Investabilityof Smart Beta Indices

Transcription

1 Investabilityof Smart Beta Indices Felix Goltz, PhD Research Director, ERI Scientific Beta Eric Shirbini, PhD Global Product Specialist, ERI Scientific Beta EDHEC-Risk Days Europe March 2015 The Brewery, London 1

2 Outline Introduction Scientific Beta Global Developed Equity Universe Turnover Control Capacity Constraints Scientific Beta Single-Factor and Multi-Factor Indices Scientific Beta Highly Liquid Indices Investability of Scientific Beta Global Multi-Factor Indices Conclusions 2

3 Outline Introduction Scientific Beta Global Developed Equity Universe Turnover Control Capacity Constraints Scientific Beta Single-Factor and Multi-Factor Indices Scientific Beta Highly Liquid Indices Investability of Scientific Beta Global Multi-Factor Indices Conclusions 3

4 Introduction Importance of Investability for Smart Beta Indices In a 2014 survey, EDHEC Risk Institute found that one of the biggest hurdle for investment professionals to invest in smart beta strategies are issues related to the investability of these strategies. Thestrengthofeachhurdlewasratedfrom1(weakhurdle)to5(stronghurdle). Source: EDHEC Risk Alternative Equity Beta Survey. The survey was conducted as part of the Newedge "Advanced Modelling for Alternative Investments" Research Chair

5 Introduction Importance of Investability for Smart Beta Indices With the advent of smart beta equity indices, which represent alternatives to market-cap weighted indices, a major question on their investabilityhas been raised. Amenc, Goltzand Lodh(2012) and Amenc, Goltzand Martellini(2013) show departing from cap-weighting leads to bearing sizable and significant risks including exposures to systematic risk factors such as size and liquidity. Amencet al. (2011) show that in contrast to cap-weighted indices (which essentially are buy-and-hold investments) smart beta indices exhibit higher levels of turnover. At what cost will investors be able to trade the index constituents in the same proportions as the underlying strategy? The purpose of this presentation is to describe how ERI Scientific Beta ensures the investabilityof its indices with the use of turnover controls and liquidity constraints and explain how these adjustments impact on key implementation metrics such as annual turnover, days-to-trade and index to cap-weight ratios. 5

6 Introduction Investability of Scientific Beta Indices At each stage of the index construction process, adjustments are applied with the aim of ensuringinvestabilityof our indices either by reducing and controlling costs or by improving their liquidity profile in a systematic robust and transparent fashion Geography Stock Selection (factor tilt) Weighting scheme (diversification method) Risk control options Scientific Beta Universe Stock Selection Weighting Scheme 2000 large and mid stocks in Developed Markets Most liquid stocks are selected Explicit factor tilt by selecting stocks with highest factor score such as Value, Size, etc. High liquidity selection is available as option to increase the liquidity of any Scientific Beta universe Diversifies unrewarded risks for a given factor tilt Concentration adjustment to avoid risk corners including illiquid and small cap stocks Turnover control prevents investors from trading on the basis of noise Highest free-float market capitalisation constituents are selected Buffer rules at annual stock selection to limit unnecessary additional turnover Post-optimization capacity constraints protect against large trades or positions in small cap stocks 6

7 Outline Introduction Scientific Beta Global Developed Equity Universe Turnover Control Capacity Constraints Scientific Beta Single-Factor and Multi-Factor Indices Scientific Beta Highly Liquid Indices Investability of Scientific Beta Global Multi-Factor Indices Conclusions 7

8 Scientific Beta Global Equity Developed Equity Universe Defining a Liquid Universe Scientific Beta indices are based on a universe of stocks belonging to the larger capitalisation range and that have been subjected to liquidity screens. The Scientific Beta universe comprises 2000 of the largest and most liquid stocks selected within each Geographic Building Block for Developed Markets for example, 500 stocks in the US, 300 in Eurozone, 100 from the UK, 500 from Japan In such a universe, liquidity issues are limited and Smart Beta strategies can be implemented with ease. 8

