Russell IndexesRUSSELL INDEXES OCTOBER 2014 About Russell Indexes Russell’s indexes business,...

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RUSSELL INDEXES OCTOBER 2014 AN OVERVIEW OF RUSSELL SMART BETA INDEXES SMART BETA GUIDEBOOK

Transcript of Russell IndexesRUSSELL INDEXES OCTOBER 2014 About Russell Indexes Russell’s indexes business,...

RUSSELLINDEXES

OCTOBER 2014

About Russell Indexes

Russell’s indexes business, which began in 1984, accurately measures U.S. market segments and tracks investment manager behavior for Russell’s investment management and consulting businesses. Today, our series of U.S. and global equity indexes reflects distinct investment universes – asset class, geographic region, capitalization and style – with no gaps or overlaps.

Russell Indexes offers more than three dozen product families and calculates more than 700,000 benchmarks daily, covering 98% of the investable market globally, 81 countries and more than 10,000 securities. Approximately $5.2 trillion in assets are benchmarked to the Russell Indexes.

Russell Indexes by Russell Investments

Contact us for more information

Email: [email protected]

Americas: +1-877-503-6437 APAC: +65-6880-5003 EMEA: +44-0-20-7024-6600

www.russell.com/indexes

AN OVERVIEW OF RUSSELL SMART BETA INDEXES

SMART BETA GUIDEBOOK

RUSSELL INDEXES SMART BETA GUIDEBOOK

In 2014 Russell Indexes celebrated 30 years of index creation. Highlights of the past three decades include our launching of the world’s first small cap index – the Russell 2000® Index – in 1984 and our pioneering of style benchmarking, with the introduction of value and growth indexes, in 1987. We extended our suite of style benchmarks in 2010 with the launch of the Russell Stability Index® series of defensive and dynamic indexes, the Russell-identified “third dimension of style™.” All along, Russell style indexes have been among the first opportunities for investors to acquire conscious exposures away from the broad market in an index form.

Today investors and their advisors understand that many sources of systematic return – generically called “beta” – can be sharply focused into an index form and used to shape the risk profile of an equity portfolio and provide for unique return patterns over a market cycle. These indexes depart from traditional style indexes in meaningful ways, as we will make clear below. The predominant term of description is “smart beta,” a phrase everyone seems to dislike but uses anyway. As The Economist put it, “Terrible name, interesting trend.” There is no shortage of suggested alternative labels, but none of them have caught on like “smart beta.”

A recent online poll asked advisors and asset owners how they would define “smart beta,” given four choices:

1. Non-cap-weighted 2. Fundamentals-based 3. Seeks to outperform a benchmark or reduce risks 4. All of the above

A majority answered “All of the above.” Clearly, a range of overlapping views is circulating. We offer our own simple definition of smart beta:

Transparent, rules-based indexes designed to provide exposure to specific factors, market segments or systematic strategies.

A guide to Russell Smart Beta Indexes

1 Tom Goodwin is a Senior Research Director for Russell Indexes in New York. [email protected]. 2 A special thanks to Linda Foster, Layla Hirschfelt and Amy Bhatt (née Doshi) for data and analysis support. Helpful comments were received from Mary Fjelstad, Mark Paris, David Koenig, Scott Bennett, Leola Ross, Guillermo Cano, Tom Jenkins, Maxine Elliott, Tricia O’Connell, Tara Snedden and Gareth Parker.3 Rolf Agather (2014), “The Russell 3000® Index Series: 30 Years of Smarter Beta,” Russell Index Insights.4 Russell Indexes are unmanaged and cannot be invested in directly. One must invest in a mutual fund or exchange-traded fund that closely tracks the index.5 “The Rise of Smart Beta,” The Economist, July 6, 2013.6 Max Chen (2014), “Institutional Investors, RIAs, Warm Up to Smart Beta ETFs,” ETF Trends, May 28.

Tom Goodwin, Ph.D. 1, 2

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OCTOBER 2014

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Russell Smart Beta Indexes

Factor IndexesStrategy Indexes

High Efficiency Factor Indexes

Optimized Factor Indexes

Russell-Axioma Low Beta

Russell-Axioma High Momentum

Russell-Axioma Low Volatility

High Efficiency Defensive (HEDI)

Fundamental

High Dividend Yield

Geographic Exposure

High Efficiency Quality

High Efficiency Low Volatility

High Efficiency Momentum

High Efficiency Value

Figure 1: Russell Smart Beta Indexes

Equal Weighted

The key words here are “transparent” and “rules-based.” All Russell indexes – broad market, style and smart beta – are accompanied by documents that provide relevant details of their construction and methodology. These are publicly available on the Russell website.

The recent launch of the Russell High Efficiency Factor Index Series (HEFI) gives us an opportunity to survey all of our smart beta offerings and put them in appropriate context for investors. Thus our paper is structured as follows: In Part 1, we summarize the investment rationale and performance of 12 Russell smart beta indexes, with special attention to their factor exposures. In Figure 1, we summarize Russell’s current smart beta offerings. Our analysis of these 12 indexes follows. In Part 2, we explore some index combinations that might help investors and their advisors generate their own ideas for shaping the exact risk/return profile desired.

7 James Barber, Scott Bennett and Mark Paris (2014), “Russell High Efficiency Factor Index Series,” Russell Research.

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Table of ContentsPart 1 – Overview of Russell Smart Beta Indexes Performance statistics

Estimating smart beta index exposures A note on data and estimation Russell High Efficiency™ Factor Index (HEFI) series

Russell High Efficiency™ Low Volatility Index (HELVI)

Performance statistics

Factor and style exposures

Russell High Efficiency™ Momentum Index (HEMI)

Performance statistics

Factor and style exposures

Russell High Efficiency™ Quality Index (HEQI)

Performance statistics

Factor and style exposures

Russell High Efficiency™ Value Index (HEVI) Performance statistics Factor and style exposures

Russell Strategy Indexes Russell High Efficiency™ Defensive Index® (HEDI) Performance statistics Factor and style exposures

Russell Fundamental Index®

Performance statistics

Factor and style exposures Russell Equal Weight Index Performance statistics Factor and style exposures Russell High Dividend Yield Index

Performance statistics Factor and style exposures Russell Geographic Exposure Index

Performance statistics

Factor and style exposures

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Russell-Axioma Factor Indexes Russell-Axioma Low Beta Index Performance statistics Factor and style exposures Russell-Axioma High Momentum Index Performance statistics Factor and style exposures Russell-Axioma Low Volatility Index

Performance statistics Factor and style exposures Part 2 – Combining smart beta indexes to improve diversification and shape portfolio risk Combinations of Russell High Efficiency Factor Indexes (HEFI) Combining High Efficiency Factor Indexes with other smart beta indexes Combining Fundamental and Equal Weight with Momentum Combining Equal Weight with Low Volatility

Combining Geographic Exposure with Value

Part 3 – Conclusion: Smart Beta is a major expansion of the investor toolbox Appendix – Performance measures and factor models

Effective Number of Stocks

Active Share The Fama-French model A style exposure model

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Performance statistics

Most of the performance statistics presented in this paper require no special explanation. Two exceptions are “effective number of stocks” and “active share.”

The effective number of stocks is a measure of how concentrated an index is. At one extreme, if an index has all of its weight on one stock and none on the rest of the constituents, then there is effectively only one stock in the index. At the other extreme, if the index is equal weighted, then the effective number of stocks is equal to N, where N is the number of actual stocks in the index. So the higher the effective number of stocks, the less concentrated the index. This is a more complete measure of concentration than the usual “top 10 weights,” as it incorporates the entire distribution of index weights. Generally speaking, a higher number of effective stocks (lower concentration) is associated with better diversification, which is desirable. But the diversification impact of a particular index might best be measured in relation to the investor’s overall portfolio. The formula for this measure is provided in the Appendix.

Active share measures the percentage of an active portfolio that differs from a cap-weighted benchmark index. Active share is based on individual weights in both the portfolio and the benchmark. We make the smart beta index the “active” portfolio for this measure, even though there is no active security selection involved. At one extreme, if none of the stocks of a smart beta index were in the cap-weighted benchmark, then the active share would be 100%. At the other extreme, if all of the weights in a smart beta index match the cap-weighted benchmark weights, then the active share would be 0%.

“Active share” complements “tracking error,” the other measure of how active a portfolio is. The two measures can lead to different conclusions, depending on how much systematic risk is in the active weights. An index might have small active weights in a volatile sector that generates high tracking error, and vice versa. Recent research has shown that active share together with tracking error is positively correlated with outperformance in active mutual funds. As a point of reference, the same research showed that the average active share for active mutual funds was 81%, and the average tracking error was 7.1%. The formula for active share is provided in the Appendix.

Overview of Russell Smart Beta Indexes

Part 1

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8 Antti Petajisto (2013), “Active Share and Mutual Fund Performance,” Financial Analysts Journal, July/August.

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Estimating smart beta index exposures

Not too long ago, the notion of “factors” was considered the exclusive province of academics and quantitative managers. Today, factors are in the mainstream of the investment conversation. There is now awareness that decomposing an investment portfolio or index into its relevant factors can provide insight into the sources of risk and return. This paper employs two complementary models for capturing the exposures of the smart beta indexes. One is the well-known Fama-French model and the other is a style exposure model based on Russell style indexes.

Why two models? The two might appear to duplicate each other, in that they both have factors for cap size and value, and both are returns-based. However, the differences in their methodologies for capturing the cap size and value factors are enough to be meaningful in some cases, where they would give us two distinct views. The Fama-French factors are drawn from the CRSP database, while the Russell style index returns are drawn from the Russell global database. The data source for the Fama-French factor returns is Professor Kenneth French’s website. It does not maintain a fully global or emerging markets set of factor returns, but those regions are available for Russell styles. The Fama-French model includes a momentum factor return, which does not have a Russell style counterpart, while the style exposure model includes a stability factor (defensive vs. dynamic) return, which the Fama–French model does not have. In the Appendix we briefly review the mathematical components of each model.

The Fama-French methodology calculates factor returns as differences in medians of quantiles. This removes sensitivity to outliers at the cost of some market information. Importantly, one cannot invest in the medians of quantiles, so the Fama-French factor returns are not implementable. The style returns incorporate all market information, but might be sensitive to extreme events in a particular stock or sector. However, the Russell style indexes used in the style exposure model are fully investable and implementable. As well, there is some academic research suggesting that models based on style benchmarks are more useful for performance evaluation than the Fama-French model. The use of two models with differing methodologies brings a degree of robustness to the analysis in addition to that contributed by the usual measures, such as t-statistics. When the two models agree, we can have more confidence. When they conflict, an increase in caution is warranted.

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9 CRSP is the Center for Research in Security Prices, based at the University of Chicago.10 See Kenneth R. French at http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html11 Martijn Cremers, Antti Petajisto and Eric Zitzewitz (2010), “Should Benchmark Indices Have Alpha? Revisiting Performance Evaluation,” American Finance Association 2010 Meetings Paper SSRN-id1540262; also (forthcoming) Critical Finance Review VOL 2.

