finance at EDHEC Business School, director of EDHEC-Risk Institute and CEO of ERI Scientific Beta. He has conducted active research in the fields of quantitative equity management, portfolio performance analysis, and active asset allocation, resulting in numerous academic and practitioner articles and books. He is on the editorial board of the Journal of Portfolio Management and serves as associate editor of the Journal of Alternative Investments and the Journal of Index Investing. He is a member of the scientific board of the French financial market authority (AMF), the Monetary Authority of Singapore Finance Research Council and the Consultative Working Group of the European Securities and Markets Authority Financial Innovation Standing Committee. He co-heads EDHEC-Risk Instituteís research on the regulation of investment management. He has a masterís in economics and a PhD in finance.

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