Live is Better

Since 2013, with the Smart Beta 2.0 framework, EDHEC-Risk Institute has created Scientific Beta multi-smart-factor indices that are well diversified and exposed to rewarded factors. At that time, the four rewarded factors validated by EDHEC-Risk Institute were Value, Size, Low Volatility and Momentum. Furthermore, as a default weighting scheme option, Scientific Beta proposed its flagship multi-strategy weighting scheme which mixes different methods of alternative weightings to cap-weighted (Efficient Minimum Volatility, Efficient Maximum Sharpe Ratio, Maximum Deconcentration, Maximum Decorrelation and Diversified Risk Weighted) in order to diversify and thus reduce the model risks associated with each of these weighting schemes. These conceptual and methodological choices were the object of research published in refereed journals by the Scientific Beta teams and notably articles on "Choose Your Betas: Benchmarking Alternative Equity Index Strategies" published in the Fall 2012 issue of the Journal of Portfolio Management, "Smart Beta 2.0" published in the Winter 2013 issue of the Journal of Index Investing, and "Towards Smart Equity Factor Indices: Harvesting Risk Premia without Taking Unrewarded Risks" published in the Summer 2014 issue of the Journal of Portfolio Management.

After more than three years of live data, these indices show a robust live track record. The average live outperformance across all Scientific Beta developed regions of Scientific Beta Multi-Beta Multi-Strategy (Equal Weight and Equal Risk Contribution allocation) indices is 2.06% and 2.20% respectively.¹

We believe that the concern for robustness that underlies the design of our smart beta indices are always demonstrated, not only in our long-term track records, but also in our live performances.

This robustness is at the heart of our investment philosophy.

Firstly, we attach a constant concern to the academic consensus that governs the methodological choices for index construction. Whether it involves the choice of factors or their definition, this consensus is a guarantee of quality because research results that are not vested by commercial interests are the best means of guarding against data-mining or model mining and the temptation to maximise the in-sample performance of the indices to the detriment of their out-of-sample robustness. A white paper entitled "Robustness of Smart Beta Strategies" details the conditions and robustness measures that are applicable to smart beta and smart factor indices.

Secondly, the choice of favouring index diversification as a source of risk-adjusted performance is intentional. In line with the work of EDHEC-Risk Institute, ERI Scientific Beta does not seek to maximise the returns of factor indices by optimising the returns associated with the characteristics of the stocks, but instead to obtain the most attractive risk-adjusted performance by diversifying these indices in order to reduce the unrewarded specific risk. This diversification strategy is clearly a distinct advantage of the Scientific Beta indices compared to traditional factor indices that favour concentration. This choice of diversification is also that of a refusal to create performance through in-sample optimisation of stock characteristics, whether involving factor exposure or the returns associated with these characteristics. All the strategies offered by Scientific Beta use diversification weightings that are non-return-dependent. This choice of diversification was the subject of a publication in the Journal of Portfolio Management, entitled "Towards Smart Equity Factor Indices: Harvesting Risk Premia without Taking Unrewarded Risks", published in 2014.

ERI Scientific Beta’s third strategic choice is that of its capacity to make its indices genuinely transparent. This transparency is indispensable for the credibility of the published performances. It enables investors to avail of details that have no equivalent on the market on the construction methodologies, the results of long-term tests and the risks and sources of performance. This subject of the transparency of the risks and sources of performance is an important aspect of the assessment of the long-term outperformance capability of indices. We do not believe that it is possible to invest in indices without ultimately being able to avail of a representation of the absolute and relative risks over the long-term, not only for the factors but also for the performance strategies. Today we are offering evaluations of these over 45 years.

It is also clear that this long-term assessment only makes sense and is only pertinent for analysing the outperformance potential of investable indices if the latter are based on coherent and constant methodological choices. This point is particularly sensitive in an industry which often confuses scientific progress with marketing innovation or that is generated by a race to the best performance based on model mining. That is why Scientific Beta is, and will remain, attached to the smart beta 2.0 approach which is the basis of its index offering. Whatever the trends, the Scientific Beta multi smart factor indices will continue to be governed by the methodological principles underlying the long-term track records that support their marketing and notably the capacity within the context of a top-down allocation to control, in full transparency, the risks to which an investor wishes or does not wish to be exposed.

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Footnotes:

¹This live analysis is based on daily total returns in the period December 20, 2013 (live date) to December 31, 2016 for the following developed world regions – USA, Eurozone, UK, Developed Europe ex UK, Japan, Developed Asia Pacific ex Japan, Developed ex UK, Developed ex USA, and Developed. The benchmark used is a cap-weighted portfolio of all stocks in the respective Scientific Beta universes.

Improving Multi Factor Exposure without Sacrificing Diversification and Risk Control

In light of increasing investor interest in multi-factor solutions, product providers have recently been debating the respective merits of the "top-down" and "bottom-up" approaches to multi-factor portfolio construction. "Top-down" approaches assemble multi-factor portfolios by combining distinct sleeves for each factor, while the "bottom-up" methods build multi-factor portfolios in a single pass by choosing and/or weighting securities by a composite measure of multi-factor exposures. In this article, we discuss the results of recent research assessing the merits of both the approaches.

"Top-down" multi-factor portfolios blend single factor portfolios with a view to drawing on differentiated sources of returns while reducing the conditionality of performance. The approach is simple and transparent and affords flexible factor-by-factor control of multi-factor allocation, which makes it possible to serve diverse needs through different combinations of the same building blocks and, more importantly, allows for dynamic strategies. Its tractability and granularity also facilitate performance analysis, attribution and reporting. Being typically assembled from reasonably diversified factor sleeves, "top-down" multi-factor portfolios tend to result in portfolios with large effective numbers of stocks and thus good diversification of idiosyncratic risk.

"Bottom-up" portfolio construction has been favoured by practitioners seeking to concentrate portfolios to offer higher scores across targeted factors with a view to reaping the higher rewards expected from higher exposures. Indeed, under reasonable assumptions about the mapping of factor scores by securities, the direct selection and/or weighting of securities on the basis of their characteristics across the targeted factors will result in higher factor scores than the combination of specialised sleeves can achieve. The difference in potential scores between the two approaches increases with the targeted concentration of the portfolio and the number of factors targeted and decreases with factor correlations. While this is a general problem, the superiority of "bottom-up" over "top-down" approaches for the achievement of high scores across multiple factors is typically illustrated by examples involving a pair of factors with low correlation such as Valuation and Momentum. Mixing stand-alone portfolios targeting a high score for one factor in isolation leads to holding securities with low or negative scores in respect of the other targeted tilt. These securities that cause accelerated dilution of the scores of targeted tilts within the total portfolio can be avoided altogether when the two-factor portfolio is built directly by choosing securities that score highly in respect of each factor or on average across the two factors.

Proponents of "bottom-up" approaches argue that their higher factor exposures produce additional performance that makes it worthwhile for most investors to forsake the simplicity, transparency and flexibility of "top-down" approaches. However, while studies of "bottom-up" approaches such as the one by Bender and Wang [2016] document increased long-term returns, they typically fail to discuss short-term risks, and implementation issues such as heightened turnover.

More generally, the question of the superiority of the "bottom-up" approach should be addressed from the perspective of the robustness and investability of the performance displayed in-sample. Ultimately, investors are interested not in attractive-looking simulated track records but in true performance that is replicable out-of-sample. For ERI Scientific Beta, one of the keys to this robustness is the support of consensual, non-vested academic research.

It is understandable that computational technicians will have a tendency to aim at accounting for stock level exposures to multiple factors with the highest possible precision; it is worth considering insights from finance. Empirical evidence on factor premia overwhelmingly suggests that the relations between factor exposures and expected returns, which have been validated for diversified test portfolios, do not hold with a high level of precision at the individual stock level. This suggests that overexploiting information in factor exposure is not likely to improve performance. In addition, while there is ample evidence that portfolios sorted on a single characteristic are related to robust patterns in expected returns, such patterns may break down when incorporating many different exposures at the same time.

In the end, the "bottom-up" versus "top-down" debate relates to two factor investing approaches. The first, which supports the "bottom-up" approach, is where the objective of maximising factor exposure justifies renouncing all other dimensions of portfolio construction and notably diversification. The second, which supports the "top-down" approach, considers that the right way to obtain improved risk-adjusted returns associated with factor investing is to reconcile exposure to the rewarded factors with an excellent diversification of the non-rewarded specific risks.

In a recent white paper, entitled "Accounting for Cross-Factor Interactions in Multi-Factor Portfolios: The Case for Multi-Beta Multi-Strategy High Factor Exposure Indices", Amenc et al. [2017] deepen the debate and show that solutions relying on factor concentration through score-weighting not only suffer from extreme relative risk and high turnover but, after controlling for factor intensity, also have risk-adjusted performances that are lower than those of smart factor investing approaches, even naively diversified (equal-weight deconcentration).

In order to reconcile the benefits of strong factor intensity, notably by taking into account the risks of dilution of the factor exposures linked to the negative interactions between factor indices, with those of diversification, Scientific Beta is proposing an evolution of its smart factor indices offering through the application of a High Factor Exposure filter. This development is in coherence with the Smart Factor 2.0 approach advocated by EDHEC-Risk Institute since 2012.

