Smart Beta Performance Report by Scientific Beta
Recent years have seen the development of numerous smart beta indices whose weighting schemes differ from those of cap-weighted indices. Smart beta indices may be obtained by tilting economic factors, such as book-to-market, size or volatility, or by introducing greater diversification into the index, as illustrated in multi-strategy indices. The positive performance of smart beta indices over the long term has been largely documented in the literature. However, these indices are exposed to risk factors that are different from those of cap-weighted indices and that may cause variations in performance over short periods. As a result, the presentation of long-term performance is not enough for investors, who are also demanding performance figures over recent and shorter periods. The present report gives a complete picture of smart beta index performance with both long-term and short-term figures that illustrate the variations in performance over the different time periods, as well as the variations in performance between the various strategies. As a result, combining the various smart beta strategies makes it possible to obtain more robust performance.
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% to 3.11% since inception for the Developed universe. Over shorter periods, the strategies are exposed to fluctuations depending on variations in market conditions. For investors willing to take tactical bets, ERI Scientific Beta offers sixteen smart factor indices.
This month, the best performing index among those smart factor indices is the SciBeta Developed Low Liquidity Diversified Multi-Strategy index, with a relative return of 1.10% compared to the broad cap-weighted index, while the SciBeta Developed High Liquidity Diversified Multi-Strategy index posts the lowest relative return (0.03%).
This Tiger Cub Giant Is Betting On Banks And Tech Stocks In The Recovery
The first two months of the third quarter were the best months for D1 Capital Partners' public portfolio since inception, that's according to a copy of the firm's August update, which ValueWalk has been able to review. Q2 2020 hedge fund letters, conferences and more According to the update, D1's public portfolio returned 20.1% gross Read More
Scientific Beta Multi-Beta Multi-Strategy (MBMS) indices associate an effective choice of weighting scheme, in terms of diversification, with an allocation to well-rewarded smart factors, to prevent indices from being too concentrated in one factor and to reduce their specific risks. Over the past ten years, the SciBeta Developed Multi-Beta Multi-Strategy EW (Equal Weights) index and the SciBeta Developed Multi-Beta Multi-Strategy ERC (Equal Risk Contribution) index post strong annual relative returns of 1.93% and 1.82%, respectively, compared to cap-weighted indices. This month, the SciBeta Developed Multi-Beta Multi-Strategy EW index and the SciBeta Developed Multi-Beta Multi-Strategy ERC index post relative returns of 0.62% and 0.58%, respectively, compared to cap-weighted indices. Looking at MBMS indices for various regions, we note that this month the best performing indices are the SciBeta Developed United States Multi-Beta Multi-Strategy indices, with a relative return of 1.22% for the EW scheme and 1.19% for the ERC scheme compared to the broad cap-weighted index, while the worst performing indices are the SciBeta Developed Europe ex-UK Multi-Strategy indices, with a relative return of
-1.07% for both the EW and ERC schemes compared to the broad cap-weighted index.
This month, all four factors that make up the MBMS indices, namely value, momentum, high volatility and mid capitalization, performed better than the regional broad cap-weighted indices in the United-States and Developed regions, and contributed positively to the relative performance of the corresponding MBMS indices. In the Developed Asia Pacific ex-Japan and the Developed ex-USA regions, relative MBMS performance was positively driven by the relative performance of the high momentum and mid-cap factors and negatively driven by the relative performance of the value and low volatility factors. In the UK and Japan, three factors out of four – value, momentum and mid-cap in the UK; momentum, low volatility and mid-cap in Japan – performed better than the broad regional cap-weighted indices and contributed positively to the relative MBMS performance of the MBMS indices, while the relative performance of the fourth factor – high volatility in the UK and value in Japan – contributed negatively. Not surprisingly, the developed Europe ex-UK region, where we observed the poorest relative performance this month compared to the broad regional cap-weighted index for the MBMS indices, is also the only one for which we observe negative relative performance for three factors out of four, namely momentum, low volatility and mid-cap.
Performance Overview for Smart Factor Indices for the Scientific Beta Developed Equity Universe and Long-Term US Data Series
Tables 1a and 1b display the performance of SciBeta Developed Diversified Multi-Strategy indices. 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 tables present performance statistics for both high and low stock selections by factor tilt. All these indices serve to create a diversified portfolio of the relevant stocks. In particular, they draw on different smart beta weighting schemes, which we refer to as a diversified multi-strategy index. In addition, these indices offer investable proxies for smart beta factor indices. These indices allow investors to be both exposed to a specific risk factor (beta) and to have good diversification of other risk factors, leading to an attractive Sharpe ratio associated with the factor tilt. Table 1c displays the performance of long-term US data series based on the same factor selection and weighting scheme, the initial reference universe of these long-term US data series being the 500 largest market-cap US stocks.