9 Scientific Beta Global Equity Developed Equity Universe Process to Define a Liquid Universe Rank all eligible securities within each geographic building block by Keep primary securities with highest Composite Liquidity Score and rank by free-float market cap Scientific Beta Universe Select fixed number of securities for each geographic building block (e.g. 500 for US, 300 for Eurozone etc.) (remove multiple-lines) Remove liquid small / mid cap companies Composite Liquidity Score Composite Liquidity Score = f(average Daily Value Traded, Trading Frequency Remove illiquid securities with lowest Composite Liquidity Score ( 25%, 33% or 50% of eligible universe) Remove illiquid securities with lowest Composite Liquidity Score ( 25%, 33% or 50% of eligible universe) Every quarter liquidity at security level (based on recent trading activity and freefloat market capitalisation) within each Geographic Building Block to select the fixed number of securities for the Scientific Beta universe The liquidity score also guides our choice of primary security, in case a company has more than one line of stock 9

10 Outline Introduction Scientific Beta Global Developed Equity Universe Turnover Control Capacity Constraints Scientific Beta Single-Factor and Multi-Factor Indices Scientific Beta Highly Liquid Indices Investability of Scientific Beta Global Multi-Factor Indices Conclusions 10

11 Turnover Control Overview The differences in performance of individual stocks in a portfolio with fixed target weights will lead to deviations from those targeted weights and investors will need to trade off the costs of rebalancing back to fixed weights versus the performance degradation of not rebalancing the portfolio back to fixed weights Turnoverprovides an intuitive and parsimonious idea of the fund's trading activity and, as such, is a sensible indicator of the actual costs of trading. Scherer (2010) reviews three methods used for managing turnover: Calendar based strategies Conditional or trigger strategies, Tracking error strategies Tracking error strategies benefit from explicitly determining which assets to rebalance in order to keep the tracking error within an acceptable range. Such strategies require solving a complex optimisation which results in lack of transparency for an index 11

12 Turnover Control Calendar Based Strategies Calendar based strategies rebalance the index at certain pre-specified intervals such as monthly, quarterly, semi-annually or annually Buetowet al. (2002) demonstrate that the higher the rebalancing frequency, the better the performance of the portfolio, but the higher the transaction costs. For non-cap weighted indices, lowering the rebalancing frequency to reduce turnover will cause the performance of the index to depend on the market conditions around rebalancing dates, which may not represent the effect of a certain weighting strategy (Donohue and Yip, 2003). Blitz, van dergrientand van Vliet(2010) argue that the subjective choice of rebalancing dates may significantly affect index performance. One way to lessen the impact of market conditions around the rebalancing date is to use staggered rebalancing which involves determining new weights at one point in time (such as yearly) and then slicing the portfolio into equal tranches and implementing the changes to each slice at different times (such as quarterly). 12

13 Turnover Control Conditional or Trigger Strategies The trigger approach activates an index rebalancing whenever the gap between the index current weights and new target weights for all stocks reaches a certain threshold (e.g. ±5% or ±10%) Turnover is controlled by limited changes to weights until a significant amount of new information has been received since the last rebalance The trigger points could be set for individual stock weights as well as for their returns. A higher trigger point results in lower turnover but a greater deviation from the target weights ERI Scientific Beta has opted for a conditional (or trigger) rebalancing approach described in Martellini and Priaulet(2002) and Leland (1999). 13

14 Turnover Control Scientific Beta Turnover Control Scientific Beta Indices are optimisationbased and are reviewed quarterly in March, September and December. At each quarterly review, following the optimisation process, if the suggested weight changes lie below the determined specific turnover threshold, the new optimal weights will not be implemented, and pre-review index weights will be maintained for the start of the forthcoming quarter. Otherwise, if the suggested weight changes reach the determined specific turnover threshold, the new optimal weights will be implemented for the start of the forthcoming quarter. 14

15 Turnover Control Scientific Beta Turnover Control Threshold The threshold level of a Scientific Beta Index is determined through a calibration procedure over its back-test history. First, the index is constructed and calculated over the calibration period for all thresholds spanning from 0% to 100%. Then, the smallest threshold (delta or δ) that results in an average one-way annual turnover below or equal to 30% over the calibration period will be used as the specific turnover threshold for that index in its live period. Index specific turnover thresholds may be re-calibrated at some point in time in order to reflect structural changes in market conditions. Finally, irrespective of whether or not the threshold mentioned above is reached, suggested optimised weights will be used if the index has not been rebalanced optimally for seven consecutive quarters. 15