Reconciling Diversification and Factor Exposure Objectives in a Top-Down Framework

Smart Factor Indices

The smart beta 2.0 index construction approach (Amenc and Goltz [2013]) distinguishes two steps in the construction of smart beta strategies, where the first step tilts towards the targeted risks by way of transparent security selection, and the second step diversifies away the undesired and unrewarded risks by applying a diversification weighting scheme. Amenc et al. [2014] use this approach to construct individual smart factor indices tilting towards documented factors, and to assemble "top-down" multi-factor portfolios. A basic smart factor index is constructed by making a (broad) stock selection on the basis of a single and consensual metric related to the targeted factor (such as the book-to-market ratio for value vs. growth selections) and then applying a deconcentration or diversification weighting scheme to the selection. The approach reconciles factor investing with diversification and deals with each in separate steps. Once smart factor indices for different targeted factors have been put together, it is straightforward to implement any multi-factor allocation by blending these indices.

The modularity of the approach allows for dynamic multi-factor allocation through transparent adjustments of single smart factor index weights within the portfolio; this can be used to incorporate tactical views and more importantly for risk management, including the control of risk factor exposures. Transparency and tractability of multi-factor allocations also facilitate risk and performance analysis and reporting. It should be noted that objectives in terms of factor exposure, such as increasing intensity, can also be addressed through allocation decisions across factor indices. A key benefit of the latter approach, which we test in this article, is that it employs well-diversified sub-portfolios to increase factor exposure rather than "bottom-up" concentration on the basis of noisy stock-level information.

A Method to Address Interaction across Factors in a "Top-Down" Index Construction Framework

Whatever the methodologies used, the "bottom-up" approach is based on the idea of selecting factor champions, i.e. stocks with the highest multi-factor scores.

However, in a long-only context, it may be less important to identify factor champions than to avoid factor losers as the market tends to penalise the losers more than it rewards the winners. We find that the absolute value of underperformance for the factor loser portfolios is greater than the outperformance of the factor champion portfolios. We test an elimination of stocks with the lowest multi-factor scores within each of six single-factor stock selections prior to applying the diversification weighting schemes. The objective is to obtain smart factor indices with higher factor exposures in multi-factor combinations and we thus term these filtered indices "diversified high factor exposure smart factor indices."

The multi-factor metric chosen is the arithmetic average of the normalised rank scores for five of the six targeted factors (Valuation, Momentum, Volatility, Investment and Probability); the Size factor is omitted as any diversification weighting scheme induces a tilt away from the largest capitalisations that is not diluted by blending smart factor indices targeting different factors.

Diversified high factor exposure smart factor indices, in addition to achieving the desired factor tilt by way of the initial selection, will thus also have aggregate exposure to the other rewarded factors that will be higher than that of their unfiltered counterparts. This will mitigate dilution when indices targeting different factors are blended. We now turn to comparing the score-weighted "bottom-up" approaches assessed previously to "top-down" multi-factor portfolios formed by assembling unfiltered and diversified high factor exposure smart factor indices, respectively.

Comparing "Bottom-Up" and "Top-Down" Approaches

In these comparisons, we benchmark different "top-down" multi-factor strategies against the concentrated "bottom-up" approaches i.e. the score-weighted approaches applied to quintile selections. These bottom-up approaches correspond to portfolios formed with 20% stock selection based on a stock-level multi-factor composite score that is either an arithmetic average or a geometric average of the normalised ranks scores of each individual factor. The smart factor indices used as building blocks in the "top-down" strategies are based on broad selections (half universe) as in Amenc et al. [2014]. For the diversified high factor exposure indices, selections are shrunk to 30% of the total number of stocks in the universe. Three "top-down" portfolios are evaluated, the unfiltered multi-beta multi-strategy six-factor index (equal-weighted), its high factor exposure counterpart, and a solution approach that dynamically allocates to individual diversified high factor exposure smart factor indices to maximise the portfolio's geometric average exposure to the targeted factors – the multi-beta multi-strategy diversified max factor exposure index. The data on these indices are sourced from the ERI Scientific Beta website, where detailed methodologies can also be found. In the context of the "top-down" portfolios reviewed here, factor exposures are thus used primarily to select broad groups of stocks and, in the context of the diversified max factor exposure index, to make allocation decisions across broad groups of stocks.

Exhibit 1: Performance and Risk Measures

EDHEC-Risk US LTTR (31/12/1975 to 31/12/2015)	Cap-Weighted	MFS 20% Stock Selection Score times cap-weighted based on:		SciBeta "Top Down"
		Arithmetic Average	Geometric Average	Multi-Beta Multi-Strategy 6F (EW)		Multi-Beta Multi-Strategy
				Standard	Diversified High Factor Exposure	Diversified Max Factor Exposure
Annualised Returns	11.12%	15.75%	15.28%	13.98%	14.79%	14.89%
Volatility	17.04%	13.95%	13.91%	15.18%	13.91%	13.88%
Sharpe Ratio	0.36	0.77	0.74	0.59	0.71	0.72
Maximum Drawdown	54.31%	45.48%	45.07%	52.59%	48.69%	48.27%
Relative Return	-	4.63%	4.16%	2.86%	3.67%	3.77%
Tracking Error	-	8.73%	8.45%	4.39%	5.80%	6.39%
Information Ratio	-	0.53	0.49	0.65	0.63	0.59
Outperformance Probability (3Y)	-	84.32%	79.61%	79.09%	81.26%	79.81%
Extreme Relative Returns (5%)	-	-13.30%	-13.58%	-6.92%	-8.37%	-9.26%
Extreme Tracking Error (95%ile)	-	18.06%	16.79%	8.33%	10.87%	12.66%
Maximum Relative Drawdown	-	51.66%	55.66%	30.34%	36.63%	41.60%
Maximum Relative Loss	-	0.00%	0.00%	0.03%	0.07%	0.00%

Analysis is based on daily total returns in USD from 31/12/1975 to 31/12/2015. Results of three different Scientific Beta "top-down" approaches are shown as well. EDHEC-Risk USA LTTR is used as the cap-weighted benchmark. The risk-free rate is the return of the 3-month US Treasury bill. The probability of outperformance is the probability of obtaining positive excess returns from investing in the strategy for a period of 3 years at any point during the history of the strategy. A rolling window of length 3 years and a step size of 1 week is used.

Long-term performance and risk measures reported in Exhibit 2 show that all strategies deliver pronounced excess returns and improved Sharpe ratios over the cap-weighted index. They also reveal that the "top-down" strategies implemented with the diversified high factor exposure smart factor indices cancel half of the performance differential between the unfiltered "top-down" strategy and the narrow-selection score-weighted indices chosen for this acid test. On a total-risk adjusted basis, the multi-beta multi-strategy diversified high factor exposure index and diversified max exposure are arguably in the same class as the score-weighted approaches; however "top-down" approaches have higher relative-risk adjusted performance. Differences are more pronounced when we look at the extreme relative returns and extreme tracking error. "Top-down" approaches have significantly lower extreme tracking error than "bottom-up" approaches (10.62% vs. 17.43% on average, i.e. an improvement of almost 40%). Similarly "top-down" approaches have less punishing extreme relative returns (-8.18% vs. -13.44% on average, an improvement approaching 40%). Hence the superior long-term performance documented for the narrow-selection score-weighted approaches comes with significant short-term risks.

The results in Exhibit 2 highlight some of the concentration and investability issues of the "bottom-up" approaches. The GLR measures of the "top-down" portfolios are significantly better than those of the "bottom-up" portfolios, whereas their total volatilities are similar or higher, which suggests better diversification of idiosyncratic risk. The lower levels of standard deviations of residuals and the superior residual Sharpe ratios displayed by the "top-down" approaches are also consistent with high diversification.

In the same exhibit, we also note the strong difference between the turnover using a "bottom-up" approach and that of a "top-down" approach since between the flagship Multi-Beta Multi-Strategy Diversified High Factor Exposure index and the two bottom-up strategies, the average difference is 43.35%.

Exhibit 2: Investability and Diversification Measures

EDHEC-Risk US LTTR (31/12/1975 to 31/12/2015)	Cap-Weighted	MFS 20% Stock Selection Score times cap-weighted based on:		SciBeta "Top Down"
		Arithmetic Average	Geometric Average	Multi-Beta Multi-Strategy 6F (EW)		Multi-Beta Multi-Strategy 6F
				Standard	Diversified High Factor Exposure	Diversified Max Factor Exposure
Annualised One-Way Turnover	2.42%	87.39%	67.66%	29.96%	34.18%	56.98%
Capacity	52.28	17.79	7.37	12.91	13.21	12.62
Effective Number of Stocks	122	34	54	333	195	153
GLR Measure	25.76%	25.68%	25.12%	19.41%	20.21%	20.64%
Idiosyncratic Risk	-	5.64%	5.21%	3.01%	3.31%	3.57%
Residual Sharpe Ratio	-	0.38	0.28	0.46	0.58	0.49
Volatility Reduction compared to Multi-Factor Benchmark	-	-2.11%	-1.22%	-2.62%	-3.24%	-2.73%

Analysis is based on daily total returns in USD from 31/12/1975 to 31/12/2015. Results of 3 different Scientific Beta "top-down" approaches are shown as well. EDHEC-Risk USA LTTR is used as the cap-weighted benchmark. Capacity is the weighted average market capitalisation in USD billion. Volatility Reduction is measured as the difference between the volatility of the strategy and its multi-factor benchmark, which is a synthetic portfolio, levered to match returns of the respective strategy, and contains an exactly similar magnitude of systematic risk. The GLR measure is the ratio of the variance of a portfolio's returns to the weighted average of the variance of its constituents' returns. The idiosyncratic risk is based on a seven-factor regression (Market + 6 factors – Size, Value, Momentum, Volatility, Investment and Profitability). The regressions are based on weekly total returns. The Market factor is the excess return series of the cap-weighted index over the risk-free rate. Other factors are constructed from the return series of long/short portfolios formed by equally weighting stocks in the top/bottom three deciles of ranks for each factor criterion.

