Nothing to Hide

With smart beta, passive investment is experiencing a veritable revolution. Up until now, the commitment that index providers and passive managers made was to allow investors to access the average return of the market in the best economic conditions possible. This average was understood to be the performance of a cap-weighted index. In the end, passive managers or index providers, unlike active managers, were taking no reputation risk, since they were not responsible for the performance delivered to investors. It was the market.

In addition, whatever the prevailing financial climate, and notably following the various financial crises over the past twenty years, this lack of reputation risk has enabled passive managers to increase their assets under management. They were not blamed for fragile promises of outperformance or absolute performance that could not stand up to the volatility of the markets. With the appearance of smart beta, the whole promise of passive investment changes. It no longer involves performing like the market, but instead involves beating the market. In that sense, and indeed that is how smart beta is sold and how it is progressing, passive managers with smart beta make the same promise as active managers. De facto, they substitute a relative risk budget, with respect to the cap-weighted benchmark used by active managers, to the benefit of passive managers. This use of the investor’s relative risk budget is based on methods and added value that used to be marketed by active investment management, whether by implementing portfolio diversification and/or exposing the portfolio to risk factors that are better rewarded than those that drive the performance of cap-weighted indices. The success of smart beta is in fact based on two main arguments that are quite different in nature, but nonetheless complementary.

The first is that the value-creating elements that are well documented in the academic literature and are subject to consensus to the point of being supported by Nobel Prize winners, like the importance of choosing factors that are rewarded over the long term, or diversification, are often compromised by tactical bets on factors, sectors or countries, or forecasts on future stock prices made by active managers. It is this set of costly to implement and ultimately, on average, value-destroying, elements that smart beta, in its passive version, wishes to avoid by proposing, through systematic index rebalancing methodologies, to leave no room for discretionary decisions.

This argument is supported by numerous academic and empirical studies, which show that, over the long term, manager alpha that comes from tactical bets or stock picking is not persistent, and that active managers underperform their benchmarks on average.

The second argument is economic. By abstaining from implementing tactical bets or stock picking, which correspond to more than 80% of investment management costs, passive smart beta investment is in a position to deliver performance at a much lower cost price.

Put together, these two arguments therefore provide the best of both worlds for the investor, since smart beta can deliver robust and inexpensive outperformance.

Unfortunately, it is clear that the industry does not really know how to take advantage of this positive paradigm today and compromises the promise of smart beta through poor practices that most often aim to attempt to offer the most attractive in-sample performances, to the detriment of their robustness¹.

As is often the case, these poor practices are hidden. On the pretext of protecting sales secrets, which can be protected by patents even though they are promoted with reference to academic research that is freely accessible, smart beta index offerings are marketed in a completely opaque manner².

With the exception of confidential bilateral relationships, which by definition do not improve market information, the information required to evaluate the robustness of the performance displayed by commercialised smart beta indices is not made available to all investors or, above all, to competitors, who are those who will have most interest in criticising their competitors’ offerings. Ultimately, investors in smart beta indices are like investors who would accept to invest in listed stocks on non-arbitraged markets, where there would be no publication of accounts, and only buyers could have confidential access to earnings reports and balance sheets…

Even though the historical performances of these indices are simulated for the most part, it is not possible to check the accuracy and the quality of these track records because the market does not have sufficiently detailed historical compositions and construction methodologies to be able to replicate these performances.

This opacity is not called into question by the regulators, because none of the SEC, the European Commission or IOSCO, in their regulations or proposals to improve the reliability of indices, has advocated the application of genuine transparency, preferring to refer index providers to financial responsibilities framed by reinforcement of their governance, even though everyone knows that reinforcing governance obligations has never enabled investors to be properly protected in the past, and what is more, it does not in any way allow the risks of the indices and the robustness of the outperformance displayed to be qualified.

Like any opaque market, that of smart beta indices is logically faced with adverse selection phenomena. Lacking genuine transparency on the quality of the outperformance displayed, investors have no choice but to refer to the only elements that are tangible and, by definition, robust out of sample: the fees. The incentive is not to do it well, but to do it cheaper, even if this means sacrificing indispensable R&D.

In the absence of being submitted to robustness checks, which are made impossible by the opacity on the construction methods employed, the temptation is large for smart beta providers to try to improve their performance in sample, whether it involves practicing factor mining, factor fishing or model mining.

It is often the sales talent or the branding of the company that promotes “their” smart beta, rather than the quality of the smart beta, that leads to success. In that case, the assets under management raised are not relevant, because they are not the result of a decision from an informed market.

The live performances of the smart beta indices that are the most popular and were the most commercially successful at their creation, are not the best today³. While there is a definite connection between assets raised and performance in active investment management, this is not the case in smart beta.

It was due to these observations that EDHEC Risk Institute, a not-for-profit academic institution, set up ERI Scientific Beta at the end of 2012.

The aim of ERI Scientific Beta is to provide access, from a platform that is freely accessible to all, to the most detailed information possible on the risks, compositions and methodologies of 2,767 smart beta indices that are representative of the rewarded factors documented in the academic literature and on their implementation within diversified indices using methodologies that have also been the subject of numerous publications.

As part of the Smart Beta 2.0 approach⁴, the www.scientificbeta.com platform also allows investors to check the impact of a change in weighting methodology, constraints and choice of factors on not only the performance and risks, but also the robustness of strategies.

The economic model of this initiative is simple. We do not charge for access to the information and merely invoice the replication support services for the indices that are available with complete transparency on the platform. To date, the platform counts more than 17,000 users. We are proud of this success, which we hope will contribute to an improvement in the level of transparency of the smart beta market.

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Footnotes:

¹For more details on the subject of the robustness of smart beta strategies and indices, please refer to Amenc, N., F. Goltz, A. Lodh and S. Sivasubramanian, October 2014, Robustness of Smart Beta Strategies, ERI Scientific Beta Publication.

²Very few indices, including those that are subject to replication by UCITS or by mutual funds in the US, meet the requirements of transparency on historical compositions or transparency on methods. For more information, please refer to Amenc, N. and F. Ducoulombier, March 2014, Index Transparency – A Survey of European Investors’ Perceptions, Needs and Expectations, EDHEC Risk Institute Publication.

³For the Developed World universe over the past 5 years of live performance (December 31, 2009 to December 31, 2014), taking MSCI World as the reference cap-weighted index, the MSCI Minimum Volatility and FTSE RAFI indices, which are the most popular smart beta indices in the sense of those with the largest amount of assets under management replicating them, post respective relative returns of +1.32% and -1.00%. This can be compared with a relative return of 2.09% for the FTSE EDHEC-Risk Efficient index, which corresponds to an index with one of the best levels of robustness of live performance since it was set up.

⁴EDHEC Risk Institute is responsible for an original smart beta index construction approach, termed “Smart Beta 2.0,” which distinguishes between the choice of factors and the choice of diversification scheme, cf. Amenc, N., F. Goltz and A. Lodh. Choose Your Betas: Benchmarking Alternative Equity Index Strategies, Fall 2012, Journal of Portfolio Management. Amenc, N. and F. Goltz. Smart Beta 2.0, Winter 2013, Journal of Index Investing.

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Noël Amenc is Professor of Finance at EDHEC Business School, Director of EDHEC-Risk Institute and CEO of ERI Scientific Beta. He has conducted active research in the fields of quantitative equity management, portfolio performance analysis, and active asset allocation, resulting in numerous academic and practitioner articles and books. He is on the editorial board of the Journal of Portfolio Management and serves as associate editor of the Journal of Alternative Investments and the Journal of Index Investing. He is a member of the Monetary Authority of Singapore Finance Research Council. He co-heads EDHEC-Risk Institute's research on the regulation of investment management. He holds a master's in economics and a PhD in finance.

Efficient Maximum Sharpe Ratio Indices: Looking Back at 5 years of Live Performance

EDHEC-Risk Institute has been active in providing smart beta equity indices since 2009. While it began by implementing Efficient Maximum Sharpe Ratio indices through a partnership with FTSE, it has continued to expand its smart beta offerings ever since 2009, notably with the launch of its dedicated index platform, Scientific Beta, in 2013. This article looks back at the live performance of the Efficient Maximum Sharpe Ratio (MSR) indices and compares it both to smart beta indices from other providers and EDHEC-Risk Institute's more recent index offerings, which allow the Efficient Maximum Sharpe Ratio weighting scheme to be combined with explicit factor tilts, as well as with additional weighting schemes.

1. Live Performance and Risk of FTSE EDHEC-Risk Efficient Indices and Comparison with US Long-Term Track Records for Efficient MSR

The FTSE EDHEC-Risk Efficient Indices use the Efficient Maximum Sharpe Ratio weighting methodology to reweight stocks in the FTSE parent index in order to improve diversification and obtain an efficient risk/reward profile of the index. This methodology is the result of research conducted by the research team at EDHEC-Risk Institute.