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A note on data and estimation

Throughout, we use all available data histories through March 31, 2014 to calculate performance statistics and exposures, rather than truncating the data to the shortest date range of any of the series. This is based on the principle that using the most information is best; plus, it reduces concerns about cherry-picking date ranges to produce a particular result. Also, the histories contain a mix of simulated and live data. Footnotes indicate where simulation ends and live performance begins. The reader should keep in mind the differing date ranges and mix of data types when looking across indexes.

The t-statistics of all regression coefficients are calculated by use of Newey–West robust standard errors. Newey–West standard errors are consistent estimates of the true standard errors in the presence of either heteroskedasticity or autocorrelation in the regression residuals.

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12 Whitney Newey and Kenneth West (1987), “A Simple, Positive Semi-Definite, Heteroskedastic and Autocorrelation Consistent Covariance Matrix,” Econometrica, Vol. 55.

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13 The NLP weighting methodology is based on a modified logistic probability distribution that naturally reduces the impact of outliers without losing their information content; see James Barber, Scott Bennett and Mark Paris (2014) op. cit.

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Russell High Efficiency™ Factor Index (HEFI) series

On April 30, 2014, Russell launched a suite of four single-factor-based indexes: Low Volatility, Momentum, Quality and Value. The HEFI series is offered for six large cap regions, namely Global, Developed, Developed ex-U.S., U.S., Europe and Emerging Markets. The series uses a similar nonlinear probability methodology (NLP) as that employed in constructing Russell’s Value, Growth, Defensive and Dynamic style indexes. “High efficiency” refers to the ability of the methodology to produce indexes that have:

• Consistent, strong factor capture • Low concentration risk • Low turnover • Meaningful departures from capitalization weights • Full transparency • High ability to combine factor exposures • Active risk awareness

An important aspect of the methodology that distinguishes it from style indexes is that the active weights are derived from the factor scores. This departure from cap-weights results in a more complete – i.e., efficient – capture of the factor.

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Russell High Efficiency™ Low Volatility Index (HELVI)

Low-volatility indexes identify companies that have had a more stable return pattern than the broad market in the past. Interest in low-volatility investing rose sharply in the wake of the 2008 financial crisis. Recent practitioner research has shown that low-volatility investing can avoid some of the most volatile stocks, which often also hit investors with low returns. Low-volatility investing can also sidestep some of the “volatility drag” inherent in compound returns over the long term. The HELVI weights are constructed from scores based on an equal combination of trailing 52-week and 60-month volatilities, using the NLP methodology. This produces an index of stocks that are stable both in the short term and the long term.

Performance statistics

Table 1 highlights the past performance statistics of the High Efficiency Low Volatility Index. The absolute volatilities of the index were substantially below the benchmark volatilities, as expected, and the maximum drawdowns were less than those of the parent index for all regions. Increased Sharpe ratios were the result.

The tracking errors of HELVI were comparable to those of active funds, which led to very modest information ratios. Going forward, in contemplating an allocation to HELVI, investors need to be clear on whether their goals are focused more on total risk and return or on cap-weighted, benchmark-relative risk and return. Also, rewards to low-volatility strategies have been cyclical, leading to periods of meaningful underperformance. Nevertheless, HELVI historically succeeded in its major objective of producing broad portfolios of stocks with substantially lower volatility than their benchmarks.

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14 R. Clarke, H. Silva and S. Thorley (2006),”Minimum-Variance Portfolios in the U. S. Equity Market,” Journal of Portfolio Management, Vol. 33; D. Blitz and P. van Vliet (2007), “The Volatility Effect,” Journal of Portfolio Management, Vol. 34; David Koenig (2013), “Russell Low Volatility Indexes: Helping Moderate Life’s Ups and Downs,” Russell Index Insights.15 Petajisto (2013), op. cit. found the average tracking error for more than 1,000 active mutual funds was 7.1%. 16 Figure A4 in Barber, Bennett and Paris (2014) op. cit.

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DevelopedEmerging Markets Europe Global U.S.

HELVI Return 10.2% 10.8% 10.4% 10.3% 10.0%Benchmark Return 8.8% 10.1% 9.8% 8.9% 9.5%HELVI Volatility 12.3% 20.1% 14.8% 12.6% 12.4%Benchmark Volatility 16.2% 25.0% 19.1% 16.5% 16.1%HELVI Sharpe Ratio 0.62 0.40 0.53 0.62 0.61Benchmark Sharpe Ratio 0.39 0.30 0.39 0.38 0.44Excess Return 1.4% 0.7% 0.6% 1.4% 0.5%Tracking Error 7.3% 7.3% 6.8% 7.2% 7.3%Information Ratio 0.19 0.10 0.09 0.20 0.07Active Share 50% 45% 48% 48% 45%HELVI Max Drawdown -45.4% -52.2% -51.3% -46.3% -42.8%Benchmark Max Drawdown -54.0% -62.3% -60.0% -54.9% -51.1%HELVI Effective Number of Stocks 370 150 115 453 164Benchmark Effective Number of Stocks 430 191 154 521 187

All figures are annualized and arithmetic, except drawdown. The benchmarks are the Russell Developed Large Lap, Emerging Markets Large Cap, Europe Large Cap, Global Large Cap and the Russell 1000® respectively. The HELVI data is simulated. Holdings for active share and effective number are as of March 31, 2014.

Table 1: High Efficiency Low Volatility (HELVI) Large Cap performance (August 1996-March 2014)

Factor and style exposures

Figures 2 and 3 summarize the past exposures beyond the broad market or parent index betas. The Fama-French estimates spanned the three regions of U.S., Developed and Europe. The style exposures spanned these regions plus Emerging Markets and fully Global. Both sets of estimates showed a meaningful exposure to value, with the exception of Emerging Markets. Exposures to small cap were varied, with Fama-French having shown a large cap tilt while the style exposures indicated a small cap tilt in three of the regions, and with the U.S. being cap-neutral and Emerging Markets tilting large cap. The conflict in measuring capitalization exposure was due to the different ways of measuring it, as is discussed in the Appendix, which reinforces the value of having two analytical perspectives.

The style model showed that all the HELVI indexes had a large and statistically significant exposure to defensive. This was not surprising, given that the defensive style methodology is a 50/50 combination of low volatility and high quality characteristics.

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Figure 2: High Efficiency Low Volatility Index Fama-French factor exposures (July 1996-March 2014)

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Russell High Efficiency™ Momentum Index (HEMI)

Momentum investing focuses on stocks that have had strong performance over the recent past, with the expectation that the performance will continue for some length of time. The momentum return pattern contradicts efficient markets theory, but has been empirically verified by academic research. Behavioral explanations have been proposed, including investors under-reacting to public news or over-reacting to private information. Whatever the explanation, momentum returns have been fairly consistent over long horizons, with periodic sharp reversals. Underperformance seems to be greatest when the market is transitioning from one part of a cycle to another.

The High Efficiency Momentum Index methodology employs trends from the previous 12 months, with the most recent month dropped. Turnover is an issue with momentum strategies, as market leadership changes frequently. The challenge in constructing a momentum index is finding the right balance between higher momentum exposure and lower turnover. The resulting indexes have a controlled turnover of 57%–67% per year.

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Performance statistics

Table 2 highlights the past performance characteristics of the High Efficiency Momentum Index. The absolute volatilities of the indexes were in the neighborhood of their benchmark volatilities, as were maximum drawdowns. The indexes are all less concentrated than their cap-weighted benchmarks, suggesting a diversification benefit. Tracking error was modest and excess returns over benchmark were good. The result was that both Sharpe ratios based on total performance and information ratios based on relative performance were very good, although drawdowns were similar to their cap-weighted benchmarks. Investors with either a total return or relative return objective may find this index attractive.

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18 N. Jagadeesh and Sheridan Titman (1993), “Returns to Buying Winners and Selling Losers,” Journal of Finance, Vol 48; Mark Carhart (1997), “On the Persistence in Mutual Fund Performance,” Journal of Finance, Vol. 52; Kent Daniel and Tobias Moskowitz (2014), “Momentum Crashes,” NBER Working Paper No. w224039, August.19 Figure A2 in Barber, Bennett and Paris (2014) op. cit.20 Table A2 in Barber, Bennett and Paris (2014) op. cit.

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DevelopedEmerging Markets Europe Global U.S.

HEMI Return 10.9% 11.6% 12.2% 11.3% 11.2%Benchmark Return 8.8% 10.1% 9.8% 8.9% 9.5%HEMI Volatility 16.7% 24.8% 18.5% 17.4% 17.1%Benchmark Volatility 16.2% 25.0% 19.1% 16.5% 16.1%HEMI Sharpe Ratio 0.50 0.37 0.52 0.51 0.51Benchmark Sharpe Ratio 0.39 0.30 0.39 0.38 0.44Excess Return 2.1% 1.6% 2.4% 2.5% 1.7%Tracking Error 4.7% 4.9% 4.8% 4.9% 5.1%Information Ratio 0.45 0.32 0.50 0.51 0.34Active Share 46% 34% 43% 40% 41%HEMI Max Drawdown -53.2% -65.4% -57.0% -56.1% -49.1%Benchmark Max Drawdown -54.0% -62.3% -60.0% -54.9% -51.1%HEMI Effective Number of Stocks 581 209 190 728 248Benchmark Effective Number of Stocks 430 191 154 521 187

All figures are annualized and arithmetic, except drawdown. The benchmarks are the Russell Developed Large Lap, Emerging Markets Large Cap, Europe Large Cap, Global Large Cap and the Russell 1000 respectively. The HEMI data is simulated. Holdings for active share and effective number are as of March 31 2014.

Table 2: High Efficiency Momentum (HEMI) Large Cap performance (August 1996-March 2014)

Factor and style exposures

The exposures on the Fama-French factors were very clear. Figure 4 shows that there was a large loading on the momentum factor – no surprise there – and that there was neutrality with respect to the small cap and value factors.

The style exposures appear to tell a different story, however. Figure 5 shows positive and large growth exposures across the five regions and a range of cap size and defensive exposures. Momentum does not fit neatly into any of the style buckets, so these estimated style exposures may have been affected by correlations with the absent momentum factor. Growth strategies in particular can rely on earnings growth that may have been correlated with price momentum. To test this, we added the Fama-French momentum factor return WML (Winners-Minus-Losers) to the style regressions for the three regions covered by Fama-French. Figure 6 illustrates that once we included a momentum factor, the style exposures were much reduced, while the exposures to momentum were hardly affected compared to the estimates for the pure Fama-French model. This is very robust support for the momentum factor capture of the HEMI indexes.

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Figure 4: High Efficiency Momentum Index Fama-French factor exposures (July 1996-March 2014)

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Figure 6: High Efficiency Momentum Index style exposures with momentum factor (July 1996-March 2014)

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Russell High Efficiency™ Quality Index (HEQI)

The idea of investing in high-quality stocks goes back at least to Graham and Dodd. “Quality” is hard to define, but has evolved to mean a combination of high profitability, low leverage and low earnings volatility. In an efficient market, these characteristics would be fully incorporated into current stock prices and no excess returns would be possible. But recent academic research has confirmed excess returns associated with quality-based investment strategies. The most likely explanation for the existence of excess returns to quality is behavioral: Focusing on high-quality stocks takes the opposite trade from emotion-driven investors who favor high volatility and highly leveraged stocks, which can produce explosive returns in the short term but usually fall to earth over the longer term. A key differentiator of quality from other smart beta strategies is this historical stability of excess returns.