Table 1a: Short-Term Performance Overview for Smart Factor Indices for the Scientific Beta Developed Equity Universe
|Diversified Multi-strategy Index for||Past month (as of 31/03/2015)||Year-to-date (as of 31/03/2015)|
|Absolute Return||Relative Return compared to tilted cap-weighted||Relative Return compared to broad cap-weighted||Absolute Return||Relative Return compared to tilted cap-weighted||Relative Return compared to broad cap-weighted|
|High/low stock selections by||High||Low||High||Low||High||Low||High||Low||High||Low||High||Low|
The history of Scientific Beta Index returns begins on 21/06/2002. The statistics are based on daily total returns (with dividend 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.
Table 1b: Long-Term Performance Overview for Smart Factor Indices for the Scientific Beta Developed Equity Universe
|Diversified Multi-strategy Index for||Since Inception: From 21/06/2002 to 31/03/2015|
|Absolute Return||Relative Return compared to tilted cap-weighted||Relative Return compared to broad cap-weighted||Volatility||Sharpe Ratio||Maximum Relative Drawdown||Outperformance Probability (1Y)*||Outperformance Probability (3Y)*|
|High/low stock selections by||High||Low||High||Low||High||Low||High||Low||High||Low||High||Low||High||Low||High||Low|
* Outperformance Probability 1Y and 3Y are computed over the latest 10-year period.
The history of Scientific Beta Index returns begins on 21/06/2002. The statistics are based on daily total returns (with dividend 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. The tilted cap-weighted indices are obtained based on the same selection of assets as each of the smart factor indices. All results are in USD.
Table 1c: Performance Overview for Long-Term US Data Series
|Diversified Multi-strategy Index for||Long-Term US Track Records since 01/01/1974 (as of 31/12/2013): 40 years|
|Relative Return compared to cap-weighted||Volatility||Sharpe Ratio|
|High/low stock selections by||High||Low||High||Low||High||Low|
Long-Term US data series are style factor data series constructed from the 500 largest market cap US stocks. The statistics are based on daily total returns (with dividend 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. The yield on Secondary Market US Treasury Bills (3M) is used as a proxy for the risk-free rate in US Dollars. All results are in USD.
Over the long term all diversified multi-strategy indices exhibit a positive relative return compared to cap-weighted indices, whether broad or tilted cap-weighted indices. The best performance relative to the broad cap-weighted index is posted by the SciBeta Developed High Profitability Diversified Multi-Strategy index (3.11%), closely followed by the SciBeta Developed Low Investment Diversified Multi-Strategy index (3.08%). Tilted cap-weighted indices are factor indices that use the same universe of assets as each smart factor index. The outperformance of smart factor indices compared to those indices is due to the difference in weighting scheme, which results in better diversification for smart factor indices compared to cap-weighted indices. We observe that the smart beta indices exhibiting a higher gain in performance for the high stock selection compared to tilted cap-weighted indices than the gain in performance compared to broad cap-weighted indices (size, liquidity, volatility and investment), exhibit the reverse relationship for the corresponding low stock selection. We have the opposite relationship for three smart beta indices out of four exhibiting a lower gain in performance for the high stock selection compared to tilted cap-weighted indices than the gain in performance compared to broad cap-weighted indices (dividend yield, momentum and profitability).
Looking at year-to-date relative returns, we observe that all strategies except two (High Dividend Yield and Low Profitability) post positive returns relative to broad cap-weighted. The best performing index among smart factor indices is the SciBeta Developed Low Dividend Yield Diversified Multi-Strategy index with a relative return of 2.68%. In addition, the spread in relative return between the two smart factor indices, respectively resulting from high and low stock selections, greatly differs over time and between the strategies. Over the first quarter of 2015, the highest spread (2.91%) is observed for the dividend yield strategy, with a relative return of -0.23% for the SciBeta Developed High Dividend Yield Diversified Multi-Strategy index, compared to 2.68% for the SciBeta Developed Low Dividend Yield Diversified Multi-Strategy index and the lowest spread (1.04%) is observed for the investment strategy, with a relative return of 1.69% for the SciBeta Developed High Investment Diversified Multi-Strategy index, compared to 0.65% for the SciBeta Developed Low Investment Diversified Multi-Strategy index. We also note that there is no systematic correlation between the size of the spread and the similarity of high and low index declinations’ market betas.
Performance Overview of Scientific Beta Multi-Beta Multi-Strategy Indices and Long-Term US Data Series
Scientific Beta Multi-Beta Multi-Strategy (MBMS) indices are a combination of smart factor indices. Multi-Beta Multi-Strategy indices provide allocations to several well-documented risk premia in equity markets (Value, Momentum, Size and Low Volatility) which follow different cycles, corresponding to well-rewarded factors as documented in the literature. Combining factor tilts in a multi-beta benchmark allows risk-adjusted performance to be improved and outperformance across market regimes to be smoothed, compared to the average result of component indices. Investors can choose from two allocation methods: Equal Weights (EW), which targets an improvement in Sharpe ratio, or Equal Risk Contribution (ERC), which provides a pronounced decrease in relative risk and a higher information ratio.