The observation underlying the implementation of this methodology is simple. Two outstanding portfolios exist on the efficient frontier, which represents all portfolios with the best possible return for a given level of risk. All these portfolios result from diversification termed "efficient". These two portfolios are remarkable since the first, a minimum variance portfolio, corresponds to an efficient portfolio with the smallest return, while the second, the Maximum Sharpe Ratio portfolio has the best risk-adjusted return made possible through diversification. Traditionally, quantitative managers, and more recently smart beta index providers, have attempted to proxy the minimum variance portfolio since the calculation of the latter does not require an estimation of expected returns. An estimation of the variance-covariance matrix alone suffices to determine the efficient portfolio of minimal risk. However, in practice, these minimum variance portfolios are not always very well-diversified. The search for the minimal risk portfolio results in concentrating the portfolio in a very small number of low-volatility stocks and gives these portfolios a very defensive character (low beta portfolio) which does not enable them to take full advantage of periods when markets are rising. This concentration problem has led managers and suppliers of minimum variance or minimum volatility portfolios or indices to use deconcentration constraints to construct the portfolios which, when they are very rigid, deteriorate significantly the performance of the latter out-of-sample. That is why EDHEC-Risk Institute is proposing, on the basis of research work¹ undertaken by one of its eminent members, Professor Raman Uppal, to introduce norm constraints that are no longer constraints on the minimum or maximum weight of the stocks but on an effective minimum number of stocks. This enables the diversification of the Scientific Beta Efficient Minimum Volatility indices to be improved and to obtain better out-of-sample performance.

The second outstanding portfolio is the Maximum Sharpe Ratio portfolio. This porfolio is, in principle, the best portfolio in terms of risk-adjusted performance and the only one that investors should hold. However, its estimation is extremely non-robust out-of-sample as it requires the use of expected returns that cannot be estimated on the basis of past returns. To circumvent this problem, EDHEC-Risk Institute's teams have introduced the hypothesis of a positive link over the long term between the risk of stocks, measured by their semi-deviation, and their return. This hypothesis, which has been validated by extensive academic research work, ultimately enables a robust proxy of the hierarchy of stock returns to be obtained. This risk-based methodology was the subject of a major academic publication² and has been reflected in the index offering promoted on the Scientific Beta platform. For further details about this methodology, please refer to the corresponding white paper, "Scientific Beta Efficient Maximum Sharpe Ratio Indices"³.

Performance of FTSE EDHEC-Risk Efficient indices since their live date for different regions

Exhibit 1: Live Performance Analysis – FTSE EDHEC-Risk Efficient Indices in Different Geographical Regions

Analysis Period 23/11/2009 to 31/12/2014	USA		UK		Eurozone		Japan		Developed Asia-Pacific ex-Japan
	Broad CW	FTSE EDHEC-Risk Efficient	Broad CW	FTSE EDHEC-Risk Efficient	Broad CW	FTSE EDHEC-Risk Efficient	Broad CW	FTSE EDHEC-Risk Efficient	Broad CW	FTSE EDHEC-Risk Efficient
Annual Returns	15.22%	18.43%	8.08%	10.52%	7.16%	8.68%	12.70%	13.91%	5.75%	7.87%
Annual Volatility	15.82%	16.01%	15.53%	14.91%	19.92%	17.22%	19.84%	18.25%	18.34%	15.25%
Sharpe Ratio	0.96	1.15	0.49	0.68	0.33	0.47	0.64	0.76	0.31	0.51
Maximum Drawdown	18.58%	19.11%	17.12%	15.29%	30.14%	26.50%	27.81%	23.60%	31.05%	26.45%
Annual Relative Returns	-	3.21%	-	2.44%	-	1.53%	-	1.21%	-	2.12%
Tracking Error	-	2.64%	-	4.26%	-	5.01%	-	4.06%	-	5.05%
Information Ratio	-	1.21	-	0.57	-	0.30	-	0.30	-	0.42
95% Tracking Error	-	3.44%	-	5.16%	-	6.61%	-	5.14%	-	6.44%
Maximum Relative Drawdown	-	4.22%	-	6.64%	-	8.51%	-	9.37%	-	6.64%

The table shows the return and risk performance of FTSE EDHEC-Risk Efficient indices across different geographical regions: USA, UK, Eurozone, Japan and Developed Asia-Pacific ex-Japan. All statistics are annualised and daily total returns from 23 November 2009 to 31 December 2014 are used for the analysis. Returns are in USD/GBP/EUR/JPY/USD currencies for USA/UK/Eurozone/Japan/Developed Asia-Pacific ex-Japan respectively. The "Secondary Market US Treasury Bills (3M)" is the risk-free rate in US Dollars for USA and Developed Asia-Pacific ex-Japan. The "UK Treasury Bill Tender (3M)" is the risk-free rate in British Pounds for UK. "Euribor (3M)" is the risk-free rate in Euros for Eurozone, and "Japan Gensaki T-Bill (1M)" is the risk-free rate in Japanese Yen for Japan. The cap-weighted benchmark is the SciBeta CW index of the corresponding universe. Source: scientificbeta.com.

The performance and risk statistics across the five major regions show substantial outperformance, with a great level of consistency across regions. Annual relative returns over the cap-weighted reference index range from 1.21% for Japan to 5.05% for Developed Asia-Pacific ex-Japan. For the important US market, live returns of efficient indices have exceeded those of cap-weighted indices by more than 3% annually. It should be noted that volatility is also lower than, or similar to, that of cap-weighted indices, leading to a pronounced increase in the Sharpe Ratio, which is well in line with the objective of these indices. Over the whole Developed region, the annual outperformance of the FTSE EDHEC-Risk Efficient index is 2.59%, with an improvement in the Sharpe Ratio of 29.29% in relation to the SciBeta Developed Cap-Weighted index.

Comparison with US long-term track records from 1975 to 2014 for the Scientific Beta Efficient MSR

It is instructive to compare the results obtained over the recent live period with the long-term results, as evidenced by backtested data over 40 years. This section conducts such an analysis by juxtaposing the results for the live period of FTSE EDHEC-Risk Efficient Indices for US stocks with the backtested data for US long-term track records for ERI Scientific Beta strategies that use the same weighting method, namely the Efficient Maximum Sharpe Ratio weighting scheme, and a universe of similar stocks.

Exhibit 2: Performance Analysis – FTSE EDHEC-Risk Efficient USA Index and SciBeta USA Long-Term Efficient Maximum Sharpe Ratio Index

Performance Analysis	23/11/2009 to 31/12/2014	31/12/1974 to 31/12/2014
	USA FTSE EDHEC-Risk Efficient	USA LTTR Efficient Maximum Sharpe Ratio
Annual Returns	18.43%	15.03%
Annual Volatility	16.01%	15.76%
Sharpe Ratio	1.15	0.63
Maximum Drawdown	19.11%	53.22%
Annual Relative Returns	3.21%	2.87%
Tracking Error	2.64%	4.33%
Information Ratio	1.21	0.66
95% Tracking Error	3.44%	7.26%
Maximum Relative Drawdown	4.22%	30.66%

The table shows the return and risk performance of the FTSE EDHEC-Risk Efficient USA index and the SciBeta USA Long-Term Efficient Maximum Sharpe Ratio index. All statistics are annualised and daily total returns from 23 November 2009 to 31 December 2014 are used for the FTSE EDHEC-Risk Efficient USA index and from 31 December 1974 to 31 December 2014 for the SciBeta USA Long-Term Efficient Maximum Sharpe Ratio index. Returns are in USD. The "Secondary Market US Treasury Bills (3M)" is the risk-free rate in US Dollars for USA. The cap-weighted benchmark is the SciBeta USA CW index for the USA FTSE EDHEC-Risk Efficient index and for the USA LTTR Efficient MSR index, the benchmark is based on the 500 largest market cap US stocks. Source: scientificbeta.com.

The performance and risk statistics suggest that the 5 year live performance shows striking resemblance to the 40 year historical backtest. Relative annualised returns over the cap-weighted reference index are 3.21% in live data compared to 2.87% in the long-term backtest. It can therefore be concluded that the outperformance potential documented in long-term historical backtests for this method provides a reliable indication for live outperformance – even though the live performance achieved is slightly underestimated. Results are also broadly similar in terms of factor exposures and conditional performance, where the properties during the live period are similar to those obtained in the historical backtest.