While the quality concept has been utilized in active management for some time, only recently has it been incorporated into index form. Russell took the lead with the creation of its defensive and dynamic style indexes, where quality characteristics are half of the criteria used to sort stocks into the defensive and dynamic buckets. The Russell High Efficiency Quality Index takes these quality characteristics as its sole criteria and weights the stocks with them by use of the NLP methodology. This produces a consistent, strong factor capture.

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Performance statistics

Table 3 highlights the past performance statistics of the High Efficiency Quality Index. Total volatilities were at or below parent index volatilities, as were maximum drawdowns. Tracking errors were low. With the exception of Emerging Markets, this led to improved Sharpe ratios over benchmark and solid information ratios over the long run, even though excess returns were modest. In the past, excess returns tended to be cyclical with periods of underperformance, such as in the most recent bull market.24

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21 D. Graham and D. Dodd (1934), Security Analysis, New York: McGraw-Hill.22 J. Campbell, J. Hilscher and J. Szilagyi (2008), “In Search of Distress Risk,” Journal of Finance, Vol.63; R. Novy-Marx (2013), “The Other Side of Value: The Gross Profitability Premium,” Journal of Financial Economics, Vol. 108.23 Dave Hintz (2010), “The Third Dimension of Style,” Russell Research.24 Figure A3 in Barber, Bennett and Paris (2014) op. cit.

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HEQI Return 10.2% 10.2% 10.8% 10.4% 10.9%Benchmark Return 8.8% 10.1% 9.8% 8.9% 9.5%HEQI Volatility 15.8% 23.3% 17.2% 16.5% 15.6%Benchmark Volatility 16.2% 25.0% 19.1% 16.5% 16.1%HEQI Sharpe Ratio 0.49 0.33 0.48 0.53 0.51Benchmark Sharpe Ratio 0.39 0.30 0.39 0.38 0.44Excess Return 1.4% 0.1% 1.0% 1.5% 1.4%Tracking Error 2.7% 3.7% 3.8% 2.9% 2.7%Information Ratio 0.51 0.03 0.26 0.53 0.51Active Share 42% 39% 41% 42% 34%HEQI Max Drawdown -49.3% -59.2% -54.8% -51.7% -44.9%Benchmark Max Drawdown -54.0% -62.3% -60.0% -54.9% -51.1%HEQI Effective Number of Stocks 413 168 134 510 184Benchmark Effective Number of Stocks 430 191 154 521 187

Table 3: High Efficiency Quality (HEQI) Large Cap performance (August 1996-March 2014)

All figures are annualized and arithmetic, except for drawdown. The benchmarks are the Russell Developed Large Lap, Emerging Markets Large Cap, Europe Large Cap, Global Large Cap and the Russell 1000 respectively. The HEQI data is simulated. Holdings for active share and effective number are as of March 31, 2014.

Factor and style exposures

The past exposures for the Fama-French factors and style exposures are illustrated in Figures 7 and 8. The style model showed meaningful exposures to defensive. This was not a surprise, given that high quality was half of the criteria used to select defensive stocks, and that the same quality characteristics were used to weight stocks in HEQI. Both models also show meaningful exposures to growth, which might seem odd at first. But growth stocks tend to favor high-profit companies as well, and so there was some overlap in the strategies. Also, one tends to pay more for quality. There was no clear cap tilt evident between the two models.

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Figure 7: High Efficiency Quality Index Fama-French factor exposures (July 1996-March 2014)

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Russell High Efficiency™ Value Index (HEVI)

Value investing is so prevalent that it needs almost no introduction; this theory, too, goes back at least to Graham and Dodd. The value premium has been one of the most extensively confirmed premiums in finance, by empirical research. There is controversy over the source of the value premium (is it compensation for shouldering more risk, or is it the result of repeated errors on the part of some investors?), but not much controversy over its existence. Russell pioneered the capturing of the value premium in index form with its Russell Value Style Index. The benefit of capturing the value factor in a style index is attenuated, however, by the cap weighting of the selected stocks. Also, the style index uses only one value indicator, the book-to-price ratio, along with two growth indicators. The High Efficiency Value Index more fully captures the value premium by adding the earnings-to-price ratio to the book-to-price ratio to create value scores, without any growth indicators. Most importantly, it weights the active positions by that value score.

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Performance statistics

Table 4 highlights the past performance statistics of the Russell High Efficiency Value Index. Total volatilities were at or slightly higher than parent index volatilities, and maximum drawdowns were greater, with the exception of Emerging Markets. But excess returns over the long run were substantial and tracking errors were modest. This led to solid Sharpe and information ratios over the long run. As with any systematic strategy, a value strategy would have had periods of underperformance, e.g., in the dot-com boom of the late 1990s.

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25 Graham and Dodd (1934) op. cit.26 Eugene Fama and Kenneth French (1993), “Common Risk Factors in the Returns on Stocks and Bonds,” Journal of Financial Economics, Vol. 33; J. Lakonishok, A. Shleifer and R. Vishny (1994), “Contrarian Investment, Extrapolation and Risk,” Journal of Finance.27 Figure A1, Barber, Bennett and Paris (2014), op. cit.

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DevelopedEmerging Markets Europe Global U.S.

HEVI Return 11.8% 11.8% 11.6% 12.1% 12.0%Benchmark Return 8.8% 10.1% 9.8% 8.9% 9.5%HEVI Volatility 16.8% 25.0% 20.3% 17.4% 16.5%Benchmark Volatility 16.2% 25.0% 19.1% 16.5% 16.1%HEVI Sharpe Ratio 0.55 0.37 0.45 0.55 0.57Benchmark Sharpe Ratio 0.39 0.30 0.39 0.38 0.44Excess Return 3.0% 1.7% 1.9% 3.3% 2.5%Tracking Error 5.5% 4.8% 4.4% 5.6% 5.9%Information Ratio 0.54 0.36 0.43 0.55 0.41Active Share 49% 44% 41% 53% 39%HEVI Max Drawdown -56.9% -59.8% -63.6% -57.2% -54.0%Benchmark Max Drawdown -54.0% -62.3% -60.0% -54.9% -51.1%HEVI Effective Number of Stocks 441 170 164 543 183Benchmark Effective Number of Stocks 430 191 154 521 187

Table 4: High Efficiency Value Index (HEVI) Large Cap performance (August 1996-March 2014)

All figures are annualized and arithmetic, except drawdown. The benchmarks are the Russell Developed Large Lap, Emerging Markets Large Cap, Europe Large Cap, Global Large Cap and the Russell 1000® respectively. The HEVI data is simulated. Holdings for active share and effective number are as of March 31, 2014.

Factor and style exposures

The Fama-French factor and Russell style exposures are illustrated in Figures 9 and 10. Both models show there were strong exposures to value, which is what one would hope to see from a value index. The estimated factor and style exposures agreed that the U.S. HEVI had a slight large cap tilt while the Developed and Europe indexes had slight small cap tilts. The negative momentum exposures could have been due to value selection and weighting that avoided growth-driven trending stocks.

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Figure 9: High Efficiency Value Index Fama-French factor exposures (July 1996-March 2014)

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Russell Strategy Indexes

Russell High Efficiency™ Defensive Index® (HEDI)

The Russell High Efficiency Defensive Index leverages the low volatility and high quality factors utilized in construction of the Russell Defensive style index. The defensive style index relies on cap weighting after selection. HEDI more efficiently utilizes the defensive signal by first selecting stocks by combined factor scores and then weighting the active positions by the same factor scores. This methodology also provides tracking error and turnover controls. Low and moderate tracking error versions of the indexes are produced.

The investment proposition behind combining low volatility with high quality factors is that the two together provide a more complete picture of company risk. Assuming that low volatility alone is synonymous with low company risk can make an investor blind to the risks of firms with high leverage and uncertain earnings prospects – a kind of low-volatility trap. A combination of high quality and low volatility characteristics more accurately identifies a firm as low risk, as our research has confirmed. HEDI provides the investor with a portfolio of genuinely low-risk companies. In efficient markets theory, lower risk is associated with lower return; but behavioral effects may counter that theoretical prediction, producing greater risk-adjusted returns.

Figure 11 is a schematic showing the construction of the HEDI combined factor stability scores, which are then mapped into weights. Note that the characteristics of the volatility and quality factors are identical to those used separately in the High Efficiency Low Volatility and High Efficiency Quality indexes, but that the calculation of the scores differs. The available regions are U.S., Global, Global ex-U.S., Developed, Developed ex-U.S., Developed ex-North America, Emerging Markets, Europe and Asia-Pacific ex-Japan. All HEDI indexes are large cap only, except for the U.S., which is available in small cap, large cap and all-cap versions. They are also available with low and moderate tracking error targets. The indexes of four regions were selected for analysis. The selected indexes were all preset with moderate tracking error targets.

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28 Barry Feldman (2012), “Stability Is the Risk Dimension of Equity Style,” Russell Research.

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Volitility factors

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Performance statistics

Table 5 shows past performance statistics of HEDI Large Cap with moderate targeted tracking error. The lower total volatilities and maximum drawdowns compared to their benchmarks were as expected, given the index construction. And the tracking error control methodology worked to produce “moderate” levels of relative volatility. Substantial excess returns were generated for all regions over the sample periods, except the U.S. Overall, HEDI historically achieved the stated goal of low total and relative risk/return patterns.

DevelopedEmerging Markets Europe U.S.

HEDI Return 9.8% 16.7% 11.8% 10.3%Benchmark Return 7.7% 14.7% 9.3% 9.5%HEDI Volatility 12.6% 19.8% 15.9% 12.6%Benchmark Volatility 16.4% 23.9% 20.3% 16.1%HEDI Sharpe Ratio 0.66 0.77 0.65 0.62Benchmark Sharpe Ratio 0.38 0.55 0.38 0.44Excess Return 2.2% 2.0% 2.6% 0.8%Tracking Error 5.0% 5.4% 6.3% 6.0%Information Ratio 0.43 0.37 0.41 0.13Active Share 54% 47% 49% 52%HEDI Max Drawdown -42.7% -51.6% -48.1% -40.8%Benchmark Max Drawdown -54.0% -62.3% -60.0% -51.1%HEDI Effective Number of Stocks 262 122 102 113Benchmark Effective Number of stocks 430 191 154 187

All figures are annualized and arithmetic, except for drawdown. The benchmarks are the Russell Developed Large Cap, Emerging Markets Large Cap, Europe Large Cap and Russell 1000 Indexes, respectively. The benchmarks are large cap to match the HEDI indexes. HEDI data before January 2013 is simulated. Holdings for active share and effective number are as of March 31, 2014.