The figures in Exhibit 3 on long-term factor exposures confirm that the use of diversified high factor exposure smart factor indices delivers "top-down" multi-factor portfolios that display significantly increased factor exposures; unsurprisingly, the diversified max factor exposure index achieves the highest factor intensity with an increase of close to 50% relative to the unfiltered multi-beta multi-strategy index. Just as unsurprisingly, the "bottom-up" strategies produce the highest exposures.

Exhibit 3: Factor Exposures

EDHEC-Risk US LTTR (31/12/1975 to 31/12/2015)	Cap-Weighted	MFS 20% Stock Selection Score times cap-weighted based on:		SciBeta "Top Down"
		Arithmetic Average	Geometric Average	Multi-Beta Multi-Strategy 6F (EW)		Multi-Beta Multi-Strategy 6F
				Standard	Diversified High Factor Exposure	Diversified Max Factor Exposure
Unexplained	0.00%	2.17%	1.47%	1.38%	1.92%	1.77%
Market	1	0.95	0.98	1	0.97	0.97
SMB	0	0.15	0.19	0.19	0.18	0.18
HML	0	0.27	0.31	0.12	0.14	0.18
MOM	0	0.1	0.1	0.04	0.04	0.06
LVOL	0	0.2	0.18	0.06	0.14	0.12
INV	0	0.13	0.13	0.05	0.07	0.13
PROF	0	0.15	0.16	0.05	0.09	0.08
R-square	100.00%	83.06%	86.15%	95.98%	94.29%	93.33%
Factor Intensity	0	1.01	1.08	0.51	0.66	0.75
Excess Return/Factor Intensity	-	4.58%	3.86%	5.61%	5.56%	5.03%
Sharpe Ratio of a Levered Portfolio	-	0.8	0.74	0.83	0.93	0.87
Factor Intensity Drift	-	0.3	0.42	0.2	0.19	0.23

Analysis is based on weekly total returns in USD from 31/12/1975 to 31/12/2015. Results of 3 different Scientific Beta "top-down" approaches are shown as well. EDHEC-Risk USA LTTR is used as the cap-weighted benchmark. The regressions are based on weekly total returns. The Market factor is the excess return series of the cap-weighted index over the risk-free rate. Other factors are constructed from the return series of long/short portfolios formed by equally weighting stocks in the top/bottom three deciles of ranks for each factor criterion. Figures in bold correspond to p-values of 5% or less. Factor Intensity is the sum of all betas except market beta. RMSE is the root mean squared error of factor betas with respect to the average beta. Excess Returns over Factor Intensity is a measure of relative return to the cap-weighted index per unit of factor intensity. Sharpe Ratio of a levered portfolio is based on the returns of a portfolio that was levered up with the objective of achieving the same factor intensity as the one with the maximum factor intensity among the strategies reported in the table, which equals 1.08. The formation of the levered portfolio is based on the constraint that the sum of the weights is equal to 1, which is achieved by taking a short position in the cap-weighted benchmark at the same time. Factor Drift is the square root of the sum of factor exposure variances excluding the market beta. Factor Intensity Drift is the standard deviation of Factor Intensity.

However, "top-down" approaches deliver higher excess returns per unit of factor intensity – on average circa 25% more than "bottom-up" strategies. The Sharpe ratios of portfolios that have been levered to achieve the highest factor intensity delivered by the "bottom-up" approaches are consistently higher for "top-down" strategies. These results clearly suggest that relative to multi-factor "top-down" approaches, score-weighted strategies deliver their higher factor intensities in an inefficient way.

The exhibit also assesses the instability of factor exposures. It is worth pointing out that the "bottom-up" approach that produces the highest factor intensity also suffers the highest absolute and relative instability of this intensity. The intensity drift of the "bottom-up" approaches is twice as high as for the "top-down" approaches built with diversified high factor exposure indices. The "top down" approaches deliver higher excess returns per unit of factor intensity (5.40% on average) compared to that of "bottom-up" portfolios (4.22%). This represents a 28% increase in excess returns per unit of factor intensity.

Conclusion

It appears that by ignoring the central tenet of modern portfolio theory to focus solely on increasing factor score intensity and by assuming strong relationships between security-level scores and performance, score-weighted approaches expose investors to risks that are unrelated to factors and for which no reward should be expected. We find that focusing solely on increasing factor intensity leads to inefficiency in capturing factor premia, as exposure to unrewarded risks more than offsets the benefits of increased factor scores. High factor scores in "bottom-up" approaches also come with high instability and high turnover. We introduce an approach that considers cross-factor interactions in "top-down" portfolios through an adjustment at the stock selection level. This approach, while producing lower factor intensity than "bottom-up" methods, this approach leads to higher levels of diversification and produces higher returns per unit of factor intensity. We find that it dominates "bottom-up" approaches in terms of relative performance, while considerably reducing extreme relative losses and turnover.

Accounting for Cross-Factor Interactions in Multi-Factor Portfolios: The Case for Multi-Beta Multi-Strategy High Factor Exposure Indices, February 2017
Scientific Beta Multi Smart Factor Indices: An Introduction, February 2017

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Felix Goltz is Research Director, ERI Scientific Beta, and Head of Applied Research at EDHEC-Risk Institute. He carries out research in empirical finance and asset allocation, with a focus on alternative investments and indexing strategies. His work has appeared in various international academic and practitioner journals and handbooks. He obtained a PhD in finance from the University of Nice Sophia-Antipolis after studying economics and business administration at the University of Bayreuth and EDHEC Business School.

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"Assessing the Crowding Hypothesis" Webinar

Broadcast on 12 January, 2017 and hosted by Dr Felix Goltz, Head of Applied Research, EDHEC-Risk Institute and Research Director, ERI Scientific Beta

Addresses the insights to be gained from considering the economic rationale of factor premia and reviews the empirical evidence on crowding.

While crowding is commonly pointed to as a potential risk, it is rarely formalised or even defined. This absence of definition is an issue when none wants to draw founded conclusions. Indeed, if it is now clear how crowding is defined or how it can be measured, it is rather futile to talk about whether or not it has or will occur. The main idea behind a crowding risk is that, as everyone knows about successful Smart Beta strategies and increasingly invests in them, flows into these strategies will ultimately cancel out their benefits. If an increasing amount of money starts chasing the returns to a momentum strategy for example, it is possible that the reward for holding this strategy - which has been documented with historical data - will ultimately disappear.

Topics include:

• What can be learnt from academic research on the development of risk premia factors?
• How to evaluate factor crowding risk?
• What are the appropriate and inappropriate responses to factor crowding risk?

Ten Misconceptions about Smart Beta: Analysing common claims on performance drivers, investability issues and strategy design choices

June 2016

In this research publication, EDHEC-Risk Institute reviews ten common but mistaken claims about smart beta that present risks for investors and sheds light on underlying issues. Among such critiques, a recurring issue is the presumption of a risk of "crowding" in Smart Beta strategies.

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"Long-Term Rewarded Equity Factors: What Can Investors Learn from Academic Research?" Webinar

Broadcast on 18 October, 2016 and hosted by Dr Felix Goltz, Head of Applied Research, EDHEC-Risk Institute and Research Director, ERI Scientific Beta

Examines what academic research has to say on equity factors with the objective of understanding which lessons can be learnt from such research in terms of designing and evaluating factor indices.

Equity index products that claim to provide exposure to factors which have been well documented in academic research, such as value and momentum, among others, have been proliferating in recent years. Interestingly, providers across the board put strong emphasis on the academic grounding of their factor indices. At the same time, product providers try to differentiate themselves using proprietary elements in their strategy, often leading to the creation of products using new factors or novel strategy construction approaches which may or may not be consistent with the broad consensual findings in the academic literature on empirical asset pricing. Moreover, discussion of the sources of performance is often based on provider-specific research rather than consensual findings in the academic literature.

Topics include:

• Analysing the main lessons from academic research on equity factors
• Addressing implementation costs and the question of crowding risks
• Discussing how practical implementation relates to the academic groundings

Long-Term Rewarded Equity Factors: What Can Investors Learn from Academic Research?

Journal of Index Investing, Fall 2016

The webinar presents and discusses a research paper published in the Fall 2016 issue of the Journal of Index Investing which analyses what academic research has to say on equity factors to understand what lessons can be learnt in terms of designing and evaluating factor indices.

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"On the Robustness of Smart Beta Strategies" Webinar

Broadcast on 20 December, 2016 and hosted by Dr Eric Shirbini, Global Research and Investment Solutions Director, ERI Scientific Beta

Reviews the importance of robustness for smart beta strategies and discusses how to measure and assess robustness in the performance analysis of smart beta strategies.

There has been significant evidence that systematic equity investment strategies (so-called smart beta strategies) outperform cap-weighted benchmarks in the long run. However, it is important to recognise that performance analysis is typically conducted on back-tests which apply the smart beta methodology to historical stock returns. Concerning actual investment decisions, it is thus relevant to question how robust the outperformance is. It is important to make a distinction between relative robustness and absolute robustness. A strategy is assumed to be ‘relatively robust’ if it is able to deliver similar outperformance under similar market conditions by aligning well with the performance of underlying factor exposure it is seeking and reducing unrewarded risks. Absolute robustness is the absence of pronounced state and/or time dependencies and a strategy shown to outperform irrespective of prevailing market conditions can be termed as robust in absolute terms.