Exhibit 3: Carhart Four-Factor Regression – FTSE EDHEC-Risk Efficient USA Index and SciBeta USA Long-Term Efficient Maximum Sharpe Ratio Index

Carhart Regression Analysis	23/11/2009 to 31/12/2014	31/12/1974 to 31/12/2014
	USA FTSE EDHEC-Risk Efficient	USA LTTR Efficient Maximum Sharpe Ratio
Annual Alpha	3.04%	1.96%
Market Beta	0.95	0.91
SMB Beta	0.17	0.15
HML Beta	-0.02	0.11
MOM Beta	0.06	0.01
R-Squared	98.2%	95.9%

The table shows the conditional performance and risk of the FTSE EDHEC-Risk Efficient USA index and the SciBeta USA Long-Term Efficient Maximum Sharpe Ratio index. All statistics are annualised. Analysis is based on daily total returns from 23 November 2009 to 31 December 2014 for the FTSE EDHEC-Risk Efficient index and from 31 December 1974 to 31 December 2014 for the USA LTTR. The Market factor is the daily return of the cap-weighted index of all stocks that constitute the index portfolio in excess of the risk-free rate. Small size factor is the daily return series of a cap-weighted portfolio that is long 30% smallest market cap stocks portfolios and short 30% largest market cap stocks of the universe. Value factor is the daily return series of a cap-weighted portfolio that is long 30% highest and short 30% lowest B/M ratio stocks of the universe. Momentum factor is the daily return series of a cap-weighted portfolio that is long 30% highest and short 30% lowest 52 weeks (minus most recent 4 weeks) past return stocks of the universe. The "Secondary Market US Treasury Bills (3M)" is the risk-free rate in US Dollars for USA. The broad cap-weighted index of the corresponding region is used as the benchmark. Coefficients that are statistically significant at 95% confidence level are highlighted in bold. Source: scientificbeta.com.

Exhibit 4: Conditional Performance Analysis – FTSE EDHEC-Risk Efficient USA Index and SciBeta USA Long-Term Efficient Maximum Sharpe Ratio Index

Conditional Performance Analysis	23/11/2009 to 31/12/2014	31/12/1974 to 31/12/2014
	USA FTSE EDHEC-Risk Efficient	USA LTTR Efficient Maximum Sharpe Ratio
Bull Markets
Annual Relative Returns	2.72%	2.10%
Tracking Error	2.47%	3.69%
Information Ratio	1.10	0.57
Bear Markets
Annual Relative Returns	2.45%	3.78%
Tracking Error	3.20%	5.62%
Information Ratio	0.77	0.67

The table shows the conditional performance and risk of the FTSE EDHEC-Risk Efficient USA index and the SciBeta USA Long-Term Efficient Maximum Sharpe Ratio index. Calendar quarters with the corresponding region’s positive cap-weighted benchmark returns comprise bull markets and the rest constitute bear markets. All statistics are annualised. Analysis is based on daily total returns from 23 November 2009 to 31 December 2014 for the FTSE EDHEC-Risk Efficient index and from 31 December 1974 to 31 December 2014 for the USA LTTR. The broad cap-weighted index of the corresponding region is used as the benchmark. Source: scientificbeta.com.

2. Comparison with Live Performance of Well-Known Competing Indices

Section 1. provided ample evidence that performance over the live period of the Efficient MSR indices has been attractive, and well-aligned with the longer historical backtest period. It is also interesting to compare the live performance to that of other smart beta indices which rely on different concepts to generate outperformance. While there is now a plethora of smart beta indices available, we focus here on the most popular indices that also have long live periods. We notably consider the following competitors to the Efficient MSR indices: the FTSE RAFI 1000 index, the MSCI Min Vol US index, and the S&P 500 EW index. These three indices, through both institutional mandates or ETPs, are those with the largest AUM in terms of replication. The RAFI 1000 index uses a composite fundamental metric of firm size both to select stocks and to attribute weights. The two other indices reweight the constituents of the standard cap-weighted parent index, using a portfolio optimisation with the objective to minimise volatility, respectively attributing simple equal weights. For comparison with the results of the Efficient MSR indices, we provide results for the period from November 23, 2009 to 31 December 2014, as above. We focus on US data, as data for other regions is not consistently available across the different competing indices.

The performance statistics in the table below reveal that some smart beta indices exhibited outperformance over cap-weighted indices of almost negligible magnitude. Annual relative returns of both the RAFI index and the MSCI Min Vol index fall short of 1%. In this context, it is all the more remarkable that the efficient MSR indices have achieved considerable outperformance over this period which is well in-line with their long-term backtest. The equal-weighted index for the US achieved outperformance of more than 2.5% over the period, slightly lower than the performance of the Efficient MSR indices. However, since the tracking error of the latter is lower, the equal-weighted index does not reach the same level of relative risk-adjusted performance as that of the FTSE EDHEC-Risk Efficient index which leads to an Information Ratio of 1.21 for the FTSE EDHEC-Risk Efficient index and only 0.9 for the equal-weighted index. It should also be noted that the MSCI Min Vol index clearly achieves the lowest volatility among all four indices over this period, suggesting alignment with its volatility minimisation objective.

Exhibit 5: Performance Analysis – FTSE EDHEC-Risk Efficient USA Index and its Competitors

USA 23/11/2009 to 31/12/2014	Broad CW	FTSE EDHEC-Risk Efficient	FTSE RAFI	MSCI Min Vol	S&P 500 EW
Annual Returns	15.22%	18.43%	16.20%	15.93%	17.79%
Annual Volatility	15.82%	16.01%	16.60%	11.92%	17.54%
Sharpe Ratio	0.96	1.15	0.97	1.33	1.01
Maximum Drawdown	18.58%	19.11%	21.08%	13.98%	22.71%
Annual Relative Returns	-	3.21%	0.97%	0.71%	2.56%
Tracking Error	-	2.64%	2.20%	5.46%	2.85%
Information Ratio	-	1.21	0.44	0.13	0.90
95% Tracking Error	-	3.44%	2.44%	7.74%	3.82%
Maximum Relative Drawdown	-	4.22%	4.92%	12.04%	6.94%

The table shows the return and risk performance of the FTSE EDHEC-Risk Efficient USA index and its competitors: FTSE RAFI US 1000 index, MSCI Minimum Volatility index and S&P 500 Equal Weight index. All statistics are annualised and daily total returns from 23 November 2009 to 31 December 2014 are used. Returns are in USD. The "Secondary Market US Treasury Bills (3M)" is the risk-free rate in US Dollars for USA. The cap-weighted benchmark is the SciBeta USA CW index. FTSE® is a registered trade mark of the London Stock Exchange Plc and The Financial Times Limited. RAFI® is a registered trademark of Research Affiliates, LLC. MSCI® is a registered trademark of MSCI Inc. S&P® and S&P 500® are registered trademarks of Standard & Poor’s Financial Services LLC ("S&P"), a subsidiary of The McGraw-Hill Companies, Inc. Source: scientificbeta.com.

The table below provides an overview of conditional performance, depending on market regimes (bull and bear markets). The most noteworthy finding is the clear defensive profile of the MSCI Min Vol strategy, as it delivered spectacular outperformance during bear periods but severe underperformance during the bull periods. The RAFI index, on the other hand, posted greater outperformance during bull periods. The FTSE EDHEC-Risk Efficient MSR index again showed a balanced behaviour over its live period, with similar outperformance in bull and bear periods. It is this balanced behaviour that ultimately allows it to appear as the best performing index over a contrasted period with a succession of pronounced bull and bear periods.

Exhibit 6: Conditional Performance Analysis – FTSE EDHEC-Risk Efficient USA Index and its Competitors

USA 23/11/2009 to 31/12/2014	FTSE EDHEC-Risk Efficient	FTSE RAFI	MSCI Min Vol	S&P 500 EW
Bull Markets
Annual Relative Returns	2.72%	1.62%	-4.94%	3.44%
Tracking Error	2.47%	1.98%	4.96%	2.58%
Information Ratio	1.10	0.82	-1.00	1.33
Bear Markets
Annual Relative Returns	2.45%	0.06%	14.85%	-1.23%
Tracking Error	3.20%	2.52%	7.17%	3.66%
Information Ratio	0.77	0.02	2.07	-0.34

The table shows the conditional performance and risk of the FTSE EDHEC-Risk Efficient USA index and its competitors: FTSE RAFI US 1000 index, MSCI Minimum Volatility index and S&P 500 Equal Weight index. Calendar quarters with the corresponding region’s positive cap-weighted benchmark returns comprise bull markets and the rest constitute bear markets. All statistics are annualised. Analysis is based on daily total returns from 23 November 2009 to 31 December 2014. The cap-weighted benchmark is the SciBeta USA CW index. FTSE® is a registered trade mark of the London Stock Exchange Plc and The Financial Times Limited. RAFI® is a registered trademark of Research Affiliates, LLC. MSCI® is a registered trademark of MSCI Inc. S&P® and S&P 500® are registered trademarks of Standard & Poor’s Financial Services LLC ("S&P"), a subsidiary of The McGraw-Hill Companies, Inc. Source: scientificbeta.com.