Table 5: High Efficiency Defensive Index (HEDI) Large Cap, Moderate Tracking Error performance (August 2001-March 2014) (U.S.: August 1996-March 2014)

Factor and style exposures

Figures 12 and 13 show the past Fama-French and style exposures. The Fama–French cap exposures were mostly neutral or showing a large cap tilt, which was not surprising, given that these indexes were large cap. The style model estimates show small cap tilts for Developed and Europe, so there was some ambiguity about cap exposures. On the value/growth dimension, the U.S. index exhibited a meaningful value tilt across both models, but any tilts for the other regions were either conflicting across the two models or small. There did seem to be substantial momentum exposures for Developed and Europe, but not for the U.S. Obviously, the large defensive style exposures were expected.

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Figure 12: High Efficiency Defensive Index - Large Cap, Moderate TE Fama-French factor exposures (August 2001-March 2014) (U.S.: August 1996-March 2014)

Figure 13: High Efficiency Defensive Index - Large Cap, Moderate TE style exposures (August 2001-March 2014) (U.S.: August 1996-March 2014)

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Russell Fundamental Index

The Russell Fundamental Index® series was developed in collaboration with Research Affiliates, LLC. It is based on a systematic strategy that weights stocks by fundamental measures of company size, including sales revenue, cash flow, and dividends plus buybacks. The methodology seeks to completely disconnect the changes in a stock’s weight from changes in its current market price by using alternative measures of company size that do not fluctuate in lockstep with price changes. The breaking of the link with current price fluctuations is ensured by constructing the weights by use of five-year averages of fundamentals and lagging them to the most recent announcements.

The theory behind de-linking index weights from changes in current market price is avoidance of the so-called “performance drag” of cap-weighted indexes. Cap-weighted indexes are “the market” and they produce the market’s return. But because the weights in a cap-weighted index are tied to current price, they automatically increase the allocation to companies whose prices have recently risen, and reduce the index weight for companies whose prices have recently fallen. If the market prices some companies too high and some too low, then cap-weighted indexes will naturally have large concentrations in companies that are overvalued and small concentrations in companies that are undervalued. Cap weights amplify the negative impact on returns when the prices correct back to their “fair value.” This can only happen systematically in an inefficient market. Repetitive behavioral errors such as investor herding and overreaction to news reports are often cited as sources of this drag on returns.

Using fundamental measures of firm size to form weights provides a stable anchor as prices change. Much of the return pattern is generated by routine rebalancing to target fundamental weights – often called “contra-trading” – against the market’s price changes. This typically results in small cap and value factor tilts, as we will see below. It is important to keep in mind, though, that Fundamental Index strategies are not constructed as value or small cap indexes. They are systematic strategies intended to avoid some of the behavioral noise in the market. While a Fundamental Index sometimes has a return pattern similar to that of a value index, at other times the dynamics can be very different. Research has shown that fundamental and value indexes are different enough to be usefully combined in a portfolio.

A perceived drawback of the Fundamental Index methodology is that when a stock goes on a run that is sustained, the positive impact to the index is less than it would be to a cap-weighted index. That is because a fundamental weight does not immediately increase with the stock price, while cap weight does. So Fundamental Index strategies can be expected to underperform during periods of growth and positive momentum, as they would have during

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29 Tom Goodwin (2012), “The Russell Fundamental Index Series: An Investment Strategy,” Russell Research.30 Rob Arnott, Jason Hsu and P. Moore (2005), “Fundamental Indexation,” Financial Analysts Journal, March/April.31 Tom Goodwin (2014), “Passive and Fundamental Index Investing: A Factor Analysis,” Investments & Wealth Monitor, May/June; also (2013) Russell Research.

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the tech bubble of the late 1990s. Of course, the stable weighting works to the benefit of the investor if the stocks that are run up quickly fall back down again.

The available regions are Global, Global ex-U.S., Developed, Developed ex-U.S., Europe, Developed Europe, Emerging Markets, U.S., Asia-Pacific, Asia-Pacific ex-Japan, BRIC (Brazil, Russia, India and China) and Developed Eurozone. All are available in large company, small company and all-company versions. In addition, a U.S. top 200 is available. Four of the regions are selected for analysis.

Performance statistics

Table 6 shows that the total volatilities of the Fundamental Index strategies were roughly on par with cap-weighted benchmark volatilities, but with less drawdown. The excess returns led to relatively high information ratios. The Fundamental Index strategies were more concentrated than their cap-weighted benchmarks.The total and excess returns potential has attracted substantial capital inflows from investors, but one has to keep in mind that periods of underperformance can be expected. The cautionary note holds true – past performance is no guarantee of future performance. An investor should have a belief in the strategy before making an allocation.

DevelopedEmerging Markets Europe U.S.

Fundamental Return 11.4% 14.8% 13.0% 12.3%Benchmark Return 8.8% 10.1% 9.9% 9.5%Fundamental Volatility 15.6% 24.8% 19.7% 15.6%Benchmark Volatility 16.2% 25.0% 19.1% 16.2%Fundamental Sharpe Ratio 0.57 0.50 0.53 0.63Benchmark Sharpe Ratio 0.39 0.30 0.38 0.44Excess Return 2.6% 4.7% 3.1% 2.8%Tracking Error 4.4% 6.9% 4.1% 5.6%Information Ratio 0.58 0.68 0.77 0.51Active Share 52% 43% 30% 27%Fundamental Max Drawdown -51.5% -58.7% -51.5% -50.8%Benchmark Max Drawdown -54.0% -63.0% -60.0% 51.1%Fundamental Effective Number of Stocks 384 94 141 145Benchmark Effective Number of Stocks 519 191 180 221

All figures are annualized and arithmetic, except for drawdown. The benchmarks are the Russell Developed All Cap, Emerging Markets Large Cap, Europe All Cap and Russell 3000® Indexes, respectively. The Fundamental data is simulated before March 2011. Holdings for active share and effective number are as of March 31, 2014.

Table 6: Fundamental Index All Company performance (Emerging Markets: Large Company) (August 1996-March 2014)

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32 Figure 4 in Goodwin (2012) op. cit.33 Tom Goodwin (2013), “Riding the Apple Roller Coaster: Reducing Risk with a Mix of Fundamental-Weighted and Cap-Weighted Indexes,” Russell Index Insights.

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Factor and style exposures

Figures 14 and 15 show that there was a strong value tilt. There was also a lesser tilt to small cap. The anti-momentum character of the Fundamental Index strategy showed up in the negative exposure to momentum. Exposures to the stability style were small, with the exception of a somewhat anomalous exposure to dynamic for Emerging Markets. Fundamental indexes are sometimes characterized by critics as simply being value indexes. But value implies relatively low price as a selection criterion, and fundamental weights are constructed to be independent of whether a stock is priced relatively high or low. As a result, some stocks classified as growth by style because they have a relatively high price will have a large weight in a Fundamental Index strategy. A comparison of Figures 9 and 10 with Figures 14 and 15 shows that historically, the High Efficiency Value Index (HEVI) would have had a stronger capture of the value factor than the Fundamental Index strategy. Investors who wanted only a value factor exposure would have done better with the HEVI index. Fundamental Index strategies are based on a broader investment concept that has delivered dynamic exposures, which have sometimes differed from those of any value index. Research has shown the differences to have been such that combining Fundamental Index strategies with standard value indexes could have provided a better risk/return profile over the sample periods covered.

Figure 14: Fundamental Index Fama-French factor exposures (August 1996-March 2014)

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34 Tom Goodwin (2014), “Passive and Fundamental Index Investing: A Factor Analysis,” Investments & Wealth Monitor, May/June; also (2013) Russell Research.

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Figure 15: Fundamental Index style exposures (August 1996-March 2014)

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Russell Equal Weight Index

The methodology of the Equal Weight Index is almost self-explanatory. Equal-weighted indexes take the notion of delinking index weight from market price a step further than the Fundamental Index strategy does. Not only are equal weights unaffected by price changes, but the weights are also unaffected by price levels, or any other measure of company size. A major benefit of equal weighting is that it avoids the top-heavy concentration that characterizes many cap-weighted and alternatively weighted indexes, where the largest few stocks can at times dominate the performance of the whole index. A low-concentration index has high diversification. Equal-weighted indexes also seek to avoid the performance drag of cap-weighted indexes by rebalancing to target equal weights.

Compared to a cap-weighted index, the equal-weight methodology gives larger weights to smaller capitalization stocks and smaller weights to larger capitalization stocks. Having large weights on small capitalization stocks raises a capacity issue. Potentially, the index could take a major position within a particular small cap stock, moving the price against the index as the stock trades and poses a liquidity risk. Russell applies a screen prior to the construction of the index to remove stocks that might pose that risk. Research has shown that the screen has no meaningful impact on returns. The standard equal-weight methodology used by most index providers is to weight each stock by 1/N, where N is the number of stocks. That creates sector biases where a particular sector may be overweighted simply because there are a lot of names in it, and another sector may be underweighted simply because it has few names. This creates concentration in just a few sectors, with no investment rationale behind it. Russell enhances the standard methodology by first equal-weighting the sectors, and then equal-weighting the stocks within each sector. Research confirms that this improves diversification and enhances returns over the long term, compared to the standard methodology.

Russell equal-weighted indexes are available for U.S. Large Cap, Mid Cap and Small Cap, Global Large Cap, BRIC and Greater China Large Cap regions. The statistical analysis will be confined to the U.S. and global indexes.

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35 While concentration and diversification are inversely related, an equal-weighted index is not necessarily the most diversified. For maximum diversification one needs to take into account the correlations between the stocks.36 Pradeep Velvadapu (2010), “The Russell Equal Weight Indexes: An Enhancement to Equal Weight Methodology,“ Russell Research.37 Velvadapu (2010) op. cit.

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Performance statistics

Table 7 shows that the absolute volatilities of the equal-weighted indexes have been elevated compared to their parent benchmark volatilities, and that drawdown comparisons are mixed. The total returns and excess returns were high enough to result in impressive Sharpe and information ratios over this sample period. And the indexes were meaningfully less concentrated than their parent benchmarks. Given the past high absolute volatility, this might make for a natural pairing of an equal-weighted index with a low-volatility index.

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CapGlobal Large

CapEqual Weight Return 12.4% 13.7% 11.8% 12.5%Benchmark Return 5.6% 10.3% 9.6% 7.9%Equal Weight Volatility 18.1% 18.8% 21.4% 18.6%Benchmark Volatility 15.8% 17.9% 20.7% 16.8%Equal-Weight Sharpe Ratio 0.58 0.79 0.46 0.59Benchmark Sharpe Ratio 0.23 0.47 0.37 0.38Excess Return 6.8% 3.4% 2.2% 4.6%Tracking Error 5.8% 4.3% 4.3% 4.1%Information Ratio 1.18 0.79 0.51 1.13Active Share 52% 33% 42% 52%Equal-Weight Max Drawdown -50.8% -51.7% -54.6% -53.8%Benchmark Max Drawdown -51.1% -54.2% -52.9% -54.9%Equal-Weighted Effective Number of stocks 794 609 1,158 2,559Benchmark Effective Number of stocks 187 523 996 521

All figures are annualized and arithmetic, except max drawdown. The benchmarks are the Russell 1000, Russell Mid Cap, Russell 2000, and Russell Global Large Cap. Equal-weighted data before November 2010 is simulated. Holdings for active share and effective number are as of March 31, 2014.