Topics include:

• What are the live performances of popular smart beta strategies?
• How can these performances be analysed?
• What tools can be used to measure the robustness of smart beta performances?
• Are past performances representative of future performances?

Robustness of Smart Beta Strategies

July 2016

The webinar presents and discusses a recent research publication that reviews the importance of robustness for smart beta strategies and discusses how to measure and assess robustness in the performance analysis of smart beta strategies.

Terri Troy, Chief Executive Officer, Halifax Regional Municipality Pension Plan

In this interview, Terri Troy, Chief Executive Officer of Halifax Regional Municipality Pension Plan, explains why the plan decided to opt for a Scientific Beta multi-beta multi-strategy index, and provides her thoughts on the attraction and future of smart beta indexes for investors.

Why did the Halifax Regional Municipality Pension Plan decide to choose the Scientific Beta developed multi-beta multi-strategy index?

Our goal was to gain factor exposure. We had invested in some individual factor portfolios with another provider utilizing cap-weighted indices. We then decided to grow the strategy, and decided to complement with another provider. EDHEC Scientific Beta was chosen based mainly on an excellent reputation. The decision also hinged on the diversification among factors that we could achieve with the multi-beta multi-strategy (EW) index.

We believed that popular risk diversification strategies have their limitations, for example the minimum volatility was overvalued, and the equal risk strategy overemphasized volatility. That is why we opted for a more diversified approach such as that offered by the Scientific Beta Multi-Beta Multi-Strategy Four-Factor EW index.

Could you tell us a little about the objectives of this allocation?

The objectives are to access the specific risk factors as outlined in the multi-beta, multi-strategy index, with the expectation of achieving better returns than that of a traditional passive manager measuring to a traditional benchmark. In addition, we expect that we will enjoy better risk-adjusted returns net of fees.

What do you consider to be the main advantages of smart beta indexes for investors?

Smart beta indices in general allow access to risk factors that have outperformed over the long term, without paying for active management, which over time has not proven to consistently outperform straight Beta.

Lastly, the indices provide good transparency, with transparent access to strategy holding.

How do you see the future of smart beta investing?

Further customization and permutations to the various factors/strategies. Although we would be cautious that the future developments actually add sufficient value compared to the four-factor EW strategy to make them worth the pursuit.

Application to fixed income has already begun and is worth further investigation.

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Terri Troy is CEO of the Halifax Regional Municipality (HRM) Pension Plan, a multi-employer pension plan. Terri is responsible for all aspects of the plan and including governance, risk management, customer service, pension administration, and investments. Prior to this, Terri oversaw the pension investments for Royal Bank of Canada's global pension plans. Terri is an active participant in the Canadian pension industry and is frequently asked to speak and write on various investment topics. She has been recognized by peers as a thought leader and innovative pension plan manager. Terri is a member of the Pension Investment Association of Canada having chaired PIAC's Government Relations Committee from 2010-2011, chaired PIAC's Investment Practices Committee from 2005-2009, served on PIAC's Board of Directors from 2004-2009, and chaired the PIAC Board in 2007. She is a member of the Advisory Committee for the Canadian Investment Review, is a member on Investor Committees for several portfolio investments and is a Director of a UK Energy Company and a Canadian financial services company.

2016 Performance Analysis: A Very Unusual Year

The year 2016 was marked by a rare event in the investment world where, in the end, four out of the six traditional rewarded factors underperformed the CW reference. This situation, which cannot allow factor diversification to play its role, has been analysed over a long-term period within the framework of the design of the Scientific Beta indices and its probability of occurrence is less than 10% (9.55% in the table below).

Time period (31/12/1975 to 31/12/2015)	US LTTR Factors (Cap/HMom/LVol/HProf) (Cap-Weighted)	US LTTR Factors (Cap/HMom/LVol/HProf) (Diversified Multi-Strategy Indices)
Probability of all 4 factors underperforming (1-yr)	0.00%	9.55%
Probability of any 3 factors underperforming (1-yr)	19.75%	16.56%
Probability of any 2 factors underperforming (1-yr)	42.04%	18.47%
Probability of any 1 factor underperforming (1-yr)	32.48%	17.83%
Probability of no factor underperforming (1-yr)	5.73%	37.58%
Probability of all 4 factors underperforming (3-yr)	0.67%	6.04%
Probability of any 3 factors underperforming (3-yr)	14.09%	9.40%
Probability of any 2 factors underperforming (3-yr)	36.24%	6.04%
Probability of any 1 factor underperforming (3-yr)	38.26%	18.79%
Probability of no factor underperforming (3-yr)	10.74%	59.73%

Time period (21/06/2002 to 31/12/2016)	Dev Factors (Cap/HMom/LVol/HProf) (Cap-Weighted)	Dev Factors (Cap/HMom/LVol/HProf) (Diversified Multi-Strategy Indices)
Probability of all 4 factors underperforming (1-yr)	1.82%	7.27%
Probability of any 3 factors underperforming (1-yr)	16.36%	3.64%
Probability of any 2 factors underperforming (1-yr)	34.55%	16.36%
Probability of any 1 factor underperforming (1-yr)	34.55%	20.00%
Probability of no factor underperforming (1-yr)	12.73%	52.73%
Probability of all 4 factors underperforming (3-yr)	0.00%	0.00%
Probability of any 3 factors underperforming (3-yr)	2.13%	0.00%
Probability of any 2 factors underperforming (3-yr)	19.15%	4.26%
Probability of any 1 factor underperforming (3-yr)	48.94%	10.64%
Probability of no factor underperforming (3-yr)	29.79%	85.11%

We can observe that the 1-year underperformance probability is also low for the developed universe (7.27%), where we have a shorter historical track record.

Over a long-term period, we can also measure the usefulness of the diversification of the factor indices offered by our multi-strategy scheme, since the probability over one year of having only two factors that perform is 42% for the cap-weighted factor indices and 18.47% for multi-strategy factor indices.

In addition, for a 3-year horizon we observe that there is a 59.73% chance for US LTTR that none of the four multi-strategy smart factor indices will underperform, compared to only 10.74% for the cap-weighted equivalents of these indices.

Looking more precisely at the High Momentum factor, we can note that the 2016 performance was the worst performance for its CW version since the inception of the Scientific Beta single factor indices in June 2002. For its multi-strategy version, we were at 98% of the worst case scenario. Regarding the High Profitability factor, the 2016 performance was the worst performance for its multi-strategy version since June 2002.

The year 2016 was also marked by a certain number of non-factor elements, i.e. that are not part of the long-term dynamics, but that had a strong influence on the variability of performance in the short term. These were notably the election of Donald Trump and Brexit, which gave rise to strong sector arbitrage. As such, we can observe that sector-neutral versions, which aimed to protect the smart factor indices from any impact due to the sector biases that are naturally present in a factor approach, produced better performance.

Developed Year 2016 (USD, Total Returns)	Diversified Multi-Strategy
	Mid-Cap	High Momentum	Low Volatility	High Profitability	Value	Low Investment
Diversified Multi-Strategy (DMS)	6.77%	1.97%	7.25%	5.81%	11.95%	10.32%
Sector Neutral DMS	8.29%	4.55%	7.07%	6.97%	10.09%	9.84%

Scientific Beta Smart Factor Indices Performance

Performance Overview

The following table displays the short-term, mid-term and long-term performance of diversified multi-strategies by factor tilt in the Developed equity universe, which is the global universe managed by Scientific Beta. The six rewarded tilts selected (Value, Mid-Cap, Low Volatility, High Momentum, Low Investment and High Profitability) are part of the common tilts documented in the literature as liable to produce outperformance compared to cap-weighted indices.

Diversified Multi-Strategy Index	Past Quarter	Year-to-Date	1 Year	5 Years	10 Years	Long-Term US Track Records 31/12/1975 to 31/12/2015 (40 years)
	Relative Return Compared to Broad Cap-Weighted as of 31/12/2016
Value	1.56%	4.26%	4.24%	1.15%	0.72%	2.80%
Mid-Cap	-2.65%	-0.92%	-0.92%	1.11%	1.69%	2.70%
Low Volatility	-2.25%	-0.44%	-0.44%	1.62%	2.44%	2.52%
High Momentum	-4.14%	-5.72%	-5.70%	0.45%	0.74%	3.09%
Low Investment	0.46%	2.63%	2.62%	2.12%	2.48%	3.05%
High Profitability	-3.75%	-1.89%	-1.88%	1.70%	3.21%	2.57%

The history of Scientific Beta Index returns begins on 21/06/2002. The statistics are based on daily total returns (with dividends reinvested). All statistics are annualised, except for the past quarter relative returns and year-to-date relative returns which are non-annualised. The performance ratios that involve the average returns are based on the geometric average, which reliably reflects multiple holding period returns for investors. ERI Scientific Beta uses the yield on Secondary Market US Treasury Bills (3M) as a proxy for the risk-free rate in US Dollars. All results are in USD.

Looking at annualised relative returns over the past year, the best performing index among smart factor indices was the SciBeta Developed Value Diversified Multi-Strategy index with a relative return of 4.24%, while the SciBeta Developed High-Momentum Diversified Multi-Strategy index posted the lowest relative return at -5.70%.

Over the past ten years, all strategies posted positive relative returns in relation to broad cap-weighted indices, with values ranging from 0.72% for the SciBeta Developed Value Diversified Multi-Strategy index to 3.21% for the SciBeta Developed High-Profitability Diversified Multi-Strategy index.