3. Introducing Multi-Strategy Weighting

Scientific Beta offers many other diversification weighting schemes in addition to the Maximum Sharpe Ratio weighting scheme, each targeting a unique objective. Even though the different weighting schemes offer efficient diversification of stocks, there is an additional need for diversification of the weighting schemes to diversify away the strategy-specific risks – a concept called "Diversifying the Diversifiers"⁴. The combination of different strategies allows the diversification of risks that are specific to each strategy by exploiting the imperfect correlation between the different strategies. Thus, diversifying the model risks further reduces the unrewarded risks and renders the weighting scheme more robust. ERI Scientific Beta proposes a flagship offering of smart beta indices based on this concept. The Diversified Multi-Strategy index combines, in equal proportions, the Efficient Maximum Sharpe Ratio, the Efficient Minimum Volatility, the Maximum Deconcentration, the Maximum Decorrelation and the Diversified Risk Weighted weighting schemes. These indices, being better diversified, enable outperformance to be obtained over the long term compared to mono-strategy indices. The details of this offering may be consulted here.

Exhibit 7 presents the return/risk performance analysis of the different MSR indices, the corresponding multi-strategy indices and the Multi-Beta Multi-Strategy equal-weight indices over the last 10 years. We can observe that the multi-strategy indices either outperform the MSR strategies slightly in terms of risk/reward or provide a comparable performance in all regions. This improvement in performance can be further magnified if the multi-strategy scheme is combined with an explicit choice of factor exposure as in the Smart Beta 2.0 approach promoted by Scientific Beta, which is the case with the Multi-Beta Multi-Strategy Equal-Weight offering.

Exhibit 7: Performance Analysis – SciBeta Efficient Maximum Sharpe Ratio, Diversified Multi-Strategy, and Multi-Beta Multi-Strategy Indices in Different Geographical Regions

Analysis Period 31/12/2004 to 31/12/2014 (10 Years)	USA			UK			Eurozone			Japan			Developed Asia-Pacific ex-Japan
	Efficient MSR	Diversified Multi-Strategy	MBMS EW	Efficient MSR	Diversified Multi-Strategy	MBMS EW	Efficient MSR	Diversified Multi-Strategy	MBMS EW	Efficient MSR	Diversified Multi-Strategy	MBMS EW	Efficient MSR	Diversified Multi-Strategy	MBMS EW
Annual Returns	9.57%	9.57%	9.88%	10.79%	10.19%	10.02%	7.14%	6.87%	7.43%	5.97%	5.81%	6.17%	11.28%	11.13%	12.46%
Annual Volatility	19.43%	19.81%	19.30%	17.89%	17.91%	17.45%	17.43%	17.92%	17.31%	19.50%	20.01%	19.17%	20.42%	20.83%	20.07%
Sharpe Ratio	0.42	0.41	0.44	0.48	0.45	0.45	0.30	0.28	0.32	0.30	0.28	0.31	0.48	0.47	0.55
Maximum Drawdown	52.35%	52.73%	51.93%	41.26%	44.71%	45.47%	57.07%	57.70%	57.09%	51.39%	52.84%	49.26%	65.11%	64.85%	63.28%
Annual Relative Returns	1.67%	1.67%	1.97%	3.49%	2.89%	2.71%	1.72%	1.46%	2.01%	1.99%	1.83%	2.19%	1.62%	1.46%	2.79%
Tracking Error	2.82%	2.50%	3.17%	4.81%	4.68%	5.50%	5.47%	4.79%	5.56%	5.76%	5.23%	6.97%	6.29%	5.45%	6.23%
Information Ratio	0.59	0.67	0.62	0.73	0.62	0.49	0.32	0.30	0.36	0.35	0.35	0.31	0.26	0.27	0.45
95% Tracking Error	5.10%	4.53%	5.65%	8.97%	8.80%	10.16%	10.06%	8.75%	10.47%	11.62%	10.04%	13.83%	12.25%	10.59%	12.57%
Maximum Relative Drawdown	5.57%	5.77%	5.39%	10.02%	14.09%	17.66%	9.21%	8.81%	10.45%	10.10%	10.44%	12.24%	10.91%	9.12%	11.05%

This exhibit shows the return and risk performance of the SciBeta Efficient Maximum Sharpe Ratio, Diversified Multi-Strategy, and Multi-Beta Multi-Strategy Equal-Weight indices across different geographical regions: USA, UK, Eurozone, Japan and Developed Asia-Pacific ex-Japan. All statistics are annualised and daily total returns from 31 December 2004 to 31 December 2014 are used for the analysis. Returns are in USD/GBP/EUR/JPY/USD currencies for USA/UK/Eurozone/Japan/Developed Asia-Pacific ex-Japan respectively. The "Secondary Market US Treasury Bills (3M)" is the risk-free rate in US Dollars for USA and Developed Asia-Pacific ex-Japan. The "UK Treasury Bill Tender (3M)" is the risk-free rate in British Pounds for UK. "Euribor (3M)" is the risk-free rate in Euros for Eurozone, and "Japan Gensaki T-Bill (1M)" is the risk-free rate in Japanese Yen for Japan. The cap-weighted benchmark is the SciBeta CW index of the corresponding universe. Source: scientificbeta.com.

Thus, over the past 10-year period and for the same regions, we observe an average difference of 0.24%, with a maximum difference in the Developed Asia-Pacific ex-Japan region of 1.17%.

In the Developed World universe, we observe that the SciBeta Developed World Multi-Beta Multi-Strategy EW index outperforms the SciBeta Developed World Efficient MSR index by 0.25% annually.

Exhibit 8: Performance Analysis – SciBeta Efficient Maximum Sharpe Ratio, Diversified Multi-Strategy, and Multi-Beta Multi-Strategy Indices in the Developed World

Analysis Period 31/12/2004 to 31/12/2014 (10 Years)	Developed World
	Broad CW	Efficient MSR	Diversified Multi-Strategy	Multi-Beta Multi-Strategy EW
Annual Returns	6.73%	8.54%	8.42%	8.79%
Annual Volatility	17.04%	15.70%	16.04%	15.63%
Sharpe Ratio	0.31	0.45	0.44	0.47
Maximum Drawdown	57.13%	53.96%	54.79%	53.94%
Annual Relative Returns	-	1.81%	1.69%	2.06%
Tracking Error	-	2.33%	2.00%	2.59%
Information Ratio	-	0.78	0.85	0.80
95% Tracking Error	-	4.62%	3.90%	5.07%
Maximum Relative Drawdown	-	4.04%	4.07%	6.37%

This exhibit shows the return and risk performance of the SciBeta Efficient Maximum Sharpe Ratio, Diversified Multi-Strategy and Multi-Beta Multi-Strategy Equal-Weight indices in the Developed World universe. All statistics are annualised and daily total returns from 31 December 2004 to 31 December 2014 are used for the analysis. Returns are in USD. The "Secondary Market US Treasury Bills (3M)" is the risk-free rate. The cap-weighted benchmark is the SciBeta Developed World CW index. Source: scientificbeta.com.

Over longer periods, the differences are even more pronounced. Thus, for a period of 40 years, when referring to the long-term track records of the three methodologies applied to a universe of the top 500 US capitalisations, we observe that the US Multi-Beta Multi-Strategy EW LTTR outperforms the US Efficient MSR LTTR by 1.08% annually. In the end, the US Multi-Beta Multi-Strategy index, with an Information Ratio of 0.79 and a Sharpe Ratio of 0.71, outperforms the US Efficient MSR by 19.79% and 12.23% respectively.

Exhibit 9: Performance Analysis – SciBeta Efficient Maximum Sharpe Ratio, Diversified Multi-Strategy and Multi-Beta Multi-Strategy Indices with USA Long-Term Track Records

Analysis Period 31/12/1974 to 31/12/2014 (40 Years)	USA LTTR
	Broad CW	Efficient MSR	Diversified Multi-Strategy	Multi-Beta Multi-Strategy EW
Annual Returns	12.16%	15.03%	14.79%	16.11%
Annual Volatility	17.12%	15.76%	16.05%	15.58%
Sharpe Ratio	0.41	0.63	0.60	0.71
Maximum Drawdown	54.53%	53.22%	54.55%	53.86%
Annual Relative Returns	-	2.87%	2.64%	3.95%
Tracking Error	-	4.33%	4.07%	4.98%
Information Ratio	-	0.66	0.65	0.79
95% Tracking Error	-	7.26%	7.67%	8.95%
Maximum Relative Drawdown	-	30.66%	32.89%	33.65%

This exhibit shows the return and risk performance of the SciBeta Efficient Maximum Sharpe Ratio, Diversified Multi-Strategy and Multi-Beta Multi-Strategy Equal-Weight indices with USA Long-Term Track Records. All statistics are annualised and daily total returns from 31 December 1974 to 31 December 2014 are used for the analysis. Returns are in USD. The "Secondary Market US Treasury Bills (3M)" is the risk-free rate. The cap-weighted benchmark is based on the 500 largest market cap US stocks. Source: scientificbeta.com.