Table 2 displays an overview of the relative and absolute performances of Scientific Beta Multi-Beta Multi Strategy indices for various regions and different time periods. Over the long term, all MBMS indices post positive excess return compared to broad cap-weighted indices. If we consider the ERC allocation, the annualised excess return over the past ten years ranges from 1.52%, for the SciBeta Developed Europe ex-UK Multi-Beta Multi-Strategy ERC index, to 2.87%, for the SciBeta United Kingdom Multi-Beta Multi-Strategy ERC index.
Over the past year, all MBMS indices, except the Developed Europe ex-UK indices, post positive excess returns compared to broad cap-weighted indices. If we consider the ERC allocation, the annualised excess return over the past year ranges from -1.32%, for the SciBeta Developed Europe ex-UK Multi-Beta Multi-Strategy ERC index, to 4.67%, for the SciBeta United Kingdom Multi-Beta Multi-Strategy ERC index. During this period, the performance of all indices was essentially driven by the performance of the low volatility factor. In addition, the performance of the high momentum factor also greatly contributes to the performance of the SciBeta United Kingdom Multi-Beta Multi-Strategy and SciBeta Japan Multi-Beta Multi-Strategy indices. Over the past year, the relative performance of the SciBeta Developed Europe ex-UK Multi-Beta Multi-Strategy indices was negatively driven by the relative performance of the value and mid-cap factors, which underperformed the regional broad cap-weighted index.
Year-to-date, most of the MBMS indices delivered positive returns compared to broad cap-weighted indices. The SciBeta Developed Europe ex-UK Multi-Beta Multi-Strategy indices are the only ones to post negative relative returns for both the EW and ERC contributions, while the SciBeta Developed ex-US Multi-Beta Multi-Strategy EW index and the SciBeta Developed Asia Pacific ex-Japan ERC index post a slightly negative relative return (-0.01% and -0.0%, respectively). If we consider the ERC allocation, the best performance is obtained by the SciBeta United States Multi-Beta Multi-Strategy ERC index with a relative return of 1.63%, while the worst performance is obtained by the SciBeta Developed Europe ex-UK Multi-Beta Multi-Strategy ERC index, with a relative return of -1.09%.
From table 2, it also appears that the volatility of the MBMS indices is significantly lower over the long term compared to the volatility of broad-cap weighted indices for all regions, with the highest differences being observed for the Developed Asia Pacific ex-Japan and Japan indices, with volatilities of 23.69% and 22.87%, respectively, for the broad cap-weighted indices, and volatilities of 20.02% and 19.34% respectively for the MBMS (ERC allocation) indices over the past ten years. As a result, we observe a considerable improvement in the Sharpe ratios for the MBMS indices over the past ten years compared to the broad cap-weighted indices. Sharpe ratios range from 0.28 (Developed Europe ex-UK), to 0.55 (Developed Asia Pacific ex-Japan) for the MBMS (ERC allocation) indices, compared to 0.19 (Developed Europe ex-UK) to 0.36 (Developed Asia Pacific ex-Japan) for the broad cap-weighted indices.
Table 2a: Relative and Absolute Performance of Scientific Beta Multi-Beta Multi Strategy Indices across Regions as of 31/03/2015
|Index||Multi-Beta Multi-Strategy||Nº of Constituents||Relative Return compared to cap-weighted||Information Ratio||Absolute Return||Volatility||Sharpe Ratio|
|SciBeta Global Developed CW||2000||6.53%||7.09%||9.25%||17.09%||0.70||0.33|
|Developed ex US||EW||1422||-0.12%||-0.01%||1.77%||1.99%||0.82||0.57||1.37%||7.82%||8.75%||16.65%||0.15||0.39|
|SciBeta Developed ex US CW||1500||-0.40%||5.83%||9.51%||18.89%||-0.04||0.24|
|SciBeta United States CW||500||13.02%||8.26%||11.94%||20.31%||1.09||0.34|
|Developed Europe ex UK||EW||378||-1.07%||-1.13%||-1.23%||1.57%||-0.33||0.34||-5.72%||7.67%||12.77%||21.91%||-0.45||0.29|
|SciBeta Europe ex UK CW||400||-4.49%||6.09%||13.71%||24.40%||-0.33||0.19|
|SciBeta United Kingdom CW||100||6.98%||7.49%||12.09%||19.34%||0.55||0.28|
|Dev. Asia Pacific ex Jp||EW||380||0.36%||0.14%||2.38%||2.61%||0.63||0.42||1.87%||12.42%||8.16%||20.05%||0.23||0.55|
|SciBeta Dev. Asia Pacific ex JP. CW||400||-0.51%||9.81%||10.16%||23.69%||-0.05||0.36|
|SciBeta Japan CW||500||30.94%||4.76%||16.12%||22.87%||1.92||0.20|
Based on daily total returns in USD for Global Developed, Developed ex-US, US, and Asia Pacific ex-Japan, and Dev. Europe ex-UK and in GBP for UK and JPY for Japan. 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 and 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.
