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Over the past five years, many asset managers and investors have called cap-weighted indices into question, not because they constituted a poor market reference, but because as an investment benchmark representative of a choice of risk and diversification, they were not optimal. It is in this context that smart beta indices were created. In fact, this first generation of smart beta indices – smart beta 1.0 – addressed either the poor factor exposure of cap-weighted indices or their poor diversification. 

As such, promoters of fundamental indexation insisted on the fact that cap-weighted indices were exposed to the most expensive stocks in the market and ultimately offered a proxy for an investment that was systematically, or fundamentally, ‘value’. The weighting of these indices does not take the relationship between the stocks into account in any way and, in the end, these indices are not better diversified and have the same degree of concentration as cap-weighted indices. 

On the other side, the advocates of diversification promoted the idea that smart weighting schemes provided a solution to the poor diversification of indices and that is how smart beta indices such as maximum deconcentration (equal-weighted), maximum decorrelation or equal risk contribution were proposed. Naturally, since the composition of these indices implicitly moved away from that of cap-weighted indices, these diversification indices have different factor exposures, which tended to make people say that smart beta was simply a marketing term for factor investing. It is in this framework that the recent developments, reconciling smart beta and factor investing in the concept of smart factor investing, are situated. 

The ‘smart beta 2.0’ approach applied to the design of smart beta factors provides a simple and consistent view of the construction of each smart factor based on two distinct and transparent steps in their implementation: 

• Selection of the universe of stocks that corresponds to the choice of factor exposure (and carried out with consensual and simple methods) 

• Choice of the weighting scheme(s) that provide efficient access to the risk premia associated with this factor exposure (and here the weighting schemes correspond to weightings that are well documented in the academic literature, with proven and robust long-term performance).

By associating an effective choice of weighting scheme in terms of diversification with this choice of factors carried out through stock selection, the defect of the strong concentration of cap-weighted indices can be remedied in favour of sound diversification that aims to provide the best return for a given level of risk (Sharpe ratio). This diversification of factor-tilted indices distinguishes them from traditional factor index offerings, which are often constructed not with a diversification objective but with a goal of maximum factor loading or exposure, or on the basis of selecting a small number of stocks weighted according to a cap-weighted scheme. The concentration of factor indices is a concern and a contradiction for proponents of alternative weighting schemes who started off by criticising the high level of concentration of cap-weighted indices and ended up offering concentrated factor indices.

The concern for the diversification of smart-factor indices allows their non-rewarded or specific risks to be reduced. This category of specific risks corresponds to all the risks that are unrewarded in the long run, and therefore not ultimately desired by the investor, but that can have a strong influence on the volatility or maximum drawdown of the index (in absolute terms) or the tracking error or maximum relative drawdown of the index (in relative terms). 

Specific risks can correspond to important financial risk factors that do not explain, over the long term, the value of the risk premium associated with the index. There are many non-rewarded financial risk factors. Academic literature considers, for example, that commodity, currency or sector risks do not have a positive long-term premium. These risks can have a strong influence on the volatility, tracking error, maximum drawdown or maximum relative drawdown over a particular period, which might sometimes be greater than that of systematically rewarded risk factors (such as exposure to the financial sector during the 2008 crisis or to sovereign risk in 2011). 

In line with portfolio theory, among the non-rewarded financial risks we also find specific financial risks (also called idiosyncratic stock risks), which correspond to the risks that are specific to the company itself (its management, the risk of the poor quality of its products, the failure of its sales team, the relevance of its R&D and innovation, and so on). It is this type of risk that asset managers are supposed to be the best at knowing, evaluating and choosing in order to create alpha, but portfolio theory considers it to be neither predictable nor rewarded, so it is better to avoid it by investing in a well-diversified portfolio. 

A globally effective diversification weighing scheme reduces the quantity of non-rewarded risk, whether it involves non-rewarded risk factors or non-rewarded specific financial risks. 

Specific, or non-rewarded, risks can also correspond to operational or non-financial risks specific to the implementation of the diversification model. These are strategy or operational-specific risks, which are usually analysed using the concept of parameter estimation error. As such, for example, a maximum decorrelation scheme depends on a good estimation of the correlation matrix for the robustness of the diversification proposed. A high price is attached to the technical quality of the models used and their implementation to reduce this type of specific risk. 

In spite of all the attention paid to the quality of model selection and the implementation methods for these models, this specific operational risk, like the non-rewarded financial risks described above, remains present nonetheless and it is useful to be able to reduce the exposures that each weighting scheme, even it is smart, is not able to diversify. This is the objective of multi-strategy approaches. 

The smart-factor, multi-strategy approaches that we have developed, which consist, for a given factor tilt, of proposing a portfolio of five different weighting schemes, enable all of the non-rewarded risks associated with each of the weighting schemes to be well diversified. It is this quality of double diversification proposed and conceptualised as ‘diversification of the diversifiers’ that leads us to consider that smart-factor, multi-strategy indices are flagship indices representing elementary building blocks for risk allocation.

This type of smart beta approach is, in our view, the new frontier for the construction of benchmarks that are representative of an efficient allocation to factors. The gains in terms of risk-adjusted returns of smart-factor indices compared with traditional factor index offerings are considerable. As such, on average over the long run (40 years) for US data, smart-factor, multi-strategy indices outperform cap-weighted factor indices by 68%. 

Noël Amenc is professor of finance at EDHEC Business School, director at EDHEC-Risk Institute and CEO of ERI Scientific Beta

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