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Footnotes:

¹DeMiguel, V., L. Garlappi, J. Nogales and R. Uppal, 2009, "A Generalized Approach to Portfolio Optimization: Improving Performance By Constraining Portfolio Norms", Management Science 55.5, 798-812.
²Amenc, N., F. Goltz, L. Martellini and P. Retkowsky, 2011, "Efficient Indexation: An Alternative to Cap-Weighted Equity Indices", Journal of Investment Management.
³Gautam K., A. Lodh, October 2013, "Scientific Beta Efficient Maximum Sharpe Ratio Indices", ERI Scientific Beta publication.
⁴"Amenc, N., F.Goltz, A.Lodh and L. Martellini, 2012, "Diversifying the Diversifiers and Tracking the Tracking Error: Outperforming Cap-Weighted Indices with Limited Risk of Underperformance", Journal of Portfolio Management.

Julie Glynn, Director, Pension Investments, United Technologies Corporation

In this interview, Julie Glynn, Director, Pension Investments, at United Technologies Corporation, discusses the reasons behind United Technologies' recent decision to adopt a portfolio based on one of the ERI Scientific Beta multi-beta multi-strategy indices, the objectives of the portfolio and her views on the current and future use of smart beta indices by investors.

Why did United Technologies decide to create a portfolio based on the ERI Scientific Beta United States Multi-Beta Multi-Strategy ERC Index?

We were looking to replace an enhanced equity index portfolio. In late 2011, early 2012 we researched low volatility investing. We were attracted to the superior risk adjusted returns of low volatility strategies and the concept fit well with our de-risking frame work. We saw it as a way to de-risk within the growth portfolio. We did not end up implementing a low volatility portfolio. We struggled with the tactical aspect of timing the investment and also had concerns over the sustainability of the anomaly. We were also concerned with turnover and tracking error to our existing equity benchmark and had trouble identifying an appropriate replacement benchmark.

When we were introduced to the concept of smart beta in 2013, we recognized that traditional capitalization weighted indices are over concentrated and inefficient. We quickly learned that there are many approaches to smart beta investing. The appeal of the ERI Scientific Beta US Multi-Beta Multi-Strategy ERC Index is that it tilts towards a broad selection of well-rewarded equity factors. We saw this portfolio as an active strategy with passive strategy benefits such as simple, rules-based portfolio construction and complete transparency. We still believe in active investment management but view this portfolio as a cost effective way to achieve active manager-like performance in a passive manner.

Could you tell us a little about the objectives of the portfolio?

The objective of the portfolio is to provide broad US Equity exposure in a diversified, efficient and cost-effective manner while tilting towards historically well-rewarded equity factors. We expect this portfolio to outperform the S&P 500 Index by 200-400 bps over the long-term with slightly lower volatility.

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

The main advantages of the portfolio are that it is consistent, transparent, diversified and cost efficient. We feel it is a good blend of the benefits of both active and passive investing.

How do you see the future of smart beta investing?

We don’t see smart beta as a replacement for active management. We implemented it as a replacement for a passive portfolio. We do see it as a potential hurdle for active managers so you could see some active managers replaced with smart beta. Ideally we feel that smart beta portfolios combined with high conviction, consistent alpha producing active managers works best.

Other investors may use smart beta products as completion portfolios. They may analyze their existing investments and use individual factor smart beta portfolios to fill in perceived gaps.

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Julie Glynn is Director of Pension Investments at United Technologies Corporation. She joined United Technologies in 2007 and primarily focuses on global public equity, risk parity and portable alpha investments for the US defined benefit retirement plan. Prior to joining United Technologies, Julie worked in the investment management group at Aetna and was a Senior Quantitative Analyst with ING Investment Management. Julie holds the Chartered Financial Analyst designation and is a member of the Hartford CFA Society. She has a B.S. in Mathematics from Southern Connecticut State University.

Scientific Beta Smart Factor Indices

The following table displays the short-, mid- and long-term performance of diversified multi-strategies. The eight tilts selected (Book-to-Market, Dividend Yield, Size, Liquidity, Volatility, Momentum, Investment and Profitability) are the common tilts documented in the literature as liable to produce outperformance compared to cap-weighted indices. The table presents performance statistics for both high and low stock selections by factor tilt.

Short-Term, Mid-Term and Long-Term Relative Performance Overview of Smart Factor Indices
for the Scientific Beta Developed Equity Universe  

Diversified Multi-Strategy Index	Past Month (as of 30/04/2015)		Year-to-Date (as of 30/04/2015)		Past Year (as of 30/04/2015)		Past 5 Years (as of 30/04/2015)		Past 10 Years (as of 30/04/2015)		Long-Term US Track Records since 01/01/1975 (as of 31/12/2014): 40 years
	Relative Return Compared to Broad Cap-Weighted
High/Low Stock Selections by:	High	Low	High	Low	High	Low	High	Low	High	Low	High	Low
Book-to-Market	-0.12%	-0.66%	0.01%	1.21%	-0.68%	4.37%	0.52%	2.41%	1.27%	1.93%	4.54%	1.14%
Dividend Yield	-0.72%	-0.08%	-0.97%	2.66%	-0.33%	4.99%	1.30%	1.94%	1.62%	1.68%	3.62%	1.54%
Size	-0.26%	-0.28%	0.11%	1.38%	1.62%	2.99%	0.76%	2.58%	1.20%	2.18%	1.45%	4.59%
Liquidity	0.38%	-1.06%	0.66%	0.67%	1.03%	3.41%	0.61%	2.53%	1.46%	1.88%	1.57%	4.19%
Volatility	0.66%	-1.10%	2.42%	-0.60%	0.95%	2.74%	-0.13%	2.97%	0.80%	2.20%	2.62%	2.87%
Momentum	-0.94%	0.57%	0.70%	1.04%	2.56%	0.80%	2.15%	0.21%	1.62%	1.36%	3.49%	1.83%
Investment	-0.10%	-0.97%	1.63%	-0.34%	3.44%	1.11%	1.06%	2.11%	0.70%	2.66%	1.29%	3.89%
Profitability	-0.63%	0.05%	1.25%	-0.07%	5.05%	-1.25%	3.30%	-0.49%	3.15%	-0.16%	3.33%	1.92%

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 and 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.

Last month, the best performing index among smart factor indices was the SciBeta Developed High-Volatility Diversified Multi-Strategy index with a relative return of 0.66% compared to broad cap-weighted indices, followed by the SciBeta Developed Low-Momentum Diversified Multi-Strategy index with a relative return of 0.57%, the SciBeta Developed High-Liquidity Diversified Multi-Strategy index with a relative return of 0.38%, and the SciBeta Developed Low-Profitability Diversified Multi-Strategy index with a relative return of 0.05%. The remaining smart factor indices posted negative relative returns compared to broad cap-weighted indices, ranging from -1.10% for the SciBeta Developed Low-Volatility Diversified Multi-Strategy index to -0.08% for the SciBeta Developed Low-Dividend Yield Diversified Multi-Strategy index. The four strategies which posted positive relative returns in April (High Volatility, Low Momentum, High Liquidity and Low Profitability), are among the six strategies for which bull market conditions are more favourable than bear market conditions over the long term. The four strategies which posted the lowest returns last month (Low Volatility, Mid Liquidity, Low Investment and High Momentum), are among the six strategies for which bear market conditions are more favourable than bull market conditions over the long term. In April, particularly bullish market conditions were observed in all regions, with the exception of the United States. There was therefore a strong market effect that impacted the performances of the various smart factors in different ways.

Looking at relative returns over the past year, all strategies with the exception of Value, High Dividend Yield, and Low Profitability posted positive relative returns compared to broad cap-weighted indices. The best performing index among smart factor indices was the SciBeta Developed High-Profitability Diversified Multi-Strategy index with a relative return of 5.05%, while the SciBeta Developed Low-Profitability Diversified Multi-Strategy index posted the lowest relative return at -1.25%.

Over the past ten years, all strategies but one posted positive relative returns in relation to broad cap-weighted indices, with values ranging from -0.16% for the SciBeta Developed Low-Profitability Diversified Multi-Strategy index to 3.15% for the SciBeta Developed High-Profitability Diversified Multi-Strategy index.

Scientific Beta Multi Smart Factor Indices

The table below displays an overview of the relative and absolute performance of Scientific Beta Multi-Beta Multi-Strategy indices for various regions and different time periods.