Live Performance

The table below shows the live performance of the six single factor smart factor indices for the Developed equity universe.

Diversified Multi-Strategy Index	Live Performance from 21/12/2012 to 31/12/2016
	Absolute Return	Relative Return		Volatility	Sharpe Ratio
		To Tilted Cap-Weighted	To Broad Cap-Weighted
Value	10.61%	1.75%	1.28%	11.14%	0.94
Mid-Cap	10.62%	0.47%	1.28%	10.67%	0.98
Low Volatility	11.21%	1.88%	1.88%	9.44%	1.18
High Momentum	10.00%	1.20%	0.67%	10.67%	0.93
Low Investment	11.70%	1.97%	2.37%	10.59%	1.09
High Profitability	11.56%	1.54%	2.23%	10.44%	1.10

The statistics are based on daily total return series (with dividends reinvested). The live date for Value, Mid Cap, Low Volatility and High Momentum indices is 21 December, 2012 and the live date for Low Investment and High Profitability Indices is 20 March, 2015. For comparison purposes, all the statistics are computed from 21 December, 2012 to 31 December, 2016. All results are in USD.

Over their live period, all six smart factor indices post positive relative returns compared to broad cap-weighted indices, with performance ranging from 0.67% for the SciBeta Developed High-Momentum Diversified Multi-Strategy index to 2.37% for the SciBeta Developed Low-Investment Diversified Multi-Strategy index.

Scientific Beta Multi Smart Factor Indices Performance

Performance Overview

The table below displays an overview of the relative and absolute performance of Scientific Beta Multi-Beta Multi-Strategy indices for the Developed, United States and Global regions over different time periods.

Index	Multi-Beta Multi-Strategy	Nº of Consti- tuents	Relative Return Compared to Cap-Weighted				Information Ratio		Absolute Return		Volatility		Sharpe Ratio
			Past Quarter	YTD	1 Year	10 Years	1 Year	10 Years	1 Year	10 Years	1 Year	10 Years	1 Year	10 Years
Developed	4-Factor EW	1518	-1.91%	-0.81%	-0.81%	1.43%	-0.30	0.53	6.86%	5.83%	11.76%	15.94%	0.56	0.32
	6-Factor EW	1581	-1.85%	-0.45%	-0.45%	1.90%	-0.18	0.71	7.22%	6.30%	11.83%	15.85%	0.58	0.36
	Quality EW	1209	-1.63%	0.39%	0.38%	2.86%	0.18	0.98	8.05%	7.25%	12.03%	15.71%	0.64	0.42
SciBeta Developed CW		1600							7.66%	4.40%	12.71%	17.51%	0.58	0.21
United States	4-Factor EW	475	-1.04%	-0.46%	-0.46%	1.14%	-0.15	0.36	10.97%	8.11%	12.50%	19.60%	0.85	0.38
	6-Factor EW	497	-1.09%	-0.02%	-0.02%	1.61%	-0.01	0.52	11.41%	8.58%	12.56%	19.40%	0.88	0.41
	Quality EW	375	-1.11%	0.91%	0.91%	2.55%	0.39	0.71	12.34%	9.51%	12.80%	19.09%	0.94	0.46
SciBeta United States CW		500							11.43%	6.96%	13.07%	20.74%	0.85	0.30
Global	4-Factor EW	2179	-1.80%	-1.35%	-1.35%	1.53%	-0.52	0.56	6.60%	5.74%	11.58%	15.63%	0.54	0.32
	6-Factor EW	2271	-1.76%	-0.93%	-0.92%	2.00%	-0.39	0.72	7.03%	6.20%	11.65%	15.55%	0.58	0.36
	Quality EW	1734	-1.58%	0.02%	0.02%	2.93%	0.01	0.99	7.97%	7.14%	11.85%	15.44%	0.65	0.42
SciBeta Global CW		2300							7.95%	4.20%	12.69%	17.43%	0.60	0.20

Based on daily total returns in USD as of 31/12/2016. Inception date is 21/06/2002 for Scientific Beta Multi-Beta Multi-Strategy 4-Factor EW indices, Scientific Beta Multi-Beta Multi-Strategy 6-Factor EW indices and Scientific Beta Multi-Beta Multi-Strategy Quality indices and 19/12/2003 for Scientific Beta Multi-Beta Multi-Strategy CW indices. All statistics are annualised, except for the past quarter relative returns and year-to-date relative returns which are non-annualised. Performance ratios that involve the average returns are based on the geometric average, which reliably reflects multiple holding period returns for investors. The risk-free rates used are defined according to the regional universe of the index. The number of index constituents are as of the last quarterly rebalancing, i.e. 16/12/2016.

Over the past year, relative returns varied from -1.35% for the SciBeta Global Multi-Beta Multi-Strategy Four-Factor EW index to 0.91% for the SciBeta United States Multi-Beta Multi-Strategy Quality index.

Over the past ten years, the SciBeta Developed Multi-Beta Multi-Strategy Four-Factor EW index, the SciBeta Developed Multi-Beta Multi-Strategy Six-Factor EW index and the SciBeta Developed Multi-Beta Multi-Strategy Quality index posted strong annual relative returns of 1.43%, 1.90% and 2.86% respectively, compared to cap-weighted indices. For the other regions, the highest performance over the past ten years was obtained by the SciBeta Global Multi-Beta Multi-Strategy Quality index with a relative return of 2.93%, compared to cap-weighted indices, with the lowest performance posted by the SciBeta United States Multi-Beta Multi-Strategy Four-Factor EW index at 1.14%.

Over the long-term, all Multi-Beta Multi-Strategy indices post positive excess returns compared to cap-weighted indices. Using long-term US track records from December 31, 1975 to December 31, 2015 (40 years), the Multi-Beta Multi-Strategy Four-Factor EW, the Multi-Beta Multi-Strategy Six-Factor EW, and the Multi-Beta Multi-Strategy Quality benchmarks post respective relative returns compared to cap-weighted indices of 2.86%, 2.86% and 2.85%.

The relative and absolute performance data for all 17 of the Scientific Beta regional universes is available here.

Live Performance

The table below reports the live performance of the Scientific Beta Multi-Beta Multi-Strategy indices for the Developed, Developed ex USA, United States and Global regions as of 31 December, 2016.

Index	Multi-Beta Multi-Strategy	Live Date	Relative Return Compared to Cap-Weighted	Absolute Return	Volatility	Sharpe Ratio
			Since Live Period (as of 31/12/2016)
Developed	4-Factor EW	20/12/2013	1.61%	6.52%	10.66%	0.60
	6-Factor EW	18/09/2015	-0.53%	10.24%	N.S.	N.S.
	Quality EW	18/09/2015	0.56%	11.32%	N.S.	N.S.
Developed ex USA	4-Factor EW	20/12/2013	2.62%	2.59%	12.13%	0.20
	6-Factor EW	18/09/2015	0.83%	4.32%	N.S.	N.S.
	Quality EW	18/09/2015	1.45%	4.94%	N.S.	N.S.
United States	4-Factor EW	20/12/2013	0.67%	9.80%	12.64%	0.76
	6-Factor EW	18/09/2015	-1.76%	14.86%	N.S.	N.S.
	Quality EW	18/09/2015	-0.25%	16.37%	N.S.	N.S.
Global	4-Factor EW	18/03/2016	-2.95%	5.31%	N.S.	N.S.
	6-Factor EW	18/03/2016	-2.65%	5.62%	N.S.	N.S.
	Quality EW	18/03/2016	-1.95%	6.32%	N.S.	N.S.

Based on daily total returns in USD. Inception date is June 21, 2002 for Scientific Beta Multi-Beta Multi-Strategy 4-Factor EW indices, Scientific Beta Multi-Beta Multi-Strategy 6-Factor EW indices and Scientific Beta Multi-Beta Multi-Strategy Quality indices. The statistics with time periods of longer than two years are annualised and performance ratios that involve the average returns are based on the geometric average, which reliably reflects multiple holding period returns for investors. The returns of indices with live periods of less than two years are non-annualised cumulative returns and volatility and Sharpe ratio are not reported. The risk-free rates used are defined according to the regional universe of the index.

The SciBeta Developed Multi-Beta Multi-Strategy Four-Factor EW (Equal Weights) index, the SciBeta Developed Multi-Beta Multi-Strategy Six-Factor EW (Equal Weights) index and the SciBeta Developed Multi-Beta Multi-Strategy Quality index post relative returns of 1.61%, -0.53% and 0.56% respectively over their corresponding live periods compared to broad cap-weighted indices.

Looking at the other regions, performance ranged from -2.95% for the SciBeta Global Multi-Beta Multi-Strategy Four-Factor EW index to 2.62% for the SciBeta Developed ex USA Multi-Beta Multi-Strategy Four-Factor EW index.

The live performance data for all 17 of the Scientific Beta regional universes is available here.

Quarterly Smart Beta Performance Report for the Developed Universe, December 2016
Scientific Beta Developed Multi-Smart Factor Indices Factsheet, January 2017

White Papers

In a concern for transparency, and as part of its aim to help investors to understand and to invest in smart beta equity strategies, ERI Scientific Beta has published a large number of white papers that are freely available on the Scientific Beta platform.