Table 7: Russell Equal Weight performance (U.S.: February 2000-March 2014) (August 2001-March 2014)

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Factor and style exposures

Figures 16 and 17 show that the factor and style exposures are generally what were expected. Both sets of exposures showed a small cap tilt, although the small cap style exposures were not statistically significant in the U.S. Mid Cap and Large Cap regions (Table A-14). The rebalancing back to equal weights created the value tilt, with the exception of Large Cap Global, which displayed no value tilt. Similar to fundamental weighting, the anti-momentum exposure of the Fama–French model was driven by the fact that when stocks went on a run, equal weights were not run up in parallel. The tilts to the dynamic style were likely a function of the high absolute volatility of the equal weight indexes.

Figure 16: Equal Weight Index Fama-French factor exposures (February 2000-March 2014)

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Figure 17: Equal Weight Index style exposures (U.S.: February 2000-March 2014) (Global: August 2001-March 2014)

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Russell High Dividend Yield Index

Investing in stocks that pay high dividends is a strategy that has been around for a long time. Historically, a dividend-focused strategy has provided steady income streams with relatively low volatility. Interest in the strategy sharply increased once the Federal Reserve lowered interest rates and vowed to keep them low for an extended period of time. That greatly decreased long-term bond yields, the income source many institutional and individual investors had come to rely on. Many of those investors and their advisors came to see stock dividend income as a partial replacement for the lost bond income.

The Russell High Dividend Yield Index series was launched in March 2012 with the objective of providing investors with steady income and low volatility, while avoiding some of the pitfalls of the strategy. Its innovative methodology identifies stocks that pay high dividends but also exhibit financial strength. Various quality screens are used to avoid the so-called “dividend yield trap,” where some stocks have fragile high dividend yields. This includes companies that finance dividend payout with debt, companies paying large dividends one year with the intention to sharply reduce them the next year, and stocks where the yield is high only because the stock’s price has been in free fall. Such companies often disappoint investors.

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38 Xin Yan and Mark Paris (2012), “High Dividend Strategies: Reviving an Old Concept Relevant for Modern Times,” Russell Research.39 Yan and Paris (2012) op. cit.

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Performance statistics

Table 8 confirms that the high dividend yield strategy has tended to have low total volatility and low drawdowns. The indexes were highly concentrated, and tracking error and active share were as much as in most active mutual funds. For the sample range that was available, excess return was meaningful for large cap but nonexistent for small cap. This index would have appeared to be more appropriate for the total risk/return investor, as opposed to the benchmark-sensitive investor.

U.S. Large Cap U.S. Small CapHigh Yield Return 10.1% 10.2%Benchmark Return 6.7% 10.2%High Yield Volatility 13.7% 16.1%Benchmark Volatility 15.4% 20.0%High Yield Sharpe Ratio 0.62 0.54Benchmark Sharpe Ratio 0.34 0.43Excess Return 3.4% 0.0%Tracking Error 8.1% 9.6%Information Ratio 0.42 0.00Active Share 88% 90%High Yield Max Drawdown -49.2% -46.8%Benchmark Max Drawdown -51.1% -52.9%High Yield Effective Number of Stocks 30 94Benchmark Effective Number of Stocks 187 996

All figures are annualized and arithmetic, except drawdown. The benchmarks are the Russell 1000 and Russell 2000®. High Yield Index data before March 2012 is simulated. Holdings for active share and effective number are as of March 31, 2014.

Table 8: High Dividend Yield Index performance (May 2001-March 2014)

40 Petajisto (2013) op. cit.

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Factor and style exposures

The estimated factor and style exposures of Figures 18 and 19 are as expected for a low-volatility/high-quality strategy. The indexes would have had distinct value and defensive tilts.

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Figure 18: High Dividend Yield Index Fama-French factor exposures (May 2001-March 2014)

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Russell Geographic Exposure Index

The Russell Geographic Exposure (“GeoExposure”) Index series target companies in developed markets that derive substantial revenue from doing business in emerging markets. It can be thought of as being either a strategy that provides indirect exposure to emerging economies through developed markets or as one that provides direct exposure to an Emerging Markets “factor” in developed markets. The basic investment idea is to capture some of the future growth in emerging economies through developed companies whose growth is tied to those countries. Developed companies tend to be better capitalized, more transparent and more stable than companies based in and traded within the emerging economies. The indexes are constructed in alliance with Revere Data LLC (now part of FactSet), which supplies country-by-country revenue decomposition for each stock. Fairly concentrated indexes are formed for four regions, with a short list of companies that derive a large amount of their revenue – both percentage and total – from business conducted in emerging economies. The four are: Developed Large Cap (400 stocks), Developed Europe Large Cap (75 stocks), Developed ex-North America Large Cap (300 stocks) and U.S. Large Cap (100 stocks). Below are the performance statistics and exposures for three of the four regions.

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41 Tom Goodwin and Mark Paris (2013), “The Russell Geographic Exposure Index Series,” Russell Research.

Performance statistics

The short history available spans the financial crisis of 2008 and the Emerging Markets bear market of 2013, so it was a good test of the robustness of the strategy. Table 9 shows that the total volatilities and maximum drawdowns of the Russell GeoExposure indexes were right between those of Developed Markets and Emerging Markets (with the exception of Europe volatility). Active shares were substantial. The indexes were relatively concentrated, due to the short list of stocks. While the short history prevents us from drawing any strong conclusions, it is notable that all three GeoExposure indexes managed to provide higher returns than did either their parent benchmarks or Emerging Markets over this time period.

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Developed Large Cap Europe Large Cap U.S. Large Cap

GeoExposure Return 8.7% 7.6% 8.4%Benchmark Return 8.2% 6.3% 7.3%Emerging Markets Return 5.1% 5.1% 5.8%GeoExposure Volatility 23.7% 27.3% 20.5%Benchmark Volatility 19.7% 24.8% 17.6%Emerging Markets Volatility 26.5% 26.5% 26.8%GeoExposure Sharpe Ratio 0.36 0.27 0.38Benchmark Sharpe Ratio 0.41 0.25 0.38Emerging Markets Sharpe Ratiow 0.19 0.19 0.19Excess Return 0.5% 1.2% 1.1%Tracking Error 5.4% 4.6% 5.4%Information Ratio 0.09 0.27 0.20Active Share 66% 66% 69%GeoExposure Max Drawdown -50.6% -55.5% -52.1%Benchmark Max Drawdown -44.5% -60.0% -51.1%Emerging Max Drawdown -52.1% -52.1% -62.3%GeoExposure Effective Number 376 71 96Benchmark Effective Number 430 154 187

All figures are annualized and arithmetic, except for drawdown. The benchmarks are the Russell Developed Large Cap, Russell Europe Large Cap and Russell 1000

Indexes, respectively. GeoExposure data before September 2012 is simulated. Holdings for active share and effective number are as of March 31, 2014.

Table 9: GeoExposure Large Cap Index performance (Developed & Europe: July 2008-March 2014) (U.S.: July 2007-March 2014)

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Factor and style exposures

Both the Fama-French and style model estimates (Figures 20 and 21) show that the GeoExposure indexes had a strong growth tilt. This was due to the indirect exposure to the high growth of emerging economies. Historically, most Emerging Markets stocks have been categorized as growth from a global perspective in the Russell Global Index. As well, the higher volatility relative to the developed benchmarks tilted the indexes toward a dynamic exposure. There were conflicting results on cap exposures between the two models.

42 Tom Goodwin (2014), “The Russell Global Index: The Seamless Benchmark for the Global Investor,” Russell Research.

42

Figure 20: Geographic Exposure Index Fama-French factor exposures (July 2008-March 2014) (U.S.: July 2007-March 2014)

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Figure 21: Geographic Exposure Index style exposures (July 2008-March 2014) (US: July 2007-March 2014)

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Russell-Axioma Factor Indexes

Russell has collaborated with Axioma Inc. to create an index series that employs the Axioma risk model and optimizer in its methodology. The advantage of using an optimizer in index construction is that very specific constraints can be applied, with the result being a “pure” factor capture. By “pure” we mean an index that provides exposure to the targeted factor but seeks to minimize exposure to competing factors. This has the advantage of providing tools investors can use to pinpoint the targeting of factor exposures without getting into a “whack-a-mole” cycle, where one factor is modified in the desired way but other factors pop up or down in unwanted ways, requiring further efforts to get the portfolio exposures into the desired profile.

The methodology involves forming a reference portfolio that goes long the 35% of stocks with the highest/lowest exposure to the desired factor (Beta, Momentum and Volatility). Then a new portfolio is formed by the optimizer, which goes through a hierarchy of constraints: First, it minimizes the tracking error with the reference portfolio; next, it imposes a turnover constraint; and finally, it imposes constraints that enforce low correlations with other factors. The Russell-Axioma indexes tend to appeal to the more quantitative-oriented investors and advisors, who are comfortable in analyzing portfolio risk with the output from optimizers in general and the Axioma risk model in particular.

The Russell-Axioma family of indexes includes Beta, Momentum and Volatility exposures for the U.S. Small Cap, U.S. Large Cap and Developed ex-U.S. Large Cap regions. We will review six of the U.S. long-only indexes: Low Beta, High Momentum and Low Volatility, in large and small cap sizes, as they have generated the most interest.

43

43 For a complete description of the methodology, see “Russell–Axioma Factor Indexes Methodology,” Russell Research, January 2013; also Barry Feldman (2011), “The Russell Axioma U.S. Long-only Factor Indexes,” Russell Research.

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Russell-Axioma Low Beta Index

The “beta” here refers to market beta, the sensitivity of a stock or index to movements in the overall market. It is the average change in the stock or index return associated with a percentage change in the overall market, which is proxied with a broad-market cap-weighted benchmark index. A market beta of 1 means the stock or index moves on average 1% in the same direction as a 1% move in the market. A low-beta index, then, is one in which the value of the constituent stocks do not move up and down as much as the market does.

Low beta is closely related to low volatility. Beta is a measure of the systematic risk of a stock, whereas total volatility encompasses both systematic and idiosyncratic risk. Estimates of market beta are model-dependent. The Axioma model is used to estimate the beta for each stock.

Performance statistics

Table 10 summarizes past performance statistics of the Low Beta indexes for the short history we have. Volatilities of the indexes were well below the volatilities of the parent indexes, as was expected. The same was true of maximum drawdown statistics. The tracking errors and active shares show that the indexes were very “active.” The large cap Sharpe ratios were substantial, while the information ratios were poor – suggesting that these indexes are more appropriate for investors with a total risk/return objective than for those with a benchmark-relative risk/return objective.