Relative and Absolute Performance of Scientific Beta Multi-Beta Multi-Strategy Indices across Regions

Index	Multi- Beta Multi- Strategy	Nº of Consti- tuents	Relative Return				Information Ratio		Absolute Return		Volatility		Sharpe Ratio
			1 Month	YTD	1 Year	10 Years	1 Year	10 Years	1 Year	10 Years	1 Year	10 Years	1 Year	10 Years
Global Developed	EW	1907	-0.61%	0.37%	1.90%	1.86%	1.11	0.72	10.08%	9.45%	8.53%	15.64%	1.18	0.52
	ERC	1907	-0.57%	0.35%	1.42%	1.76%	0.86	0.75	9.60%	9.35%	8.61%	15.94%	1.11	0.50
SciBeta Global Developed CW		2000							8.18%	7.59%	9.12%	17.08%	0.89	0.37
Developed ex US	EW	1422	0.36%	0.37%	2.61%	1.99%	1.24	0.57	5.36%	8.55%	8.76%	16.63%	0.61	0.43
	ERC	1422	0.39%	0.43%	2.06%	1.89%	1.03	0.60	4.81%	8.45%	8.90%	17.04%	0.54	0.42
SciBeta Developed ex US CW		1500							2.75%	6.56%	9.55%	18.87%	0.29	0.28
United States	EW	485	-1.44%	0.27%	1.06%	1.76%	0.48	0.56	14.33%	10.31%	11.11%	19.29%	1.29	0.46
	ERC	485	-1.41%	0.20%	0.82%	1.76%	0.37	0.58	14.10%	10.31%	11.19%	19.25%	1.26	0.47
SciBeta United States CW		500							13.28%	8.55%	11.59%	20.28%	1.14	0.36
Dev. Europe ex UK	EW	378	1.29%	0.18%	0.69%	1.66%	0.19	0.36	-2.06%	8.42%	12.73%	21.91%	-0.16	0.32
	ERC	378	1.24%	0.16%	0.51%	1.60%	0.14	0.38	-2.24%	8.37%	12.79%	22.52%	-0.18	0.31
SciBeta Europe ex UK CW		400							-2.75%	6.76%	13.76%	24.39%	-0.20	0.22
United Kingdom	EW	98	-1.03%	0.35%	5.72%	2.88%	1.42	0.52	12.96%	10.94%	11.86%	17.52%	1.06	0.51
	ERC	98	-1.00%	0.23%	5.03%	2.75%	1.31	0.52	12.27%	10.81%	11.85%	17.61%	1.00	0.50
SciBeta United Kingdom CW		100							7.24%	8.07%	12.23%	19.35%	0.56	0.31
Dev. Asia Pacific ex. Japan	EW	380	2.75%	3.03%	5.60%	3.03%	1.45	0.49	8.79%	13.59%	8.51%	20.03%	1.03	0.61
	ERC	380	2.73%	2.85%	5.34%	3.04%	1.40	0.49	8.53%	13.59%	8.51%	20.00%	1.00	0.61
SciBeta Dev. Asia Pacific ex. Japan CW		400							3.19%	10.55%	10.52%	23.67%	0.30	0.39
Japan	EW	473	-1.44%	0.11%	1.23%	1.60%	0.32	0.23	40.98%	7.17%	13.96%	19.19%	2.93	0.36
	ERC	473	-1.31%	0.08%	1.09%	1.48%	0.30	0.22	40.84%	7.05%	14.07%	19.31%	2.90	0.36
SciBeta Japan CW		500							39.75%	5.57%	15.85%	22.86%	2.51	0.24

Based on daily total returns in USD for Global Developed, Developed ex US, US, and Asia Pacific ex Japan, and Developed Europe ex UK and in GBP for UK and JPY for Japan. As of 30/04/2015. Inception date is 21/06/2002 for Multi-Beta Multi-Strategy EW indices and 19/12/2003 for Multi-Beta Multi-Strategy ERC indices and CW indices. All statistics are annualised, except for the 1-Month 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 US universe is based on the top 500 stocks by free-float-adjusted market cap. The risk-free rates used are defined according to the regional universe of the index.

Last month, the SciBeta Developed Multi-Beta Multi-Strategy EW (Equal Weights) index and the SciBeta Developed Multi-Beta Multi-Strategy ERC (Equal Risk Contribution) index posted negative relative returns of -0.61% and -0.57%, respectively, compared to cap-weighted indices. This underperformance is due to the strong rise in the equity markets in a large number of regions which is favourable to cap-weighted indices. Overall, the Multi-Beta Multi-Strategy indices, which are very well diversified, cannot take advantage of extreme bull periods that are excessive compared to normal market conditions, since one of the characteristics of the Multi-Beta Multi-Strategy indices, as with all diversified indices, is that they are not concentrated in a tiny number of stocks that are representative of this "abnormal" appreciation.

Looking at the different regions, the relative returns ranged from -1.44% for both the SciBeta United States Multi-Beta Multi-Strategy EW index and the SciBeta Japan Multi-Beta Multi-Strategy EW index to 2.75% for the SciBeta Developed Asia Pacific ex Japan Multi-Beta Multi-Strategy EW index, compared to broad cap-weighted indices. In April, all four factors that make up the Multi-Beta Multi-Strategy indices – Value, Momentum, High Volatility and Mid Capitalisation – performed better than the regional broad cap-weighted indices in the Developed Europe ex UK and Developed Asia Pacific ex Japan regions, contributing positively to the relative performance of the corresponding Multi-Beta Multi-Strategy indices. In the Developed ex-USA region, relative performance was driven positively by the relative performance of the Value and Mid-Cap factors and driven negatively by the relative performance of the High Momentum and Low Volatility factors, leading globally to a positive relative return. In Japan, only the Value factor performed better than the broad regional cap-weighted indices and contributed positively to the relative performance of the Multi-Beta Multi-Strategy indices, while the relative performance of the remaining three factors – High Momentum, Low Volatility and Mid-Cap – contributed negatively, leading to a negative relative return. In the Global Developed region, in the USA and in the UK, the four factors performed more poorly than the regional broad cap-weighted indices, which explains the relative negative performance of the Multi-Beta Multi-Strategy indices in these regions.

Over the past year, all Multi-Beta Multi-Strategy indices delivered positive relative returns compared to broad cap-weighted indices, with relative returns varying from 0.51% for the SciBeta Developed Europe ex UK Multi-Beta Multi-Strategy ERC index to 5.72% for the SciBeta United Kingdom Multi-Beta Multi-Strategy EW index. During this period, the performance of all indices was essentially driven by the performance of the Low Volatility and High Momentum factors. In addition, the performance of the Mid-Cap factor also greatly contributed to the performance of the SciBeta United Kingdom Multi-Beta Multi-Strategy and SciBeta Developed Asia Pacific ex Japan Multi-Beta Multi-Strategy indices.

Over the past ten years, the SciBeta Developed Multi-Beta Multi-Strategy EW index and the SciBeta Developed Multi-Beta Multi-Strategy ERC index posted strong annual relative returns of 1.86% and 1.76% respectively, compared to cap-weighted indices. For the different regions, the highest performance over the past ten years was obtained by the SciBeta Developed Asia Pacific ex Japan Multi-Beta Multi-Strategy ERC index with a relative return of 3.04%, compared to cap-weighted indices, with the lowest performance posted by the SciBeta Japan Multi-Beta Multi-Strategy ERC index at 1.48%.

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 since January 1, 1975 (40 years), the EW and ERC benchmarks post respective relative returns compared to cap-weighted indices of 3.95% and 3.76%.

April performance report for the smart beta indices

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 Paper

The Dimensions of Quality Investing: High Profitability and Low Investment Smart Factor Indices
March 2015

Recently, two new rewarded risk factors, High Profitability and Low Investment, have been identified in the literature as not only providing high risk premia in the long run based on empirical evidence but also having simple and straightforward economic explanations for the existence of their premia, providing reassurance on the robustness and persistence of the factors. Several commercial index providers are marketing indices under the label "Quality Factor Indices" which supposedly seek the premium associated with these two factors. This paper discusses the literature and evidence found so far in support of these two factors. It also examines various arguments and explanations surrounding the reasons for expecting a premium out of the two factors. The paper also presents Scientific Beta’s smart factor approach to gaining exposure to High Profitability and Low Investment factors that provide a well-diversified way to seek the factor risk premia, briefly discussing Scientific Beta’s implementation methodology, the choice of proxy variables and the performance of the two factor indices. It also explores the possibility of combining the two smart factor indices to form a multi-factor index that gains exposure to both factors simultaneously. Finally, the paper reviews some of the "quality" indices marketed by competitors and their methodology, and performs a comparative study with Scientific Beta’s smart factor indices.

All White Papers

• Smart Beta 2.0
• Overview of Diversification Strategy Indices
• Scientific Beta Maximum Deconcentration Indices
• Scientific Beta Maximum Decorrelation Indices
• Scientific Beta Efficient Maximum Sharpe Ratio Indices
• Scientific Beta Efficient Minimum Volatility Indices
• Scientific Beta Diversified Risk Weighted Indices
• Scientific Beta Diversified Multi-Strategy Index
• Scientific Beta Global Developed Universe
• Scientific Beta Cap-Weighted Indices: Tools for Relative Performance and Risk Analysis of Scientific Beta’s Smart Beta Strategies
• Risk Managing Smart Beta Strategies
• Scientific Beta Multi-Beta Multi-Strategy Indices: Implementing Multi-Factor Equity Portfolios with Smart Factor Indices
• Scientific Beta Multi-Strategy Factor Indices: Combining Factor Tilts and Improved Diversification
• Investability of Scientific Beta Indices
• Robustness of Smart Beta Strategies
• Scientific Beta USA Long-Term Track Records
• Scientific Beta Analytics: Examining the Performance and Risks of Smart Beta Strategies
• The Dimensions of Quality Investing: High Profitability and Low Investment Smart Factor Indices

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Research Papers

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 approach of scientific rigour and research-based veracity, which it applies to all the services that it offers investors and asset managers. The following section presents selected research publications that have been produced recently by EDHEC-Risk Institute, together with reference papers that have been published in academic journals.