Highlighted White Papers

Scientific Beta Multi Smart Factor Indices: An Introduction
February 2017

Smart Factor Indices and multi-beta indices provided by Scientific Beta are efficient and highly flexible tools for the construction of equity portfolios. This document reviews the conceptual groundings underpinning Scientific Beta’s index construction and reviews the empirical properties of the resulting indices. More specifically, we examine the foundations of smart beta and factor investing, before turning to a discussion on how the construction principles of ERI Scientific Beta's Smart Factor Indices lead to material improvements in performance, risk-adjusted performance and robustness relative to traditional factor indices. We introduce the two types of multi-factor indices offered by ERI Scientific Beta - with one targeting high diversification with factor risk control and the other aiming for high factor exposures without sacrificing diversification or the considerable benefits of the "top-down" approach to multi-factor investing - and emphasise the robustness benefits of employing a consistent index design framework, using consensual factor definitions, and aiming at broad diversification to avoid taking on unrewarded risks. We also include a discussion on ERI Scientific Beta's stock universe and practical implementation rules. Additionally, we report various performance and risk measures for Scientific Beta's Multi-Smart Factor Indices, including long-term data covering several decades. We conclude with an illustration of some of the extensions, dynamic allocations and customisations that can be applied on the SciBeta Multi-Factor index construction framework.

Accounting for Cross-Factor Interactions in Multi-Factor Portfolios: The Case for Multi-Beta Multi-Strategy High Factor Exposure Indices
February 2017

In this white paper we introduce Scientific Beta’s well-diversified "top-down" multi factor approaches and compare them with "bottom-up" score-weighting approaches that target high factor intensity. We find that focusing solely on increasing factor intensity leads to inefficiency in capturing factor premia, as exposure to unrewarded risks more than offsets the benefits of increased factor scores. High factor scores in "bottom-up" approaches also come with high instability and high turnover. We introduce an approach that considers cross-factor interactions in "top-down" portfolios through an adjustment at the stock selection level. While producing lower factor intensity than "bottom-up" methods, this approach leads to higher levels of diversification and produces higher returns per unit of factor intensity. We show that it dominates "bottom-up" approaches in terms of relative performance, while considerably reducing extreme relative losses and turnover.

All White Papers

• Accounting for Cross-Factor Interactions in Multi-Factor Portfolios: The Case for Multi-Beta Multi-Strategy High Factor Exposure Indices
• Scientific Beta Multi Smart Factor Indices: An Introduction
• Academic Lessons on Factor Investing
• The Dimensions of Quality Investing: High Profitability and Low Investment Smart Factor Indices
• Scientific Beta Low Carbon Multi-Beta Multi-Strategy Indices
• Robustness of Smart Beta Strategies
• Investability of Scientific Beta Indices
• Overview of Diversification Strategy Indices
• Scientific Beta Maximum Deconcentration Indices
• Scientific Beta Maximum Decorrelation Indices
• Scientific Beta Efficient Minimum Volatility Indices
• Scientific Beta Efficient Maximum Sharpe Ratio Indices
• Scientific Beta Diversified Risk Weighted Indices
• Scientific Beta Diversified Multi-Strategy Index
• Scientific Beta Global Universe
• Scientific Beta Analytics: Examining the Performance and Risks of Smart Beta Strategies

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Supplements in Partnership with Industry Publications

EDHEC-Risk Institute has established partnerships with a number of industry publications to produce special editorial supplements providing industry-relevant research of the highest academic standards.

P&I EDHEC-Risk Institute Research for Institutional Money Management
December 2016

This smart factor indexing special issue of the Research for Institutional Money Management supplement to P&I looks at how factor indexes have evolved, how to reconcile environmental and financial objectives using low carbon indexes, the difference between defensive strategies and the low risk factor, alternative approaches to limiting concentration in minimum and low volatility strategies, dynamic strategies that are defensive when needed, and the live performance of multi smart factor indexes.

How Factor Indexes have Evolved: Smart Factor Indexes, Multi-Beta Indexes, and Solutions
This article clarifies the conceptual underpinnings of factor investing in the equity space and the need for diversification. Choosing good factor tilts combined with well-diversified weighting schemes generates attractive and robust risk-adjusted performance.

Achieving Environmental and Financial Objectives with Low Carbon Smart Beta Indexes
Our research shows that it is possible to reconcile environmental and financial objectives using low carbon indexes introduced by ERI Scientific Beta which aim to outperform the market not because they are green, but because they are exposed to traditional risk premia and are better diversified than traditional cap-weighted indexes.

Difference between Defensive Strategies and the Low Risk Factor
In academia, there are two types of defensive equity strategies with very different objectives, but they are often confused as they both lead to a reduction in risk: low volatility factor harvesting strategies and minimum volatility strategies.

Alternative Approaches to Limiting Concentration in Minimum and Low Volatility Strategies
This article introduces alternative approaches to improving diversification in minimum and low volatility strategies. It shows how low volatility and minimum volatility strategies can be implemented with smart factor indexes tilted towards the Low Risk factor and documents their risk and performance using 45 years of U.S. large and mid-cap data.

Dynamic Strategies that are Defensive When Needed
The Scientific Beta Multi-Beta Multi-Strategy Relative Volatility (90%) solutions rely on risk-based allocation models to select and dynamically allocate to a diversity of smart factor indexes that are representative of six long-term risk premia to deliver a dissymmetric defensive profile.

The Live Performance of Multi Smart Factor Indexes
This article analyzes the Scientific Beta Multi-Beta Multi-Strategy Four-Factor EW index and presents its performance since its launch in December 2013. The index has displayed stable outperformance over the cap-weighted benchmark during the live period, as well as stable volatility reduction. These live results are consistent with the long-term back history of the index over 45 years.

Legal & General Investment Management America Launches ERI Scientific Beta Multi-Factor Commingled Funds for Institutional Investors

Legal & General Investment Management America, Inc. ("LGIMA") announced on 5 December, 2016 that its US index fund management business will be launching Scientific Beta Multi-factor strategies using commingled funds designed for institutional investors such as defined benefit, defined contribution, Taft-Hartley and public plans. LGIMA will be launching four funds, including the global, US, developed ex-US and emerging market components of the Scientific Beta Multi-factor indices. These indices will initially have exposure to the low volatility, value, momentum and size factors and will use innovative weighting methodologies that seek to improve diversification and risk-adjusted returns relative to market-cap weighted indices. "It's a perfect marriage of LGIMA's commingled fund platform that emphasizes a high level of governance, transparency and flexibility," said Greg Behar, Head of Index Strategy at LGIMA. "This marks the first time that US investors will be able to access Scientific Beta's academically-driven smart beta 2.0 methodology in a collective investment trust designed for institutional investors." Noël Amenc, CEO of ERI Scientific Beta, commented "We are delighted that LGIMA has chosen to launch an institutional commingled fund offering in North America that is based on ERI Scientific Beta indices. Our ambition is to serve North American asset owners with the most academically-proven and transparent smart factor and multi smart factor indices, and this new offering from LGIMA considerably expands the range of choices that institutional investors have at their disposal."

ERI Scientific Beta's "Robustness of Smart Beta Strategies" Wins Savvy Investor Best Smart Beta Paper Award for 2016

ERI Scientific Beta is delighted to announce that its research publication, "Robustness of Smart Beta Strategies," has won the Best Smart Beta Paper award in the Savvy Investor Awards 2016. The paper makes a distinction between relative robustness and absolute robustness. A strategy is assumed to be 'relatively robust' if it is able to deliver similar outperformance under similar market conditions by aligning well with the performance of the underlying factor exposure it is seeking and reducing unrewarded risks. Absolute robustness is the absence of pronounced state and/or time dependencies and a strategy shown to outperform irrespective of prevailing market conditions can be termed robust in absolute terms. The paper reviews the importance of robustness for smart beta strategies, explains various methods by which smart beta strategies try to improve robustness, and discusses how to measure and assess robustness in the performance analysis of smart beta strategies. The paper's intention is to provide useful orientation for investors on how to set suitable requirements for robustness. Savvy Investor is a leading research hub for the institutional investment industry. The site has over 15,500 members, who between them download more than 12,000 white papers every month.

Carbon Metrics on the Scientific Beta Platform Extended to All Indices in the Developed Regions

ERI Scientific Beta is pleased to announce that the analytics dedicated to carbon exposure that were previously available on the Scientific Beta platform for the Scientific Beta Multi-Beta Multi-Strategy Low Carbon indices, have now been extended to all indices built on the Developed and Extended Developed Europe universes and may be accessed free of charge. The carbon metrics report two measures: the carbon footprint, which represents the total emissions in relation to an investment of one billion US$ in the index, and the carbon intensity, which is the weighted average carbon intensity of the individual companies in the index.

Assets Replicating Scientific Beta’s Multi-Factor Indices Break Through the USD 12bn Barrier

ERI Scientific Beta has announced that assets tracking its smart beta indices had reached USD 12.3bn as of December 31, 2016. In terms of geographical distribution, these assets come from North America (60%), Europe (35%) and Asia-Pacific (5%). Compared to December 31, 2015, this amount of assets under replication represents growth of 45%. Among the drivers of this growth can be cited the success of the launch in 2016 of an offering reconciling a low carbon objective and multi-smart-factor portfolio construction, since this new series of multi-beta multi-strategy low carbon indices now represents more than USD 2 billion in assets under replication. Another driver of the growth in assets replicating Scientific Beta indices is their performance. The Scientific Beta multi-smart-factor indices have a live track record that shows annualised outperformance of over 2% compared to their cap-weighted benchmark.¹

¹The average live outperformance across all Scientific Beta developed regions of Scientific Beta Multi-Beta Multi-Strategy (Equal Weight and Relative Equal Risk Contribution) indices is 2.06% and 2.20% respectively. This live analysis is based on daily total returns in the period from December 20, 2013 (live date) to December 31, 2016, for the following developed world regions – USA, Eurozone, UK, Developed Europe ex UK, Japan, Developed Asia Pacific ex Japan, Developed ex UK, Developed ex USA and Developed. The benchmark used is a cap-weighted portfolio of all stocks in the respective Scientific Beta universes.