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U.S. Large Cap U.S. Small CapLow Beta Return 9.3% 7.7%Benchmark Return 8.7% 10.3%Low Beta Volatility 11.9% 15.6%Benchmark Volatility 15.5% 20.0%Low Beta Sharpe Ratio 0.65 0.40Benchmark Sharpe Ratio 0.46 0.44Excess Return 0.5% -2.6%Tracking Error 6.0% 7.0%Information Ratio 0.09 -0.37Active Share 71% 82%Low Beta Max Drawdown -38.4% -48.2%Benchmark Max Drawdown -51.1% -59.2%Low Beta Effective Number 78 134Benchmark Effective Number 187 996

All figures are annualized and arithmetic, except drawdown. The benchmarks are the Russell 1000 and Russell 2000. Low Beta Index data before May 2011 is simulated. Holdings for active share and effective number are as of March 31, 2014.

Table 10: Russell-Axioma Low Beta Index performance (February 2005-March 2014)

Factor and style exposures

Tables A-19 and A-20 in the Appendix show that the estimated betas for both models and both indexes were in the range of 0.85 to 0.89 – well below 1 – as designed. Figures 22 and 23 show that the cap size exposures were in line with the cap size of the parent indexes. The Fama-French value exposures were close to zero, in line with the design of neutrality with respect to other factors. The style exposures indicate a slight growth bias, but only the small cap growth exposure was statistically significant. There was a small but statistically significant positive exposure to momentum. The strong defensive tilts were to be expected, as a low-beta strategy is closely related to a low-volatility strategy.

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Figure 22: Russell-Axioma Low Beta Index Fama-French factor exposures (February 2005-March 2014)

Figure 23: Russell-Axioma Low Beta Index style exposures (February 2005-March 2014)

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Russell-Axioma High Momentum Index

The investment rationale for the Russell-Axioma High Momentum Index is identical to that for the High Efficiency Momentum Index discussed above, so we will not repeat it here. Differences with HEMI are the use of an optimizer, and the basing of medium-term momentum on the cumulative return to the previous 250 trading days less the last 20 days.

Performance statistics

Table 11 summarizes past performance statistics of the High Momentum indexes for the short history we have. Volatilities of the indexes were about the same as those of the benchmark indexes, although maximum drawdowns were less. Relatively high active shares and concentrations made these indexes sharply focused. The sharpe and information ratios were modest. As with the Low Beta indexes, the High Momentum indexes may be more suitable for the total return investor, less so for the relative-return investor.

U.S. Large Cap U.S. Small CapHigh Momentum Return 9.0% 11.7%Benchmark Return 8.7% 10.3%High Momentum Volatility 15.8% 20.3%Benchmark Volatility 15.5% 20.0%High Momentum Sharpe Ratio 0.47 0.50Benchmark Sharpe Ratio 0.46 0.44Excess Return 0.3% 1.4%Tracking Error 5.4% 5.9%Information Ratio 0.06 0.23Active Share 64% 76%Momentum Max Drawdown -49.6% -53.5%Benchmark Max Drawdown -51.1% -59.2%High Momentum Effective Number 83 152Benchmark Effective Number 187 996

All figures are annualized and arithmetic, except for drawdown. The benchmarks are the Russell 1000 and Russell 2000®. High Momentum Index data before May 2011 is simulated. Holdings for active share and effective number are as of March 31, 2014.

Table 11: Russell-Axioma High Momentum Index performance (February 2005-March 2014)

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Factor and style exposures

In Figure 24 we can see that the value/growth exposures were neutral, as designed, and that there has been a strong momentum exposure. However, Figure 25 indicates a substantial growth bias. Recall from the analysis of HEMI that there was a correlation between growth and momentum that may lead to a misleading style exposure estimate.

Figure 24: Russell-Axioma Momentum Index Fama-French factor exposures (February 2005-March 2014)

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Figure 25: Russell-Axioma Momentum Index style exposures (February 2005-March 2014)

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Figure 26: Russell-Axioma Momentum Index style exposures with momentum factor (February 2005-March 2014)

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Russell-Axioma Low Volatility Index

The investment rationale for the Russell-Axioma Low Volatility Index is identical to that for the High Efficiency Low Volatility Index (HELVI) discussed above, so we will not repeat it here. A major difference in the methodologies is that volatility is defined as the annualized volatility computed from the preceding 252 days. The Axioma optimizer is used to control turnover and render correlations with other Axioma factors very small.

Performance statistics

Table 12 summarizes past performance statistics of the low-volatility indexes for the short history we have. These indexes were designed to reduce portfolio risk in a whole-portfolio context, not to enhance returns directly. The past performance statistics reflect that. The volatilities of the indexes were substantially below the volatilities of the benchmark indexes, as were the maximum drawdowns; this satisfies the primary design goal. Active shares were comparable to active mutual funds. Both indexes have relatively high concentrations. The Sharpe ratios show a meaningful improvement over benchmark, while the information ratios were close to zero. These indexes may be attractive to the total risk/return investor.

U.S. Large Cap U.S. Small CapLow Volatility Return 8.4% 10.4%Benchmark Return 8.7% 10.3%Low Vol Volatility 11.6% 15.7%Benchmark Volatility 15.5% 20.0%Low Vol Sharpe Ratio 0.59 0.56Benchmark Sharpe Ratio 0.46 0.44Excess Return -0.3% 0.1%Tracking Error 5.8% 6.2%Information Ratio -0.05 0.01Active Share 75% 85%Low Vol Max Drawdown -40.9% -36.7%Benchmark Max Drawdown -51.1% -59.2%Low Vol Effective Number 62 83Benchmark Effective Number 187 996

All figures are annualized and arithmetic, except for drawdown. The benchmarks are the Russell 1000 and Russell 2000. Low Volatility Index data before May 2011 is simulated. Holdings for active share and effective number are as of March 31, 2014.

Table 12: Russell-Axioma Low Volatility Index performance (February 2005-March 2014)

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Factor and style exposures

Figures 27 and 28 show that the Russell-Axioma Low Volatility Indexes achieved their aim of neutrality with respect to other factors. The value exposures in the Fama–French model were close to zero, while the value/growth style exposures were not large, with the estimated small cap growth tilt only barely statistically significant (Table A-24 in the Appendix). The large defensive exposure was just a function of the low-volatility factor and so was a product of the design.

Figure 27: Russell-Axioma Low Volatility Index Fama-French factor exposures (February 2005-March 2014)

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Figure 28: Russell-Axioma Low Volatility Index style exposures (February 2005-March 2014)

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So far we have discussed Russell’s smart beta indexes as if they were stand-alone strategies. But some of the most compelling use cases for smart beta indexes are in modifying the exposures of an existing portfolio or in combining several smart beta indexes within their own portfolio. Modifying the exposures of an existing portfolio is an exercise that is bespoke to the investor’s specific portfolio and beyond the scope of this paper. Instead, we will explore some simple combinations of smart beta indexes that may be of illustrative interest to investors and their advisors.

Combinations of Russell High Efficiency Factor Indexes (HEFI)

One of the motivations for using the same weighting methodology for all four HEFI indexes was to increase the ability to combine exposures into multi-factor portfolios. We will start out by looking at all four High Efficiency factor indexes together in a single portfolio. Greater insight can be found in doing the analysis in excess-return space, as the common market drivers can make the absolute correlations very high. Table 13 shows the correlations between the HEFI indexes in excess-return space for the Developed and U.S. Large Cap regions. There are a number of negative correlations, suggesting diversification opportunities through combining the indexes. The only positive correlations are between Momentum and Quality and between Value and Low Volatility, but they are still low enough to support combining them.

Combining smart beta indexes to improve diversification and shape portfolio risk

Part 2

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Ideally, the investor or their advisor would have an investment basis for weighting the four indexes a particular way. We will take the naïve route at this stage and simply equal weight them as an illustration. Eyeballing Figure 29 tells a lot of the diversification story. The simple average of the four HEFI indexes provides a much smoother ride by avoiding a lot of the timing risk that comes with any one index. When Momentum is underperforming, Value is typically outperforming, and vice versa. Likewise, when Low Volatility is outperforming, Quality is often underperforming, and vice versa.

Figure 29: High Efficiency Factor Indexes 1YR Rolling Excess Returns over the Russell 1000 (July 1996-March 2014)

U.S. Large CapLow Volatility Momentum Quality Value

Low Volatility 1.00Momentum -0.26 1.00Quality -0.12 0.47 1.00Value 0.57 -0.35 -0.30 1.00

Developed Large CapLow Volatility Momentum Quality Value

Low Volatility 1.00Momentum -0.04 1.00Quality -0.06 0.53 1.00Value 0.49 -0.26 -0.31 1.00

Benchmarks are the Russell 1000 and the Russell Developed Large Cap Index.

Table 13: High Efficiency Factor Index diversification opportunities through the lens of Correlations of Excess Returns (August 1996-March 2014)

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U.S. Large Cap Low Volatility Momentum Quality Value Equal Combination

Excess Return 0.53% 1.72% 1.37% 2.46% 1.52%Tracking Error 7.29% 5.08% 2.71% 5.95% 2.83%Information Ratio 0.07 0.34 0.51 0.41 0.54Developed Large Cap Low Volatility Momentum Quality Value Equal CombinationExcess Return 1.37% 2.09% 1.39% 2.98% 1.96%Tracking Error 7.33% 4.69% 2.74% 5.55% 2.95%Information Ratio 0.19 0.45 0.51 0.54 0.66

Benchmarks are the Russell 1000 and the Russell Developed Large Cap Index.

Table14: Relative return performance for High Efficiency Factor Indexes and an Equal-Weighted combination (August 1996-March 2014)

Table 14 shows the benchmark-relative performance statistics. The equal combination of the four-index excess returns is just a simple average of the four excess returns. But the low correlations would have worked to reduce the tracking error of the combination, leading to information ratios that would have been higher than any of the individual information ratios.

Figures 30 and 31 show that this simple blend of the four indexes would have had a value tilt. The anti-momentum tilt of the value index alone would have been offset by the other indexes in combination. The Fama–French and style models render conflicting conclusions on the cap tilts.

Figure 30: Equal combination of High Efficiency Low Volatility, Momentum, Quality and Value Indexes Fama-French factor exposures (July 1996-March 2014)

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While the simple combination of all four factors is an interesting illustration, one would hope to use a more thoughtful combination of the factors for actual investing. One combination that has recently appeared in the literature is a portfolio of quality, value and low-volatility factors. This strategy goes back to Benjamin Graham – arguably the father of value investing – whose premise was that investors should look for quality companies with sustainable earnings that also have good valuation metrics, such as earnings-to-price. The addition of the low-volatility factor to quality and value has recently been promoted as a simple replication of Warren Buffett’s approach to investing. A glance back at Table 13 suggests a two-factor combination of value and momentum, as they have had very low (negative) excess return correlations with each other. Recent research confirms that value and momentum strategies can be powerful complements across several asset classes, especially equities. The addition of the quality factor to the value and momentum pairing also has appeal, as quality has had low tracking error and low correlation with value.