16 Turnover Control Scientific Beta Turnover Control: An Illustration Turnover control is not binding for Maximum Deconcentration and Diversified Risk Weighted strategies. Turnover control is binding for Maximum Decorrelation, Efficient Minimum Volatility and Maximum Sharpe Ratio strategies. In cases where turnover control is binding the reduction in turnover is accompanied by marginal or no change in strategy returns and volatility. The table shows the annualised return, annualised volatility, annualised 1-way turnover and average market cap of the five Scientific Beta strategy indices and the cap-weighted reference index for the US market. The first panel reports the statistics for the indices before any turnover control is applied, whereas the second panel reports the statistics for the indices after turnover control is applied. Returns and Volatility are calculated using daily total returns in the period: 31/12/1973 to 31/12/2013 (40 years). Weighted Average Market Cap is the weighted average market capitalisation of the index in $m over the 40- year period. Reported Turnover is one-way annualised. Turnover and Weighted Average Market Cap are average values across 160 quarters (40 years). The net returns of transaction costs are obtained using two levels of transaction costs -20 bps per 100% 1-Way turnover and 100 bps per 100% 1-Way turnover. The first case corresponds to the worst case observed historically for the large and mid cap universe of our indices while the second case assumes 80% reduction in market liquidity and a corresponding increase in transaction costs. All statistics are annualised 16

17 Outline Introduction Scientific Beta Global Developed Equity Universe Turnover Control Capacity Constraints Scientific Beta Single-Factor and Multi-Factor Indices Scientific Beta Highly Liquid Indices Investability of Scientific Beta Global Multi-Factor Indices Conclusions 17

18 Capacity Constraints Overview Capacity can be defined as the absolute or relative amount that can be invested in the index in order for its investment objective and/or style to remain intact, without adding additional constraints on liquidity that would force a deviation from the index's stated objective. The principle used to make such adjustments is to impose a threshold for the weight of a stock and for the weight change at rebalancing, relative to the market-cap-weight of the stock in its universe. Scientific Beta indices respect constraints relative to cap-weighted to ensure sufficient capacity. Specifically, Scientific Beta define cap-weight multipliers rules as follows Holding capacity constraints:each stock weight is capped at a multiple of 10 of its free-float adjusted market cap weight to avoid big investment in the smallest stocks. Trading capacity constraints: Change in weight of each stock is capped to its free-float adjusted market cap weight to avoid large rebalancing in small stocks. 18

19 Capacity Constraints Scientific Beta Capacity Constraints: An illustration Capacity constraints do not have a substantial impact on overall performance. The constraints result in a marginal decrease in estimated Days-to-Trade at 95% and increase in average market capitalisation. Capacity constraints have a material impact on the Index-to-Cap ratio for all strategies. The table shows the annualised return, annualised volatility, annualised 1-Wayay turnover and average market cap of the five Scientific Beta strategy indices and the cap-weighted reference index for the US market. The first panel reports the statistics for the indices before any capacity constraint is applied, whereas the second panel reports the statistics for the indices after capacity constraint is applied. Returns and Volatility are calculated using daily total returns in the period: 31/12/1973 to 31/12/2013 (40 years). Weighted Average Market Cap is the weighted average market capitalisation of the index in $m over the 40-year period. Index-to-Cap Weight (Low MktCap Decile) is the median weight ratio of lowest market cap decilestocks averaged across 40 years. Reported Turnover is one-way annualised. Turnover and Weighted Average Market Cap are average values across 160 quarters (40 years). Days-To-Trade is the number of days necessary to trade the total stock positions, assuming US$1bn AUM and that 100% of the Average Daily Dollar Traded Volume can be traded every day. All statistics are annualised 19

20 Outline Introduction Scientific Beta Global Developed Equity Universe Turnover Control Capacity Constraints Scientific Beta Single-Factor and Multi-Factor Indices Scientific Beta Highly Liquid Indices Investability of Scientific Beta Global Multi-Factor Indices Conclusions 20

21 Scientific Beta Single-Factor and Multi-Factor Indices Better Factor Tilts and Better Diversification Smart Beta 2.0 1,2 harnesses the full benefits of smart beta: The stock selection defines exposure to the right (rewarded) risk factors The smart weighting scheme allows unrewarded risks to be reduced: Diversification strategies reduce stock-specific risk (management decisions, product success, etc.) Multi-strategy weighting reduces weighting scheme-specific risk (parameter estimation risk) 3 Tilt to desired factor ("beta") Diversify undesired risks ("smart" weighting) "Smart Beta" 1. Amenc, N., F. Goltz, A. Lodh Choose Your Betas: Benchmarking Alternative Equity Index Strategies. Journal of Portfolio Management. 2. Amenc, N., F. Goltz Smart Beta 2.0. Journal of Index Investing. 3. Amenc, N. F. Goltz, A. Lodh, L. Martellini Diversifying the Diversifiers and Tracking the Tracking Error: Outperforming Cap-Weighted Indices with Limited Risk of Underperformance. Journal of Portfolio Management.