New Release of the Scientific Beta Platform Available

ERI Scientific Beta is pleased to announce the availability of a new version of the Scientific Beta platform from February 2017, further extending its offering in the area of smart beta indices and expanding the analytics capabilities. A new multi-smart factor index offering, Multi-Beta Multi-Strategy Diversified High-Factor-Exposure Indices, has been added which takes into account the interactions between factor indices by applying a filter to each selection of stocks exposed to the factor represented in the single factor index and includes the Multi-Beta Multi-Strategy Diversified High-Factor-Exposure Equal-Weight (EW) indices and the Multi-Beta Multi-Strategy Diversified Max Factor Exposure Solutions indices. Narrow versions of ERI Scientific Beta’s single smart factor indices, which are concentrated in the desired factor tilt where the initial factor selection targets 30% of the stocks instead of the standard 50%, are also now available. The analytics platform has been extended with the addition of the Score Factor Exposure, additional Gross Profitability and Total Asset Growth metrics in the Fundamentals analytics and the incorporation in the Risk Factor Exposure analytics of a Factor Intensity measure. Finally, a new Developed Europe region has been included, as the natural union of the "United Kingdom", "Eurozone" and "Developed Europe ex-UK ex-Euro" blocks. Further details of these new features may be found in the corresponding announcement.

Forthcoming Events:

EDHEC-Risk Smart Beta Day Europe 2017 Conference

21 March, 2017 – London (The Grange Tower Bridge Hotel)

The conference will present the asset owner and financial advisory communities with the latest conceptual advances and research results in smart beta investing, enabling their implications and applications to be discussed with researchers who combine expertise of advanced financial techniques with a sound awareness of their industry relevance.

Organised by EDHEC-Risk Institute in partnership with ERI Scientific Beta, this one-day conference offers a rich and varied programme consisting of multiple plenary sessions, allowing professionals to review major industry challenges, explore state-of-the-art investment techniques and benchmark practices to advances in research.

The conference will focus on smart beta indexation, factor investing and smart beta solutions and will present the latest research results on the challenges of smart beta investing for institutional investors, the best methods for the construction of multi-factor indices and the issue of choosing between approaches based on picking factor champion stocks, or rather maintaining an approach that focuses more on constructing portfolios with good beta properties, analysing the costs of smart beta strategies, evaluation criteria of a smart beta index and the case for long/short multi-factor strategies. For this last topic, the aim is to promote those approaches offering very strong factor spreads while also limiting their variation. This integrated approach breaks with the traditional practices of long/short factor investing, which are often based on poor risk management practices and inefficient design of the short leg. The conference will also present research of great interest to asset owners on defensive strategies and the means to limit their sensitivity to interest rates. Finally, the issue of reconciling factor investing, which generally increases the investment's carbon footprint and low carbon investing will also be addressed in this conference.

The conference is reserved for asset owners (including pension schemes, charities, endowments, foundations, insurance companies, single family offices and financial executives from non-financial companies) and institutional consultants. Admission is complimentary and by invitation only. For further information, please contact Joanne Finlay at joanne.finlay@edhec-risk.com.

"Bottom-Up Versus Top-Down: What is the Best Method to Use for the Construction of Multi-Factor Indices?" Webinar

9 March, 2017 – 17:00-17:45 Central European Time / 11:00-11:45 Eastern Standard Time

The webinar will compare "bottom-up" methodologies that rely on multi-factor score-weighting to build concentrated portfolios to achieve higher composite exposure across targeted factors with less concentrated "top-down" multi-factor approaches.

In light of increasing investor interest in multi-factor solutions, product providers have recently been debating the respective merits of the "top-down" and "bottom-up" approaches to multi-factor portfolio construction. Our recent research shows that focusing solely on increasing factor intensity leads to inefficiency in capturing factor premia, as exposure to unrewarded risks more than offsets the benefits of increased factor scores. High factor scores in "bottom-up" approaches also come with high instability and high turnover. Our approach considers cross-factor interactions in "top-down" portfolios through an adjustment at the stock selection level. This approach, while producing lower factor intensity than "bottom-up" methods, leads to higher levels of diversification and produces higher returns per unit of factor intensity. It dominates "bottom-up" approaches in terms of relative performance, while considerably reducing extreme relative losses and turnover. The objective of this webinar is to compare "bottom-up" methodologies that rely on multi-factor score-weighting to build concentrated portfolios to achieve higher composite exposure across targeted factors with less concentrated "top-down" multi-factor approaches. Topics covered will include "Considering cross-sectional negatives of single factor indices, seeking maximum exposure to rewarded factors, portfolio concentration versus diversification; what are the issues behind the bottom-up versus top-down debate?", "From beta to stock picking: do stock factor champions exist?", "What are the limits of bottom-up approaches?", "Can we reconcile the top-down approach and consideration of cross-sectional negatives of single smart factor indices combinations?" and "What method can be used to maximise the benefits of factor investing?". The webinar will be hosted by Felix Goltz, Head of Applied Research at EDHEC-Risk Institute and Research Director at ERI Scientific Beta.

To register, please visit the registration website or contact Severine Cibelly at severine.cibelly@scientificbeta.com for further information.

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Past Events:

 

"Smart Beta and Low Carbon Investing" Webinar

Discusses how it is possible to reconcile environmental and financial objectives using low carbon indices introduced by ERI Scientific Beta.

For some years now, there has been an increasing interest among investors in low carbon investments. EDHEC-Risk Institute has been conducting research for several years on the possibility of reconciling financial and environmental performance. The launch of a new series of low carbon indices by ERI Scientific Beta, the smart beta index provider set up by EDHEC-Risk Institute in 2012, marks the practical realisation of these research efforts. Our research shows that it is possible to reconcile environmental and financial objectives using low carbon indices introduced by ERI Scientific Beta. While on the one hand these indices achieve an environmental objective by excluding high carbon stocks, thus putting pressure on high polluting companies to reform, on the other hand these indices achieve a financial objective by retaining exposure to rewarded factors and by maintaining an appropriate level of diversification. The green indices that ERI Scientific Beta offers its institutional investors aim to outperform the market not because they are green, but because they are more exposed to traditional risk premia and better diversified than traditional cap-weighted indices.

Replay of the "Smart Beta and Low Carbon Investing" webinar broadcast on 8 November, 2016

"Active Manager Substitution" Webinar

Addresses the potential for smart beta strategies to replace or substitute active managers.

When smart beta strategies were first launched, there was much talk as to whether these smart beta indices, which have better risk-adjusted profiles than cap-weighted indices, would eventually end up being a substitute to cap-weighted indices or whether they were expected to deliver outperformance in a more cost-effective way than active managers and eventually end up substituting active managers. Academic evidence shows that while many investors have adopted smart beta, few believe that these smart beta strategies – even in the form of indices – can replace cap-weighted indices as a reference in asset allocation policy. This is not surprising, as the long-standing monopoly and popularity of cap-weighted indices as benchmarks, owing to their simplicity, are not easy to replace. As a consequence, smart beta techniques have found a rather broader application as a complement to cap-weighted indices or as a substitute for active managers.

Replay of the "Active Manager Substitution" webinar broadcast on 29 November, 2016

'Great rotation' highlights clash over unseen risks in factor investing

Risk.net (25/01/2017)

"(...) "What index providers are offering billed as factor investing based on academic consensus is no such thing," says Felix Goltz, head of applied research at EDHEC-Risk Institute in Nice. "The academic consensus exists around half a dozen factors, providing a sort of open-source due diligence for investors," he says. "But index providers have tweaked their definitions of factors to try to make them 'better'. The problem is, these 'better' examples haven't faced the same levels of scrutiny." (...)"
Copyright Incisive Risk Information (IP) Limited

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Smart Beta Assets Growing

Benefits and Pensions Monitor (23/01/2017)

"(...) Assets tracking the EDHEC Risk Institute's smart beta indices reached US$12.3 billion as of December 31. In terms of geographical distribution, these assets come from North America (60 per cent), Europe (35 per cent), and Asia-Pacific (five per cent). Compared to December 31, 2015, this represents growth of 45 per cent. (...)"
Copyright Benefits and Pensions Monitor

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Finalists Announced For 2016 ETF.com Awards

ETF.com (17/01/2017)

"(...) ETF.com and Inside ETFs are pleased to announce the finalists for the 2016 ETF.com awards. The awards are designed to recognize the people, companies and products that are driving the ETF industry forward and delivering new value to investors. (...)
Best Index Provider Website – 2016
Awarded to the most informative and user-friendly website by an index provider.
ERI Scientific Beta: This indexing website powerhouse actually lets users create their own benchmarks, emphasizing the factors that matter to them. http://www.scientificbeta.com (...)"
Copyright ETF.com

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ERI Scientific Beta: Scientific Beta multi-factor indices post impressive three-year live track record

Financial Investigator (04/01/2017)

"(...) The Scientific Beta Multi-Beta Multi-Strategy Equal Weight (EW) and Equal Risk Contribution (ERC) flagship indices in all regions have posted positive live performance in comparison with their cap-weighted counterparts, with an average annualised outperformance of 2.13% over the period since the indices went live on December 20, 2013 to the end of December 2016. (...)"
Copyright Financial Investigator Publishers

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Asia warms up to new ETF offerings

The Asset (29/12/2016)

"(...) "You need to rely on empirical evidence and multiple studies showing that factors have outperformed in the past, says Frederic Ducoulombier, founding director at EDHEC Risk Institute-Asia. While selecting the right factors, investors need to look for economic rationale justifying the model. "[They need to] make sure that there's a good story." (...)"
Copyright Asset Publishing and Research Limited

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LGIMA Launches New Scientific Beta Funds for Retirement Plans

Plan Sponsor (08/12/2016)

"(...) The U.S. index fund management business of Legal & General Investment Management America (LGIMA) is moving forward with Scientific Beta Multi-factor strategies using commingled funds designed for institutional investors. (...) LGIMA will be launching four funds comprising the global, U.S., developed ex-U.S., and emerging market components of the Scientific Beta Multi-factor indices. (...)"
Copyright Strategic Insight, Inc.