New Papers

Accounting for Geographic Exposure in Performance and Risk Reporting for Equity Portfolios
EDHEC-Risk Institute Publication, March 2015

This paper underlines the usefulness of analysing the performance and risks of portfolios, by taking into account their geographic equity exposure based on real economic activity and not only on their place of listing or, more generally, the nationality assigned to them in market indices. The study finds that, for a number of stocks, their official nationality does not match their real economic exposure as represented by the company’s distribution of sales. A dominant practice in the search for international diversification of equity portfolios is to classify stocks according to their place of listing, incorporation or headquarters. However, such a practice is questionable within the context of a globalised marketplace where a company's operations are typically not restricted to any single country. The study uses geographic segmentation sales data introduced by the new international accounting rules that preside over the reporting activities of listed companies within the context of assessing geographic equity risk exposure. This study clearly shows that, in developed market indices, the percentage of company sales generated outside the official region of the index is significant and has increased in recent years. For example, the cap-weighted non-US exposure of the S&P 500 and the non-European exposure of the STOXX Europe 600 between June 2004 and June 2013 increased from 30% to 39% and from 41% to 53%, respectively. This indicates, for the STOXX Europe 600 for example, that the index is predominantly non-European. These real economic exposures essentially influence variations in index performance.

The EDHEC European ETF Survey 2014
EDHEC-Risk Institute Publication, March 2015

EDHEC Risk Institute conducted its eighth survey of European investment professionals about the usage and perceptions of ETFs at the end of 2014. The aim of this study is to analyse the usage of exchange-traded funds (ETFs) in investment management and to give a detailed account of the current perceptions and practices of European investors in ETFs. Among the key findings of the 2014 survey: Among the biggest priorities seen by investors for future product development in the ETF space, four concern indices relating to smart beta approaches, namely smart beta equity (37%), equity factor (31%), equity style (29%), and smart beta bond (25%). 25% of respondents already use products tracking "smart beta" indices and more than an additional two-fifths of respondents (40%) are considering investing in such products in the near future. More than 80% of respondents think that smart beta indices allow factor risk premia, such as value and small cap, to be captured. This capturing of factor premia is a prime motivation for investment in smart beta ETFs for a vast majority of respondents.

Reference Papers

Choose Your Betas: Benchmarking Alternative Equity Index Strategies
Journal of Portfolio Management, Fall 2012

This article clarifies that methodological choices can be made independently for two steps in the construction of alternative equity index strategies: the constituent selection and choice of a diversification-based weighting scheme. By flexibly combining the different possible choices for these steps, the authors create a large variety of strategies and test their performance and risk results. The results suggest that diversification approaches may be a superior alternative, or at least a very important complement, to pure stock selection approaches when it comes to reaching a risk-return objective. Moreover, even though some argue that the risk and performance of diversification-based weighting schemes are solely driven by factor tilts, the authors show how straightforward it is to correct such tilts through the selection of stocks with appropriate characteristics while maintaining the improvement in achieving a risk–return objective that is due to the respective diversification approaches.

Towards Smart Equity Factor Indices: Harvesting Risk Premia without Taking Unrewarded Risks
Journal of Portfolio Management, Summer 2014

This article argues that current smart beta investment approaches only provide a partial answer to the main shortcomings of capitalisation-weighted indices, and develops a new approach to equity investing, which the authors refer to as smart factor investing. The authors then provide an assessment of the benefits of simultaneously addressing the two main problems of cap-weighted indices - their undesirable factor exposures and their heavy concentration - by constructing factor indices that explicitly seek exposures to rewarded risk factors, while diversifying away unrewarded risks. The results suggest that such smart factor indices lead to considerable improvements in risk-adjusted performance. For long-term US data, smart factor indices for a range of different factor tilts consistently outperform cap-weighted, factor-tilted indices. Compared with the broad cap-weighted index, smart factor indices roughly double the risk-adjusted return (Sharpe ratio). Outperformance of such indices persists at levels ranging from 2.92% to 4.46% annually, even when assuming unrealistically high transaction costs. Moreover, by providing explicit tilts to consensual factors, such indices improve upon many current smart beta offerings where, more often than not, factor tilts exist as unintended consequences of ad hoc methodologies.

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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
May 2015

The latest issue of the P&I EDHEC-Risk Institute Research for Institutional Money Management supplement includes research that examines different equity risk factors, specifically high profitability and low investment, looks at the fallacy that all smart beta strategies are the same, and presents the results of a survey on the use of alternative equity beta by investment professionals.

The Dimensions of Quality Investing: High Profitability and Low Investment Factors
This article examines the role of two separate equity risk factors related to balance sheet characteristics: Low Investment and High Profitability. These factors rely on straightforward and parsimonious indicators, and can be expected to provide more robust performance benefits than ad-hoc stock picking indicators of "quality" used in the industry.

Identifying Equity Factors with a Genuine Economic Rationale
Here, we explore the economic rationale behind the various "factors" in the equity space. Rather than accepting new factors based on back-tested performance improvements, investors may be better advised to assess the theoretical groundings behind a factor. Having a convincing explanation should be a key requirement for investors when they decide to gain exposure to a given factor, as a theoretical justification of an observed effect provides some safeguard against data-mining.

Smart Beta Performance is not "Monkey Business"
In this article, we examine the claim that that all smart beta strategies generate positive value and small-cap exposure similar to that generated by any random portfolio strategy, and that the inverse of such strategies perform similarly or better. Our results are not supportive of the "monkey portfolio" argument (derived from Burton Malkiel’s reference to how a monkey would perform when randomly selecting stocks for a portfolio).

Investment Professionals' Views on Alternative Equity Beta Strategies
Alternative equity beta investing has clearly been the focus of increasing attention in the industry recently. Though products in this segment currently represent only a fraction of overall assets, there has been tremendous growth recently in terms of both assets under management and new product development. In this context, EDHEC-Risk recently carried out a survey among a representative sample of investment professionals to identify their views and uses of alternative equity beta.

Global X Launches Family of ETFs Based on Scientific Beta Indices

Global X Funds, the New York-based provider of exchange-traded funds (ETFs) offering access to investment opportunities across global markets, has launched a family of four ETFs based on Scientific Beta indices:

• The Global X Scientific Beta US ETF (SCIU)
• The Global X Scientific Beta Europe ETF (SCID)
• The Global X Scientific Beta Japan ETF (SCIJ)
• The Global X Scientific Beta Asia ex-Japan ETF (SCIX)

These ETFs, which track multi-factor indices developed by ERI Scientific Beta, provide core equity exposure to the US, Europe, Asia ex-Japan, and Japan. Representing the next generation of smart beta indexing, the ETFs capture four factors simultaneously: Value, Size, Momentum and Low Volatility. While each individual factor has historically outperformed the market in the long term, the Scientific Beta strategy seeks to smooth the cyclicality of their returns and deliver more consistent outperformance by combining the factors together. Furthermore, by combining five unique weighting schemes, the ETFs seek to maximise diversification and minimise inadvertent factor tilts.

ERI Scientific Beta Receives "Best Index Provider Website" 2014 Award at the Second Annual ETF.com Awards Ceremony Held in New York City on 19 March, 2015

ERI Scientific Beta is pleased to announce that it received the "Best Index Provider Website" award at the second annual ETF.com awards ceremony held in New York City on 19 March, 2015 for being "The only index website to provide do-it-yourself index-building tools, for free". The award recognises the most informative and user-friendly website by an index provider. ERI Scientific Beta had been nominated alongside fellow finalists Morningstar, MSCI, Russell and S&P Dow Jones. The annual awards ceremony, which took place at Chelsea Piers, recognises the people, products and companies that have been instrumental in moving the 22-year-old ETF industry forward and that have helped create better options and outcomes for investors. Details of the selection of ERI Scientific Beta for this award are available in an extract from the ETF.com April 2015 ETF Report.

 

ERI Scientific Beta Announces New Organisation of its Asia-Pacific Activities

ERI Scientific Beta has announced a new organisation for its Asia-Pacific activities. Frédéric Ducoulombier becomes Business Development Director Asia ex-Japan and the Middle East. Frédéric Ducoulombier is a member of the ERI Scientific Beta Executive Committee and Corporate Director of ERI Scientific Beta, the head office of which is located in Singapore. Paul Hoff remains Business Development Director for Japan and is responsible for ERI Scientific Beta's Tokyo office.