Figure 31: Equal combination of High Efficiency Low Volatility, Momentum, Quality and Value Indexes style exposures (July 1996-March 2014)

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44 Jennifer Bender, Eric Brandhorst and Taie Wang (2014), “The Latest Wave in Advanced Beta: Combining Value, Low Volatility and Quality,” Journal of Index Investing, Summer.45 Benjamin Graham (1949), The Intelligent Investor, NY: Harper-Collins.46 Andrea Frazzini, David Kabiller and Lasse H. Pedersen (2012), “Buffett’s Alpha,” AQR Capital Management.47 Cliff Asness, Tobias Moskowitz and Lasse H. Pedersen (2013), “Value and Momentum Everywhere,” Journal of Finance, Vol. 68, June.

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How an investor might combine value, quality and low-volatility factor indexes into a “Buffett portfolio,” and the value and momentum indexes into a “value-plus-momentum portfolio” or the “value-plus-momentum-plus-quality portfolio,” will depend on the investor’s objectives and the circumstances of the rest of the portfolio. In the absence of that specific knowledge, we again assumed equal weighting between the indexes. Table 15 displays the benchmark-relative statistics of these three combinations, and it repeats the combination of all of the indexes for easy comparison. The absence of the diversifying momentum factor from the “Buffett portfolio” of value-plus-quality-plus-low volatility would have led to lower information ratios than those of combinations that included momentum. The value-plus-momentum-plus-quality combination would have achieved the highest information ratios. All of the combinations would have had modest tracking errors. Figures 32 and 33 visualize the trade-offs in relative risk/return space.

Value + Quality + Low Volatility Value + Momentum

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Quality

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VolatilityU.S. Large Cap

Excess Return 1.45% 2.09% 1.85% 1.52%Tracking Error 3.80% 3.15% 2.36% 2.83%Information Ratio 0.38 0.66 0.78 0.54

Developed Large CapExcess Return 1.91% 2.54% 2.15% 1.96%Tracking Error 3.65% 3.13% 2.37% 2.95%Information Ratio 0.52 0.81 0.91 0.66

Benchmarks are the Russell 1000 and the Russell Developed Large Cap Index.

Table 15: Combinations of High Efficiency Factor Indexes (August 1996-March 2014)

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Figure 32: Relative Risk/Return tradeoffs for combinations of High Efficiency Factor Indexes - U.S. Large Cap (July 1996-March 2014)

Figure 33: Relative Risk/Return tradeoffs for combinations of High Efficiency Factor Indexes - Developed Large Cap (July 1996-March 2014)

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Combining High Efficiency Factor Indexes with other smart beta indexes

Combining Fundamental and Equal Weight with Momentum

The HEFI series was designed to provide investors with precise tools for combining exposures in a portfolio. In this section we show how they can be used to enhance or reduce exposures in other smart beta indexes.

Both fundamental and equal-weight indexes have a substantial negative exposure to momentum, which is a side effect of its rebalancing back to stable price-indifferent weights. Some investors may see this anti-momentum bias as desirable, as it may aid in downside protection. But many other investors might see it as a potential drag on performance, and there is some research supporting that conclusion.

We combine the Fundamental Developed, Fundamental U.S. and Equal-Weight U.S. indexes with the High Efficiency Momentum Index matched to the respective region in a 75% / 25% mix. The thinking behind the mix is that 25% of momentum is enough to have an impact, but not so much as to overwhelm the fundamental or equal-weight strategy. Obviously, many other considerations might come into play before an investor makes an allocation in a real portfolio.

48 Goodwin (2014), “Passive and Fundamental Index Investing: A Factor Analysis” op. cit.

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Table 16 displays the performance statistics of the three momentum-combined indexes together with the statistics of the indexes without momentum. All of the combined indexes showed reductions in both total and excess returns compared to the indexes without momentum. However, the diversification benefit would have resulted in substantial reductions in tracking error compared to any of the indexes considered separately. This would have led to increased information ratios for the two Fundamental Index strategies, but not the Equal-Weight strategy.

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Large Cap

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Total Return 11.4% 11.3% 12.3% 12.1% 12.4% 11.0%

Benchmark Return 8.8% 8.8% 9.5% 9.5% 5.6% 5.6%

Total Volatility 15.6% 15.6% 15.6% 15.5% 18.1% 17.5%

Benchmark Volatility 16.2% 16.2% 16.2% 16.2% 15.8% 15.8%

Sharpe Ratio 0.57 0.56 0.63 0.62 0.58 0.52

Benchmark Sharpe Ratio 0.39 0.39 0.44 0.44 0.23 0.23

Excess Return 2.6% 2.4% 2.8% 2.5% 6.8% 5.4%

Tracking Error 4.4% 3.2% 5.6% 3.8% 5.8% 4.6%

Information Ratio 0.58 0.77 0.51 0.67 1.18 1.18

Max Drawdown -51.5% -51.9% -50.8% -50.1% -50.8% -50.4%

Benchmark Maximum Drawdown -54.0% -54.0% -51.1% -51.1% -51.1% -51.1%

Benchmarks are the Russell Developed All-Cap Index for the Fundamental Developed All-Company Index, the Russell 3000 for the Fundamental U.S. All-Company Index and the Russell 1000 for the Equal-Weighted U.S. Large Cap Index.

Table 16: Combinations of Fundamental and Equal-Weighted indexes with the High Efficiency Momentum Index(FDM: July 1996-March 2014) (EW: February 2000-March 2014)

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Figures 34, 35 and 36 show the Fama-French factor exposures for the three indexes, both with and without momentum. All would have shown a meaningful reduction in the negative exposure to the WML factor. None of the remaining momentum exposures would have been statistically significant (see Table A-27). There would have been a small reduction in exposures to the value factor.

Figure 34: Fundamental Developed Combined with High Efficiency Momentum Fama-French factor exposures (July 1996-March 2014)

0.8

0.6

0.4

0.2

0

-0.2

Small Cap (SMB) Value (HML) Momentum (WML)-0.4

75% FDM Developed + 25% Momentum FDM Developed

Exp

osur

es

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Figure 35: Fundamental U.S. combined with High Efficiency Momentum Fama-French factor exposures (July 1996-March 2014)

Figure 36: Equal-Weighted U.S. combined with High Efficiency Momentum Fama-French factor exposures (February 2000-March 2014)

0.8

0.8

0.6

0.6

0.4

0.4

0.2

0.2

0

0

-0.2

-0.2

Small Cap (SMB)

Small Cap (SMB)

Value (HML)

Value (HML)

Momentum (WML)

Momentum (WML)

-0.4

-0.4

75% FDM US + 25% Momentum

75% EW US + 25% Momentum

FDM US

EW US

Exp

osur

esE

xpos

ures

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Combining Equal Weight with Low Volatility

The performance statistics in Table 16 do not show a great improvement in the Equal Weight Index performance with the addition of a sleeve of momentum. For one thing, the high total volatility of the Equal Weight Index would have remained. So as another experiment, we combined Equal Weight indexes with a 25% sleeve of the High Efficiency Low Volatility Index.

Table 17 shows that adding a 25% sleeve of High Efficiency Low Volatility to the U.S. and global Equal Weight Index would have led to a modest reduction in total returns. But total volatility would have been reduced to the neighborhood of the benchmark volatilities. As well, there would have been a substantial reduction in tracking errors, leading to increased information ratios. And the maximum drawdowns would have been reduced. So for the volatility-averse investor, this may have been an attractive path to getting exposure to an equal weight strategy.

Equal-Weighted U.S. Large Cap

Equal-Weighted + Low Volatility U.S. Large Cap

(75/25)Equal-Weighted Global

Large Cap

Equal-Weighted + Low Volatility Global Large Cap

(75/25)

Total Return 12.4% 11.4% 12.5% 11.8%

Benchmark Return 5.6% 5.6% 7.9% 7.9%

Total Volatility 18.1% 16.3% 18.6% 17.0%

Benchmark Volatility 15.8% 15.8% 16.8% 16.8%

Sharpe Ratio 0.58 0.58 0.59 0.61

Benchmark Sharpe Ratio 0.23 0.23 0.38 0.38

Excess Return 6.8% 5.8% 4.6% 3.9%

Tracking Error 5.8% 4.6% 4.1% 3.0%

Information Ratio 1.18 1.26 1.13 1.32

Total Max Drawdown -50.8% -48.7% -53.8% -52.0%

Benchmark Max Drawdown -51.1% -51.1% -54.9% -54.9%

All figures are annualized and arithmetic, except max drawdown. The benchmarks are the Russell Global Large Cap Index and Russell 1000.

Table 17: Combinations of Equal-Weighted with High Efficiency Low Volatility Indexes (U.S.: February 2000-March 2014) (Global: August 2001-March 2014)

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Combining Geographic Exposure with Value

As a final experiment, we look back at Figures 20 and 21, which show that GeoExposure Indexes had strong tilts to the growth factor. Some investors might have wanted the indirect exposure to Emerging Markets but not the growth tilt in Developed Markets that came with it. Would it have been possible to combine the High Efficiency Value Index with the GeoExposure Index to produce a portfolio with less growth that still had strong exposure to Emerging Markets? Also, would combining the two indexes have had diversification benefits? To find out, we made a simple 75/25 combination of the two indexes for both the U.S. and Developed regions.

The figures in Table 18 show that combining GeoExposure and the High Efficiency Value Index would have resulted in modest increases in both total and excess returns. Total volatility and maximum drawdowns would have been modestly reduced, or would have stayed the same. Diversification would have worked to lower tracking error substantially. The net result would have been that information ratios would have increased in both the U.S. and Developed regions. The style exposures in Figures 37 and 38 demonstrate that the strong factor capture of the High Efficiency Value Indexes would have meaningfully offset the growth exposure of the GeoExposure Indexes. The other style exposures were only modestly affected. Fama-French factor exposures (not shown) were very similar.

So far, so good, but what would have happened to the indirect exposure to Emerging Markets through the GeoExposure Indexes? Would that have been reduced? One way to measure that is through a two-factor regression, with one factor being the parent or benchmark index and the other factor being the difference between the Emerging Markets index and the benchmark index. The idea is that the coefficient on the difference between the Emerging Markets index and the benchmark index – or the return premium – measures the exposure to Emerging Markets over the exposure already contained in the benchmark index. The regression results are shown in Table 19. The estimated exposures for the GeoExposure Indexes combined with the High Efficiency Value Indexes would have shown a modest reduction: from 0.17 to 0.13 for the U.S., and from 0.24 to 0.21 for Developed. The reduced exposures would still have been statistically significant, however.

49 Goodwin and Paris (2013) op. cit.

49

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GeoExposure Developed Large Cap

GeoExposure Developed and Value (75/25)

GeoExposure U.S. Large Cap

GeoExposure U.S. and Value (75/25)

Total Return 8.7% 9.0% 8.4% 8.5%Benchmark Return 8.2% 8.2% 7.3% 7.3%Emerging Markets Return 5.1% 5.1% 5.8% 5.8%

Total Volatility 23.7% 23.3% 20.5% 20.0%Benchmark Volatility 19.7% 19.7% 17.6% 17.6%Emerging Market Volatility 26.5% 26.5% 26.8% 26.8%

Sharpe Ratio 0.36 0.38 0.38 0.39Benchmark Sharpe Ratio 0.41 0.41 0.38 0.38

Excess Return 0.5% 0.8% 1.1% 1.2%Tracking Error 5.4% 4.7% 5.4% 4.1%Information Ratio 0.09 0.17 0.20 0.28Max Drawdown -50.6% -49.9% -52.1% -52.0%Benchmark Max Drawdown -44.5% -44.5% -51.1% -51.1%

Emerging Markets Max Drawdown -52.1% -52.1% -62.3% -62.3%

Benchmarks are the Russell Developed Large-Cap Index and the Russell 1000. Date ranges are July 2008-March 2014 for Developed and July 2007-March 2014 for the U.S.

