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26An ERI Scientific Beta Publication — The Dimensions of Quality Investing: High Profitability and Low Investment Smart Factor Indices — July 2015Copyright © 2015 ERI Scientific Beta. All rights reserved. Please refer to the disclaimer at the end of this document.2. Smart Factor Indices for High Profitabilityand Low Investment Tilts2.2.5. InvestabilityIssues relating to high turnover and capacity are usually held up as arguments against factorinvesting. The results of a recent survey conducted by EDHEC-Risk Institute shows that investors areconcerned about the implementation constraints of smart beta strategies.Exhibit 13 shows a summary of findings of the EDHEC-Risk Alternative Equity Beta Surveyconducted as part of the Newedge "Advanced Modelling for Alternative Investments" researchchair at EDHEC-Risk Institute (Badaoui et al., 2015). 3 Survey participants were provided with a list ofpotential reasons as to why they would not invest in smart beta strategies and were asked to ratethese from 1 to 5, with 1 being the weakest reason and 5 being the strongest for not choosing smartbeta investment strategies. As can be seen from Exhibit 13, the survey reveals that “Issues related toturnover and capacity” (with an average score of 3.23 out of 5) is the second most important reasonwhy investors are reluctant to choose smart beta strategies, just after robustness concerns.Exhibit 13: Summary of EDHEC Risk Alternative Equity Beta Survey conducted as part of the Newedge "Advanced Modelling for AlternativeInvestments" research chair at EDHEC-Risk InstituteReasons for not Investing in Smart Beta StrategiesAverage ScoreDoubts over robustness of outperformance 3.62Issues related to turnover and capacity 3.23Limited information on risks 3.10Limited availability of independent research 2.97Limited availability of data 2.87High licensing fees 2.82Insufficient explanation of concepts behind offerings 2.76Low transparency of rules 2.60Insufficient number of offerings 2.40Turnover rules and liquidity rules are applied to smart factor indices to overcome the problemsof high turnover and limited capacity. Within the Scientific Beta index construction process,adjustments are performed with the aim of ensuring the investability of our indices, either byreducing and controlling turnover-related costs, or by allowing their liquidity profile to be improvedin a systematic, robust and transparent fashion. A conditional rebalancing approach is used thatavoids unnecessary rebalancing unless a significant amount of new information has been receivedsince the last index rebalancing, hence avoiding rebalancing due to noise. The capacity constraintsallow us to manage the deviations from the cap-weighted reference index in terms of individualstock market capitalisation both at the trading and the holding levels. 4To foster more liquidity, investors have the option of making a highly liquid stock selection on top ofthe existing factor-tilted selection. These indices are constructed on the most liquid 70% of stocksamong the selected stocks and are called “high liquidity smart factor indices.” High profitabilityand low investment smart factor indices, constructed within the consistent smart factor indexconstruction framework, are also subject to the investability checks.3 - Badaoui, S., F. Goltz, V. Le Sourd and A. Lodh. 2015. Alternative Equity Beta Investing: A Survey. EDHEC-Risk Institute Publication (forthcoming).4 - The target for smart factor indices is 30% 1-way annual turnover. For more information on turnover and liquidity rules, please refer to the white paper“Overview of Diversification Strategies” by Gonzalez and Thabault (2013).
An ERI Scientific Beta Publication — The Dimensions of Quality Investing: High Profitability and Low Investment Smart Factor Indices — July 2015Copyright © 2015 ERI Scientific Beta. All rights reserved. Please refer to the disclaimer at the end of this document.272. Smart Factor Indices for High Profitabilityand Low Investment TiltsExhibit 14 shows that the capacity of both standard smart factor indices is sufficiently high at$11.1bn and $15.2bn, compared to $50.2bn for the broad CW index. The ‘Highly Liquid’ filterimproves capacity drastically ($15.7bn and $22.5bn respectively). The impact of the high liquidityfilter on performance is marginal – outperformance is reduced from 3.89% to 3.29% for the lowinvestment smart factor index, and from 3.29% to 2.54% for the high profitability smart factor index.The annual one-way turnovers of the low investment and high profitability smart factor indices are31.7% and 22.2% respectively. Two levels of transaction costs are used to obtain a more realisticestimate of the impact of trading on outperformance in practice: - 20 bps per 100% 1-W turnoverand 100 bps per 100% 1-W turnover (extreme scenario). The excess returns net of transaction costsare still quite significantly high – 3.22% and 2.29% for highly-liquid low investment and highlyliquidhigh profitability smart factor indices respectively.Exhibit 14: Investability of High Profitability and Low Investment Smart Factor IndicesAll statistics are annualised. Yield on Secondary US Treasury Bills (3M) is used as a proxy for the risk-free rate. The analysis is based on daily totalreturn data from 31 December 1974 to 31 December 2014 (40 years). The Scientific Beta LTTR Low Investment Diversified Multi-Strategy portfoliosare constructed on the 50% of stocks with the lowest 2-Year total asset growth rate in the US universe. The Scientific Beta LTTR High ProfitabilityDiversified Multi-Strategy portfolios are constructed on the 50% of stocks with the highest Gross Profit/Total Assets ratio in the US universe. TheHigh Liquidity filter selects (over the existing selection) the top 60% stocks by past liquidity. The Scientific Beta LTTR US universe consists of the 500largest US stocks. The benchmark is the cap-weighted portfolio of all stocks in the US universe. The effective number of stocks (ENS) is defined asthe reciprocal of the Herfindahl Index, which is defined as the sum of squared weights across portfolio constituents. Mean Capacity is the weightedaverage market capitalisation of the index in $million.Dec 1974 – Dec 2014Scientific Beta Long Term Track Records Multi-Strategy(40 Years)All Stocks Standard Highly Liquid SelectionCap-Weighted Low Invest. High Profit. Low Invest. High Profit.Effective Number 117 190 201 117 124Capacity ($million) 50,222 11,108 15,221 15,744 22,533Ann. 1-Way Turnover 2.68% 31.70% 22.21% 33.92% 24.08%Information Ratio - 0.72 0.76 0.67 0.64Ann. Relative Returns - 3.89% 3.33% 3.29% 2.54%Net Returns (20 bps) - 3.83% 3.29% 3.22% 2.49%Net Returns (100 bps) - 3.58% 3.11% 2.95% 2.29%2.2.6. Performance Analysis of Scientific Beta Investable IndicesFinally, we present a performance and risk snapshot for the Scientific Beta low investment andhigh profitability smart factor indices in six different developed regions – Eurozone, UK, Japan,Developed Asia Pacific ex Japan, Developed ex US, and Developed. Scientific Beta investableindices are constructed using price and fundamentals data from the CIQ database. It is these indicesthat constitute the investable reference offered by Scientific Beta. The long-term track records usedpreviously serve as a basis for long-term performance and risk analyses that are useful for qualifyingthe robustness and economic and statistical significance of the performances of the strategy. 6For Scientific Beta investable indices, the eligibility of securities is decided by i) the stock exchange,ii) the type of instrument and iii) the issue date. In each Scientific Beta universe, a liquidity screen5 - 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.6 - The Scientific Beta US Long-Term Track Records are based on stocks that are members of the S&P 500 universe and are alive at the cut-off day. Thebenchmark used is the total market-cap-weighted portfolio of all stocks. These track records are updated yearly, on 15 May of year (y+1) for end of year (y).At the time that this study was published, the most recent long-term track records available were those from 2013.
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