Frédéric Ducoulombier Speaking on Smart Beta at the FT Investment Management Summit Asia on 18 June, 2015

Frédéric Ducoulombier, Business Development Director Asia ex-Japan and the Middle East at ERI Scientific Beta, has been invited to speak at the FT Investment Management Summit Asia held in Hong Kong on 18 June, 2015. The summit brings together senior investors from across the region and internationally for informed and stimulating debate on investment strategies that can most effectively protect and grow the expanding capital base of Asia’s pension funds, sovereign wealth funds, life insurance companies, endowments and other large institutions. Frédéric Ducoulombier will be participating as a panellist in the session entitled "Beating the Market – Passive, Active or Rules Based Strategies?" moderated by Kylie Wong, Managing Editor of Ignites Asia. High costs and the desire for greater yield are driving growing numbers of investors to reassess their allocations between passive and active strategies, with strategic beta approaches blurring the lines between the two. The session will examine the following questions: In this new environment what does the future hold for active funds? Are some markets more suited to passive investing than others? Is now the right time for Asia-based investors to tap the smart beta market? Can they achieve sufficient diversification of their smart beta investments? Why are some institutions still skeptical about this strategy?

"The Dimensions of Quality Investing" Seminar

The Dimensions of Quality Investing seminar will introduce the high profitability and low investment factors and provide empirical evidence that justifies their usage, address how to combine low investment and high profitability factor tilts, and present performance comparisons with industry offerings:

• Is there a premium to high profitability and low investment stocks? Empirical evidence
• Why should the investment and profitability premia persist? Economic rationale
• Making the right stock selection approach
• Reducing the tracking error
• Diversification benefits of combining the two factor indices
• Strategies used by competitors mixing a systematic approach to stock picking (alpha) with criteria used to define beta
• An approach which leads to better diversified portfolios and better performance compared to other commercially-available indices

The dates and locations of the European seminar series may be found below:

• Stockholm - 11 June, 2015
• Helsinki - 17 June, 2015
• Copenhagen - 18 June, 2015
• Munich - 30 June, 2015
• Rome - 1 July 2015

Admission to the seminar is complimentary and by invitation only. To request an invitation, please contact Amandine Badel at amandine.badel@scientificbeta.com or on +33 493 183 484.

Global X Scientific Beta ETFs – Evening Launch Event on the NYSE Trading Floor

Global X Funds are organising a reception on the historic floor of the New York Stock Exchange on 10 June, 2015 from 6.00pm to 9.00pm to celebrate the launch of their new ETF range based on Scientific Beta indices:

• The Global X Scientific Beta US ETF (SCIU)
• The Global X Scientific Beta Europe ETF (SCID)
• The Global X Scientific Beta Japan ETF (SCIJ)
• The Global X Scientific Beta Asia ex-Japan ETF (SCIX)

Following an opening presentation by Bruno del Ama, CEO of Global X Funds, Noël Amenc, CEO of ERI Scientific Beta, and Director of EDHEC-Risk Institute, will be guest speaker at the event.

To register for this event, please contact events@globalxfunds.com, specifying your full name and company affiliation.

Global X launches family of Scientific Beta ETFs

ETF Express (14/05/2015)

"(...) Global X Funds, a New York-based provider of exchange-traded funds (ETFs), has launched a family of four ETFs based on EDHEC-Risk Institute's Scientific Beta indexes. (...) The Scientific Beta ETFs provide core equity exposure to the US, Europe, Asia ex-Japan and Japan. The new funds may be considered alternatives to actively managed funds as they seek to outperform market cap-weighted indices at a fraction of the fees typically charged for active management. (...)"
Copyright GFM Limited

Why multi-factor funds are smarter beta

Financial Times (13/05/2015)

"(...) Global X is launching a fund that combines four different factors in an index designed by the EDHEC-Risk Institute. The “Scientific Beta” indices combine four factors — value, size (small stocks do well), low volatility and momentum. It takes an underlying index, and constructs four different indices from it. (...)"
Copyright Financial Times

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Under The Hood With Multifactor ETFs

ETF.com (08/05/2015)

"(...) Eric Shirbini, global product specialist with EDHEC Risk Institute, told us these ETFs are designed to deliver better risk/return than a traditional allocation to equities in the long run. We talked to him about what makes these multifactor approaches so innovative, and why investors should care. (...)"
Copyright ETF.com

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ERI Scientific Beta adds High Profitability and Low Investment smart factor "quality" indices to range

Institutional Asset Manager (21/04/2015)

"(...) ERI Scientific Beta has launched two new families of smart "quality-type" factor indices – High Profitability and Low Investment, allowing investors to benefit from well-documented additional risk premia. These new smart factor indices have been available on the www.scientificbeta.com platform since 20 March, 2015. The role of these separate factors relating to firm characteristics has been documented in recent empirical studies. (...)"
Copyright GFM Limited

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ERI Scientific Beta launches two indices

Investment Europe (21/04/2015)

"(...) By proposing not one but two smart factor quality indices, ERI Scientific Beta is allowing investors to gain exposure to two very different, and therefore highly decorrelated, factors that represent two dimensions of the quality approach. "This dissociation is in contrast with grey indices constructed through multi-criteria approaches that are not consistent with academic research in the area of quality. "The performance of factor-tilted indices is improved by the use of the diversified multi-strategy scheme offered by Scientific Beta," the company said. (...)"
Copyright Open Door Media Publishing Limited

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ERI Scientific Beta adds to range of quality factors

Global Investor (21/04/2015)

"(...) ERI Scientific Beta has added two families of quality-type factors to its range of smart beta indices which target additional risk premia, the high profitability and low investment families. The firm says it aims to give investors access to a more systematised and robust way of approaching qualitative stock picking than the ad-hoc indicators of quality factors currently used in the industry. Over 40 years to 2014, these smart factors have outperformed their corresponding cap-weighted indices by an average annual rate of 3.61% for the US market. (...)"
Copyright Euromoney Institutional Investor PLC

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SciBeta Developed Low Liquidity Diversified Multi-Strategy is top performing ERI Scientific Beta smart beta index in March

HedgeWeek (16/04/2015)

"(...) The SciBeta Developed Low Liquidity Diversified Multi-Strategy index, with a relative return of 1.10 per cent compared to the broad cap-weighted index, was the top performing ERI Scientific Beta smart beta index in March. The SciBeta Developed High Liquidity Diversified Multi-Strategy index posted the lowest relative return (0.03 per cent). Performance for smart factor indices exposed to risk factors known to be well rewarded over long periods remains strong, with annual performance in excess of broad cap-weighted indices ranging from 1.03 per cent to 3.11 per cent since inception for the Developed universe. (...)"
Copyright GFM Limited

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ERI Scientific Beta re-organises Asia-Pacific activities

Institutional Asset Manager (02/04/2015)

"(...) Frédéric Ducoulombier becomes Business Development Director Asia ex-Japan and the Middle East. Ducoulombier is a member of the ERI Scientific Beta Executive Committee and Corporate Director of ERI Scientific Beta, the head office of which is located in Singapore. Paul Hoff remains Business Development Director for Japan and is responsible for ERI Scientific Beta’s Tokyo office. Ducoulombier is directly responsible for compliance, governance and transparency at ERI Scientific Beta. (...)"
Copyright GFM Limited

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Daily ETF Watch: Global X To Do Smart Beta

ETF.com (23/03/2015)

"(...) Global X has filed for five ETFs tied to indexes created by EDHEC Risk Institute's Asia arm. The underlying "Scientific Beta" indexes take a multifactor approach and also use multiple weighting methodologies. These will not be the first ETFs in the U.S. to track EDHEC's Scientific Beta indexes, however. Earlier this year, ETF Securities launched the ETFS Diversified-Factor U.S. Large Cap ETF (SBUS) and the ETFS Diversified-Factor Developed Europe ETF (SBEU). (...)"
Copyright ETF.com

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.
  
  
• 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.

Part of EDHEC Business School, a not-for-profit organisation, EDHEC-Risk Institute has sought to provide the ERI Scientific Beta services in the best possible economic conditions.

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). The Scientific Beta platform offers 2,767 smart beta indices.
  
  
• Scientific Beta Analytics: Scientific Beta Analytics are detailed analytics and exhaustive information on 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. 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 service and EDHEC-Risk Smart Allocation Solutions: Scientific Beta Fully Customised Benchmarks is a service proposed by ERI Scientific Beta, and its partners, within 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. In 2015, ERI Scientific Beta established an offering based on EDHEC-Risk Institute’s applied research expertise in the field of risk management. This offering, referred to as “EDHEC-Risk Smart Allocation Solutions”, enables tailored solutions for multi smart beta allocation to be defined for institutional investors and asset managers, allowing specific objectives with regard to relative or absolute risks in an asset management only or an asset-liability management dimension to be taken into account.

With a concern to provide worldwide client servicing, ERI Scientific Beta is present in Boston, London, Paris, Nice, Singapore and Tokyo.

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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