22 Scientific Beta Single-Factor and Multi-Factor Indices Multi-Factor Index Construction Scientific Beta Developed Scientific Beta Developed Value Diversified Value Diversified Multi-Strategy Multi-Strategy Scientific Beta Developed High Momentum Diversified Multi-Strategy Scientific Beta Developed Mid Cap Diversified Multi-Strategy EQUAL RISK CONTRIBUTION (qu uarterly) Scientific Beta Developed High Momentum Diversified Multi-Strategy Scientific Beta Developed Mid Cap Diversified Multi-Strategy EQUAL WEIGHT (quarterly y) Scientific Beta Developed Multi-Beta Multi- Strategy ERC Indices Scientific Beta Developed Multi-Beta Multi- Strategy EW Indices Scientific Beta Developed Scientific Beta Developed Low Volatility Diversified Low Volatility Diversified Multi-Strategy Multi-Strategy 22

23 Scientific Beta Single Factor and Multi-Factor Indices Measuring the Investability of Scientific Beta Indices I The multi-beta allocations provide a reduction in turnover (and hence of transaction costs) compared to a separate investment in each of the smart factor indices. The amount of turnover that is internally crossed in multi-beta allocation is 5.65% and 7.52% for EW and ERC, respectively. The excess returns net of unrealistically high transaction costs, even for high momentum indices, remain quite significantly high. The table shows the annualised return, annualised volatility, annualised 1-way turnover and average float of four Scientific Beta single-factor indices, two multi-factor indices and the cap-weighted reference index for the US market. Returns and Volatility are calculated using daily total returns in the period: 31/12/1973 to 31/12/2013 (40 years). Turnover is averaged across 160 quarters (40 years). The net returns of transaction costs are obtained using two levels of transaction costs -20 bps per 100% 1-W turnover and 100 bps per 100% 1-W turnover. The first case corresponds to the worst case observed historically for the large and mid cap universe of our indices while the second case assumes 80% reduction in market liquidity and a corresponding increase in transaction costs. All statistics are annualised. 23

24 Scientific Beta Single Factor and Multi-Factor Indices Measuring the Investability of Scientific Beta Indices II The Days-to-Trade metric ranges from 0.24 for Mid Cap Diversified Multi-Strategy to 0.12 for Multi-Beta Multi-Strategy indices. The capacity of for Multi-Beta MultiStrategyindices remains quite high at around $10bn compared to $47bn for the broad CW index (which itself is highly liquid). The Index-to-Cap Weight Ratio (relative to broad CW index) of single factor indices is high (28.45 for Mid Cap and for Value) due to a reduction in the stock universe through stock selection. In the case of Multi-Beta Multi-Strategy indices, the ratio is brought back to lower levels (below 15). Weighted Average Market Cap is the weighted average market capitalisation of the index in $m over the 40-year period. Weighted Average Market Cap are average values across 160 quarters (40 years). Index-to-Cap Weight (95% Value) is the 95th percentile of individual weight ratios of all stocks and across 40 years. Days-To-Trade is the number of days necessary to trade the total stock positions, assuming US$1bn AUM and that 100% of the Average Daily Dollar Traded Volume can be traded every day. All statistics are annualised. 24

25 Outline Introduction Scientific Beta Global Developed Equity Universe Turnover Control Capacity Constraints Scientific Beta Single-Factor and Multi-Factor Indices Scientific Beta Highly Liquid Indices Investability of Scientific Beta Global Multi-Factor Indices Conclusions 25