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FRENCH ETFS: France's ETF pioneers

Funds Europe (November 2016)

"(...) Amundi has partnered with a variety of index providers so is able to offer a broad range of products. It released its second product with Scientific Beta, the commercial arm of the EDHEC Institute, earlier this year. This multi-factor smart beta product adds to the firm's comprehensive range of mono-strategy ETFs, which include minimum volatility, mid-cap, small-cap, growth, value, high dividend and buybacks. (...)"
Copyright Funds Europe

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What's behind factor investing

Pensions Expert (31/10/2016)

Article by Felix Goltz, research director at index provider ERI Scientific Beta
"(...) With pension funds increasingly embracing risk factor investing, it is essential to ensure a factor premium is supported by empirical analysis, economic rationale and a simple factor definition. (...) It is daunting to conduct empirical analysis to establish which risk factors carry a reward. Researchers struggle to estimate expected returns, simply because they rely on very few data points: the starting price level and the end date price level. This is also true for factor returns. (...)"
Copyright The Financial Times Limited

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Goldman’s Multifactor Robots: A Post-Human Investing Guide

Bloomberg (27/10/2016)

"(...) "The factors that have been proven again and again are simple," said Felix Goltz at EDHEC Risk Institute, part of a team that published an April study countering accusations that factor investing provides a mathematical gloss to random returns. "Very often, providers come up with complex factor definitions that are original and different from the academic work, with several adjustments. You come up with a result that could be a fluke." (...)"
Copyright Bloomberg L.P.

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Why investors need to analyse smart beta

Citywire New Model Adviser (26/10/2016)

"(...) A new study, Investor Perceptions about Smart Beta ETFs, by Noel Amenc, Felix Goltz, and Veronique Le Sourd, suggests investors are not approaching beta particularly smartly. Issued in August by the EDHEC-Risk Institute, the report asked professional investors how much time they spent evaluating traditional passive investments, active managers and smart beta or systematic factor investments. (...)"
Copyright citywire.co.uk

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Mixed Vs. Integrated Multifactor ETF Portfolios

ETF.com (21/10/2016)

"(...) A mixed approach is one in which a portfolio is built for each target factor individually, and those portfolios are combined as sleeves to create a multifactor portfolio. (...) ETFs Using a Mixed Approach: Global X Scientific Beta US Equity ETF (SCIU). (...) At Newfound, we currently advocate for a mixed approach. (...) To date, the majority of research has substantiated the individual factors as historically reliable ways to generate excess risk-adjusted returns; evidence suggesting that securities with multiple simultaneous factor exposures are better is still lacking. (...)"
Copyright ETF.com

Operations Engineer, ERI Scientific Beta (Nice, France)

As part of its policy of transferring know-how to the industry, EDHEC-Risk Institute has set up ERI Scientific Beta. ERI Scientific Beta is an original initiative which aims to favour the adoption of the latest advances in smart beta design and implementation by the whole investment industry. Its academic origin provides the foundation for its strategy: offer, in the best economic conditions possible, the smart beta solutions that are most proven scientifically with full transparency of both the methods and the associated risks.

As part of its international development programme and in order to strengthen its equity index development activity, the EDHEC group, one of Europe’s leading research and teaching institutions, is recruiting an Operations Engineer in IT/Finance for ERI Scientific Beta in Nice, France.

Main missions:

• Run index valuation batches on a daily basis (shift working) to ensure data accuracy (monitoring and analysis of automated alerts, data discrepancy analysis and resolution, reporting).
• Work in coordination with corporate actions analysts to ensure all corporate actions are reflected in the system in accordance with the index valuation methodology.
• Run index analytics calculation batches and associated quality checks.
• Take part in the equity universe maintenance (data quality checks).
• Liaise with data providers when necessary.
• Keep the overall system up and running by taking charge of any technical alerts (mirroring, backups, performance and resource usage monitoring).
• Suggest any improvements to the existing processes. Participate in their implementation.
• Customer support.

Experience and education:

• Bachelor or Master Degree in finance, accounting, engineering, IT or other related fields.
• Prior experience in banking/finance industry (run) is required.

Skills:

• Proficiency in SQL and scripting on Windows and Linux platforms. Knowledge of Matlab would be an asset.
• Familiarity with spreadsheet products and proficiency in data management.
• Written and spoken English is essential; spoken French would be an asset.
• The role requires an individual with a high level of attention to detail and strong problem-solving ability.
• The successful candidate will be able to call on dual skills to master both the technical and business aspects of the whole process.

More information about ERI Scientific Beta on www.scientificbeta.com.
The ERI Scientific Beta corporate brochure can be accessed here.
The EDHEC-Risk Institute corporate brochure can be accessed here.

To apply, please send your CV and a cover letter to Laurence Kriloff: laurence.kriloff@edhec-risk.com.

The salary will be determined according to the EDHEC pay scale, based on qualifications and prior experience.

ERI Scientific Beta

ERI Scientific Beta aims to be the first provider of a smart beta platform to help investors understand and invest in advanced beta equity strategies. It has three principles:

• Choice: A multitude of strategies are available allowing users to build their own benchmark, choosing the risks to which they wish, or do not wish, to be exposed. This approach, which makes investors responsible for their own risk choices, referred to as Smart Beta 2.0, is the core component of the index offerings proposed by ERI Scientific Beta.
  
  
• Transparency: The rules for all of the Scientific Beta series are replicable and transparent. The track records of the Scientific Beta indices can be checked and justified through access to historical compositions.
  
  
• Clarity: Exhaustive explanations of construction methodologies are provided, as well as detailed performance and risk analytics.

Established by EDHEC-Risk Institute, one of the very top academic institutions in the field of fundamental and applied research for the investment industry, ERI Scientific Beta shares the same concern for scientific rigour and veracity, which it applies to all the services that it offers investors and asset managers.

The ERI Scientific Beta offering covers three major services:

• Scientific Beta Indices: Scientific Beta Indices are smart beta indices that aim to be the reference for the investment and analysis of alternative beta strategies. Scientific Beta Indices reflect the state-of-the-art in the construction of different alternative beta strategies and allow for a flexible choice among a wide range of options at each stage of their construction process. This choice enables users of the platform to construct their own benchmark, thus controlling the risks of investing in this new type of beta (Smart Beta 2.0). Within the framework of Smart Beta 2.0 offerings, ERI Scientific Beta provides access to smart factor indices, which give exposure to risk factors that are well rewarded over the long term while at the same time diversifying away unrewarded specific risks. By combining these smart factor indices, one can design very high performance passive investment solutions.
  
  
• Scientific Beta Analytics: Scientific Beta Analytics are detailed analytics and exhaustive information on its smart beta indices to allow investors to evaluate the advanced beta strategies in terms of risk and performance. The analytics capabilities include risk and performance assessments, factor and sector attribution, and relative risk assessment. Scientific Beta Analytics also allow the liquidity, turnover and diversification quality of the indices offered to be analysed. In the same way, analytics provide an evaluation of the probability of out-of-sample outperformance of the various strategies present on the platform. We believe that it is important for investors to be able to conduct their own analyses, select their preferred time period and choose among a wide range of analytics in order to produce their own picture of strategy performance and risk.
  
  
• Scientific Beta Fully-Customised Benchmarks and Smart Beta Solutions: Scientific Beta Fully-Customised Benchmarks and Smart Beta Solutions is a service proposed by ERI Scientific Beta, and its partners, in the context of an advisory relationship for the construction and implementation of benchmarks specially designed to meet the specific objectives and constraints of investors and asset managers. This service notably offers the possibility of determining specific combinations of factors, considering optimal combinations of smart beta strategies, defining a stock universe specific to the investor, and taking account of specific risk constraints during the benchmark construction process.

With a concern to provide worldwide client servicing, ERI Scientific Beta is present in Boston, London, Nice, Singapore and Tokyo. As of December 31, 2016, the Scientific Beta indices corresponded to USD 12.3bn in assets under replication.

ERI Scientific Beta has a dedicated team of 45 people who cover not only client support from Nice, Singapore and Boston, but also the development, production and promotion of its index offering. ERI Scientific Beta signed the United Nations-supported Principles for Responsible Investment (PRI) on September 27, 2016.

ERI Scientific Beta
1 George Street, #07-02, Singapore 049145
Tel. +33 493 187 851 (from 9.00am to 6.00pm CET)
E-mail: clientservices@scientificbeta.com | Website: www.scientificbeta.com

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