Table 2b: Performance Overview for US Long-Term Data Series (40 years)
|Long-Term US Track Records since 01/01/1974 (as of 31/12/2013): 40 years|
|Relative Return compared to cap-weighted||Volatility||Sharpe Ratio|
Long-Term US data series are style factor data series constructed from the 500 largest market-cap US stocks. The statistics are based on daily total returns (with dividend 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. The yield on Secondary Market US Treasury Bills (3M) is used as a proxy for the risk-free rate in US Dollars. All results are in USD.
About 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: The Scientific Beta Fully-Customised Benchmarks service enables investors and asset managers to benefit from its expertise and the ability to determine and implement their choice of stocks, weighting schemes, and absolute and relative risk constraints in keeping with their objectives.
With a concern to provide worldwide client servicing, ERI Scientific Beta is present in Boston, London, Nice, Singapore and Tokyo.
Copyright © 2015 ERI Scientific Beta. All rights reserved. Scientific Beta is a registered trademark licensed to EDHEC Risk Institute Asia Ltd (“ERIA”). All information provided by ERIA is impersonal and not tailored to the needs of any person, entity or group of persons. Past performance of an index is not a guarantee of future results.
This material, and all the information contained in it (the “information”), have been prepared by ERIA solely for informational purposes, are not a recommendation to participate in any particular trading strategy and should not be considered as an investment advice or an offer to sell or buy securities. The information shall not be used for any unlawful or unauthorised purposes. The information is provided on an “as is” basis. Although ERIA shall obtain information from sources which ERIA considers reliable, neither ERIA nor its information providers involved in, or related to, compiling, computing or creating the information (collectively, the “ERIA Parties”) guarantees the accuracy and/or the completeness of any of this information. None of the ERIA Parties makes any representation or warranty, express or implied, as to the results to be obtained by any person or entity from any use of this information, and the user of this information assumes the entire risk of any use made of this information. None of the ERIA Parties makes any express or implied warranties, and the ERIA Parties hereby expressly disclaim all implied warranties (including, without limitation, any implied warranties of accuracy, completeness, timeliness, sequence, currentness, merchantability, quality or fitness for a particular purpose) with respect to any of this information. Without limiting any of the foregoing, in no event shall any of the ERIA Parties have any liability for any direct, indirect, special, punitive, consequential or any other damages (including lost profits) even if notified of the possibility of such damages. All Scientific Beta indices and data are the exclusive property of ERIA.
Information containing any historical information, data or analysis should not be taken as an indication or guarantee of any future performance, analysis, forecast or prediction. Past performance does not guarantee future results. In many cases, hypothetical, back-tested results were achieved by means of the retroactive application of a simulation model and, as such, the corresponding results have inherent limitations. The index returns shown do not represent the results of actual trading of investable assets/securities. ERIA maintains the index and calculates the index levels and performance shown or discussed, but does not manage actual assets. Index returns do not reflect payment of any sales charges or fees an investor may pay to purchase the securities underlying the index or investment funds that are intended to track the performance of the index. The imposition of these fees and charges would cause actual and back-tested performance of the securities/fund to be lower than the index performance shown. Back-tested performance may not reflect the impact that any material market or economic factors might have had on the advisor’s management of actual client assets. The information may be used to create works such as charts and reports. Limited extracts of information and/or data derived from the information may be distributed or redistributed provided this is done infrequently in a non-systematic manner.
The information may be used within the framework of investment activities provided that it is not done in connection with the marketing or promotion of any financial instrument or investment product that makes any explicit reference to the trademarks licensed to ERIA (ERI SCIENTIFIC BETA, SCIENTIFIC BETA, SCIBETA, EDHEC RISK and any other trademarks licensed to ERIA) and that is based on, or seeks to match, the performance of the whole, or any part, of a Scientific Beta index. Such use requires that the Subscriber first enters into a separate license agreement with ERIA. The information may not be used to verify or correct other data or information from other sources.
 Maximum Deconcentration, Diversified Risk Weighted, Maximum Decorrelation, Efficient Maximum Sharpe Ratio and Efficient Minimum Volatility.
 Beta values are available on www.scientificbeta.com.