26 Scientific Beta Highly Liquid Indices Highly Liquid Indices I Capacity of highly liquid multi-factor indices lies in the range of $14.2bn to $16.2bn, which is a sizeable improvement from already high capacity broad multi-factor indices. This is achieved at the cost of performance which drops slightly from 15.04% to 14.41% for the EW allocation case. All Stocks Highly Liquid Selection US Long Term Cap- (Dec 1973 Dec 2013) MBMS (EW) MBMS (ERC) MBMS (EW) MBMS (ERC) Weighted Annualised return 10.95% 15.04% 14.84% 14.41% 14.02% Annualised volatility 17.38% 15.71% 15.66% 16.26% 16.22% Ann. 1-w turnover 2.67% 29.06% 31.54% 33.43% 36.84% Internally Crossed Turnover % 7.52% 5.57% 7.67% Annualised return net of 20bps transaction costs Annualised return net of 100bps transaction costs Days To Trade for $1 bninitial Investment (Quantile 95%)* 10.95% 14.98% 14.77% 14.34% 13.94% 10.92% 14.75% 14.52% 14.08% 13.65% Average float (US $m) 47,381 10,039 10,931 14,229 16,227 Index to Cap-Weight Ratio (95% Value) The table shows the annualised return, annualised volatility, annualised 1-way turnover and average float of four Scientific Beta single-factor indices, two multi-factor indices (both broad and highly liquid selection) and the cap-weighted reference index for the US market. Returns and Volatility are calculated using daily total returns in the period: 31/12/1973 to 31/12/2013 (40 years). Weighted Average Market Cap is the weighted average market capitalisation of the index in $m over the 40-year period. Reported Turnover is one-way annualised. Turnover and Weighted Average Market Cap are average values across 160 quarters (40 years). The net returns of transaction costs are obtained using two levelsof transaction costs -20 bps per 100% 1-W turnover and 100 bps per 100% 1-W turnover. The first case corresponds to the worst case observed historically for the large and mid cap universe of our indices while the second case assumes 80% reduction in market liquidity and a corresponding increase in transaction costs. Index-toCapWeight is the 95th percentile of individual weight ratios of all stocks and across 40 years. Days-To-Trade is the number of days necessary to trade the total stock positions, assuming US$1bn AUM and that 100% of the Average Daily Dollar Traded Volume can be traded every day. All statistics are annualised. 26

27 Scientific Beta Single Factor and Multi-Factor Indices Highly Liquid Indices II The Highly Liquid filter improves the liquidity profile (in terms of decreasing further the number of Days-to-Trade) of four smart factor indices; Low Volatility, Mid Cap, Value and High Momentum Diversified Multi-Strategy indices. High Liquidity Multi-Beta indices have the best liquidity profile through time The chart shows the estimated 95% tail average (i.e. mean for all observations that lie between the 95th percentile and the maximum) of the number of Days-to-Trade for four popular US smart factor indices (Low Volatility Div. Multi-Strategy, Mid Cap Div. Multi-Strategy, Value Div. Multi-Strategy, High Momentum Div. Multi-Strategy, Multi-Beta Div. Multi-Strategy-EW and Div. Multi-Strategy-ERC) with and without a highly liquid filtering applied, over the period from 31/12/2003 to 31/12/2013 (10 years). Days-To-Trade is the numberof days necessary to trade the total stock positions, assuming a US$1bn AUM and that 100% of the Average Daily Dollar Traded Volume can be traded every day. 27

28 Scientific Beta Single Factor and Multi-Factor Indices Highly Liquid Indices III The initial investment of $1bn into the US Value Diversified Multi-Strategy Index, assuming a 100% daily participation rate, required on average 0.32 days over the 5% least liquid trades, 7 in our history, while, when imposing a highly liquid filter to the same index would have decreased the same estimate to 0.19 Days-to-Trade. The chart shows the estimated 95% tail average (i.e. mean for all observations that lie between the 95th percentile and the maximum) of the number of Days-to-Trade for four popular US smart factor indices (Low Volatility Div. Multi-Strategy, Mid Cap Div. Multi-Strategy, Value Div. Multi-Strategy, High Momentum Div. Multi-Strategy, Multi-Beta Div. Multi-Strategy-EW and Div. Multi- Strategy-ERC) with and without a highly liquid filtering applied, averaged over the period from 31/12/2003 to 31/12/2013 (10 years). Days-To-Trade is the number of days necessary to trade the total stock positions, assuming a US$1bn AUM and that 100% of the Average Daily Dollar Traded Volume can be traded every day. 28

29 Outline Introduction Scientific Beta Global Developed Equity Universe Turnover Control Capacity Constraints Scientific Beta Single-Factor and Multi-Factor Indices Scientific Beta Highly Liquid Indices Investability of Scientific Beta Global Multi-Factor Indices Conclusions 29

30 Investability of Scientific Beta Global Multi-Factor Indices Trading a US $1bn portfolio on the Developed Multi-Beta Multi-Strategy (EW) index requires about 0.27 Days-To-Trade at the 95% Quantile The High Liquidity filter reduces the Days-To-Trade to 0.09 and increases weighted averagemarketcapofstocksfrom$16bnto$23bn. SciBeta Developed Multi-Beta Multi- Strategy Diversified Multi-Strategy All Stocks High Liquidity Stocks Multi-Beta Multi-Beta Multi-Beta Multi- Multi-Strategy Multi-Strategy Strategy EW ERC 1-Way Turnover 39.63% 38.59% 39.83% 38.36% Internally Crossed Turnover 6.22% 7.76% 6.27% 8.12% Days-To-Trade for $1bn Initial Investment (Quantile 95%)* Weighted Avg. Market Cap ($m) Information Ratio Relative Returns 2.61% 2.55% 2.40% 2.38% Relative Returns net of 20 bps transaction costs (historical worst case) 2.53% 2.47% 2.32% 2.31% Relative Returns net of 100 bps transaction costs (extreme liquidity stress scenario) 2.21% 2.16% 2.00% 2.00% The analysis is based on daily total return data from 31/12/2003 to 31/12/2013 (10 years). The SciBeta Developed CW index is used as the cap-weighted reference. The Internally Crossed Turnover is the difference between the turnover of managing the componentindicesseparatelywiththeturnoverof themulti-betaasasinglemandate.daystotradeisthenumberofdaysnecessarytotradethetotalstockpositions,assuminga USD1bnAUMandthat100%oftheAverageDailyDollarTradedVolume can be traded every day. Mean Capacity is the weighted average market capitalisation of the index in $million over the 10-year period. All statistics are computed across 40 quarters(10 years). The net returns are the relative returns over the cap-weighted benchmarknetoftransactioncosts.twolevelsoftransactioncostsareused-20bpsper100%1-wturnoverand100bpsper100%1-wturnover.thefirstcasecorrespondstotheworstcaseobservedhistoricallyforthelargeandmid-capuniverseofour indices while the second case assumes 80% reduction in market liquidity and a corresponding increase in transaction costs. The risk-free rate is the return of the 3-month US Treasury Bill. The full names of the global developed indices used are: SciBeta Developed Mid-Cap Diversified Multi-Strategy, SciBeta Developed High-Momentum Diversified Multi-Strategy, SciBeta Developed Low-Volatility Diversified Multi-Strategy, SciBeta Developed Value Diversified Multi-Strategy, SciBeta Developed Multi-Beta Multi-Strategy EW, SciBeta Developed Multi-Beta Multi-Strategy ERC. Source: To Trade is the number of days necessary to trade the total stock positions, assuming USD1bn AUM and that 100% of the Average Daily Dollar TradedVolumecanbetradedeveryday.Duetodataavailability,theperiodisrestrictedtothelast10yearsofthesamplefortheScientificBetaUSindices. EW ERC Copyright ERI Scientific Beta. All rights reserved. Please refer to the disclaimer at the end of this document.

31 Outline Introduction Scientific Beta Global Developed Equity Universe Turnover Control Capacity Constraints Scientific Beta Single-Factor and Multi-Factor Indices Scientific Beta Highly Liquid Indices Conclusions 31

32 Investability of Smart Beta Indices Conclusions Investabilityof smart beta indices can be assessed by asking at what cost will investors be able to trade the index constituents in the same proportion as the underlying strategy. Minimising costs requires explicit control of turnover, capacity and liquidity Scientific Beta uses a conditional or trigger based methodology to control turnover in a systematic, robust and transparent way resulting in average annual one-way turnover of 30% for US Multi-Factor indices (40 years) and 39% for Developed indices (10 years) Implementing a multi-beta index in a single mandate exploits the benefits of natural crossing of single index trades resulting in lower turnover. Imposing capacity constraints to index constituents both at the trading and holding levels controls the imbalance between the weight allocated to smaller market cap stocks and their corresponding cap. From a trading perspective, Scientific Beta Indices exhibit very low cost metrics in terms of the number of days to trade any of our indices. Trading a US$1bn portfolio on the US Multi-Beta Multi-Strategy (EW) index requires about 0.12 days in 95% of trades. Our Highly Liquid single and multi-factor indices result in a further significant reduction in the average days-to-trade. US Highly Liquid Multi-Factor reduces the days-to-trade 95% of the index from 0.12 to

33 Disclaimer Copyright 2013 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 unauthorisedpurposes. 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 involvedin, 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 obtainedby 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 anyliability 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 calculatesthe 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 theindex. 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 fromthe 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.