Table 18: Combinations of High Efficiency Value with GeoExposure Indexes (July 2008-March 2014) ( U.S.: July 2007-March 2014)

To sum up, combining GeoExposure Indexes with High Efficiency Value Indexes on a 75/25 basis would have succeeded in meaningfully offsetting the growth bias in GeoExposure. There were also diversification benefits that would have led to higher information ratios. There would have been only a modest reduction in indirect Emerging Markets exposure. To eliminate the growth tilt entirely, something like a 50/50 combination would have been necessary. But the trade-off is that it would have further reduced the exposure to Emerging Markets.

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Figure 37: GeoExposure U.S. combined with High Efficiency Value style exposures (July 2007-March 2014)

0.8

0.6

0.4

0.2

0

-0.2

Small - Large Cap Value - Growth Defensive - Dynamic-0.4

75% GeoExp + 25% Value Geographic Exposure

Exp

osur

es

Figure 38: GeoExposure Developed combined with High Efficiency Value style exposures (July 2007-March 2014)

0.8

0.6

0.4

0.2

0

-0.2

-0.4

75% GeoExp + 25% Value Geographic Exposure

Exp

osur

es

Small - Large Cap Value - Growth Defensive - Dynamic

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Exposure to Benchmark IndexEmerging Markets Premium

(EM-Benchmark Index)GeoExposure U.S. 1.09 0.17

(30.33) (5.37)75% GeoExposure + 25% Value U.S. 1.09 0.13

(50.59) (6.19)GeoExposure Developed 1.13 0.24

(41.39) (6.03)75% GeoExposure + 25% Value Developed 1.12 0.21

(64.31) (6.79)Benchmarks are the Russell Developed Large Cap Index and the Russell 1000.

Table 19: Two-factor regressions measuring Emerging Markets Exposure in GeoExposure and GeoExposure + High Efficiency Value Indexes (U.S. July 2008-March 2014); (Global July 2007-March 2014)

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PART 3 – CONCLUSION: SMART BETA IS A MAJOR EXPANSION OF THE INVESTOR TOOLBOX

This tour of 12 smart beta indexes has demonstrated the diversity of strategies now at the investor’s fingertips. These strategies allow the investor to obtain focused exposures, each guided by specific investment ideas. They represent a major expansion of the investor toolbox.

Here are two important considerations for the investor considering an allocation to smart beta indexes:

In Part 2 we demonstrated how a few of the smart beta indexes might be usefully combined with each other, but ultimately the real question is how they might affect the investor’s overall strategy. Russell Indexes research will continue to explore the integration of this new set of tools with passive and active portfolios, and with outcome-oriented objectives. The continued adoption of these tools will help to increase transparency, reduce costs and sharpen implementation in portfolio construction.

Suitability of the index objectives. Some of the smart beta indexes are “active-risk-aware,” meaning that they are constructed with cap-weighted benchmark sensitivity in mind. Other indexes have been constructed without any consideration given to benchmark-relative measures such as tracking error. Investors need to be clear as to how benchmark-sensitive their objectives are in thinking about the right mix of smart beta indexes. Some investors are sensitive to both total volatility and tracking error; managing both total and relative risk budgets simultaneously inevitably involves trade-offs that need to be carefully considered.

Knowledge of and commitment to the investment proposition. All investment strategies have periods of underperformance, and that goes for smart beta indexes based on systematic strategies. It is important for the investor to be well educated about the strategy and have an understanding of where in a market cycle the strategy is likely to outperform and underperform. Without that knowledge, there will be a temptation to exit the strategy at the first sign of underperformance, which is often the most harmful sort of market timing.

Conclusion: Smart Beta is a major expansion of the investor toolbox

Part 3

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APPENDIX – PERFORMANCE MEASURES AND FACTOR MODELS

Appendix – Performance measures and factor models

Effective Number of Stocks

The Effective Number of Stocks (ENS) is defined as the reciprocal of the Herfindahl-Hirschman Index, which is the sum of the squared weights. The Herfindahl-Hirschman Index was originally designed to measure the concentration of an industry for anti-trust purposes. The formula is:

N is the number of stocks and is the squared weight of the i-th stock in the index. ENS will range from 1 (concentrated in one stock) to N (equal weighted). Active Share

Active Share (AS) is the percentage of a portfolio that differs from a cap-weighted benchmark index. The measure incorporates both the overweights and underweights:

N is the number of stocks and | | is the absolute difference between the weights in the smart beta index and the benchmark. Note that this number will be positive, whether the smart beta weight is an underweight or an overweight. AS will range from a minimum of 0% (holdings and weights identical to the benchmark) to a maximum of 100% (all off-benchmark holdings).

The Fama-French model

Fama and French extended the single-factor CAPM of Sharpe to include factors for both value and small cap. Carhart showed that a fourth factor, momentum, was an important explanation of stock returns as well. The model is:

rf : The “risk-free” rate of financial theory, proxied by the one-month U.S. T-Bill. BroadMarket : Is a broad-market cap-weighted benchmark return. French uses CRSP indexes.

SMB : “Small minus Big” is the median return to a portfolio of small cap stocks minus the median return to a portfolio of large cap stocks, and so is an estimate of how the market rewards a tilt to small cap stocks.

Ni = 1 w 2

iENS = 1 / ∑

AS = ∑12

w

w

w

wN

i = 1

smart beta, i

smart beta, i

benchmark, i

benchmark, i

50 Eugene Fama and Kenneth French (1992), “The Cross-Section of Expected Stock Returns,” Journal of Finance, Vol. 47.51 William Sharpe (1964), “Capital Asset Prices: A Theory of Market Equilibrium Under Conditions of Risk,” Journal of Finance, Vol. 19.52 Mark Carhart (1997) op. cit.

50 51

52

SBIndex-rf = a + b ∙(BroadMarket-rf) + c ∙ SMB + d ∙ HML + e ∙WML + residual

w 2i

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HML : “High minus Low” is the median return to a portfolio of stocks with high book-to-price ratios minus the median return to a portfolio of low book-to-price ratio stocks. This is an estimate of how the market rewards tilt to value stocks as measured by book-to-price.

WML : “Winners minus Losers” is the median return of a portfolio of stocks with the highest returns over the previous 12 months (lagged one month) minus the median return to a portfolio with the lowest returns over the previous 12 months (lagged one month). This is an estimate of how the market rewards a tilt to momentum.

The coefficients b, c, d and e measure the exposures of the index to each factor. The intercept of the equation, a, is a systematic return that cannot be explained by the four factors. All four factors are derived by use of cap-weighted methodologies, so we expect that these factors cannot capture all of the return variation in non-cap-weighted indexes.

A style exposure model

At Russell, we often refer to Russell style indexes as “Smart Beta 1.0,” because they were the first to provide investors with conscious exposures away from a broad-market cap-weighted benchmark. However, it is important to recognize that there are sharp distinctions between traditional style indexes and the current crop of smart beta indexes. Russell’s style indexes are strongly tethered to broad-market cap-weighted indexes in several ways. First, valuation (value/growth) and stability (defensive/dynamic) are symmetrical, meaning value is a mirror image of growth and defensive is a mirror image of dynamic. Put another way, an equal number of dollars invested on each side of a style is style-neutral. Put yet another way, the three dimensions of style – cap size, valuation and stability – are modular: each pair of style indexes contains all of the stocks in the broad-market cap-weighted parent index and rolls up to it without gaps or overlaps.

Second, style indexes are constructed by using individual stock characteristics to sort stocks into one side or the other of the style divide, resulting in a subset of stocks from the parent index in each style index. The resulting subsets of stocks are then cap-weighted. The cap-weighting at the final step preserves the symmetrical and “roll-up” properties, but attenuates the factor exposures to a moderate “tilt.” These properties allow style indexes to serve as both investment vehicles and style benchmarks.

Smart beta indexes are not cap-weighted and are not designed to have a mirror-image counterpart, but rather to deliver full-throated exposures to a single factor or systematic strategy in one direction only. They are meant to be

53 David Koenig (2014), “Styles vs. Factors: What They Are, How They’re Similar/Different and How They Fit Within Portfolios,” Russell Index Insights, June.

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investment vehicles and are not appropriate as benchmarks in general.In the spirit of the Fama-French model, the style benchmark returns are constructed as long/short returns to the three dimensions of style: cap size, valuation and stability. The model places any index at its appropriate point in the “style cube.” The model is:

SBIndex-rf=α+β∙(ParentIndex-rf)+γ∙SCMLC+δ∙VMG+ε∙DeMDy+residual

rf : The “risk-free” rate of financial theory, proxied by the one-month U.S. T-Bill.

ParentIndex : Is the Russell broad-market cap-weighted parent index return, from which the style benchmarks are drawn.

The U.S. Parent Index : Russell 3000

The Europe Parent Index : Russell Developed Europe All-Cap

The Developed Parent Index : Russell Developed All-Cap

The Emerging Parent Index : Russell Emerging Markets All-Cap

The Global Parent Index : Russell Global Index All-Cap (RGI)

SCMLC : “Small Cap minus Large Cap” is the Russell Small Cap – Large Cap style index return.

VMG : “Value minus Growth” is the Russell All-Cap Value – All-Cap Growth style index return.

DeMDy : “Defensive minus Dynamic” is the Russell All-Cap Defensive – All-Cap Dynamic style index return.

The coefficients are the estimated style exposures, as with the Fama-French model. That the two models may not produce the same results for any particular index can be seen by comparing the methodologies of the value factor returns. The Fama-French value factor return HML is calculated as the median return to the stocks in the highest quantile of book-to-market ratios less the median return to the lowest quantile. While it is well established in research that a high book-to-market ratio is a powerful value characteristic, not as well established is how much a low book-to-market ratio, alone, is a valid growth characteristic. The Russell Value and Growth indexes use book-to-market as their value characteristic, but also include two growth characteristics: two-year forecasted earnings growth and five-year historical sales growth. Thus, while a negative exposure to the Fama-French HML factor return suggests “not value,” a negative exposure to the Russell VMG is more clearly a positive growth exposure.

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There are important differences between the two models in the way capitalization returns are measured as well. The Fama–French small cap return SMB is the difference between the median return of stocks in the lowest capitalization quantile and the highest quantile. The Russell cap return SCMLC is the difference between the cap-weighted return to the (approximately) bottom 10% of stocks by capitalization and the cap-weighted return to the top 90%. The results show that the most conflicts between the two models were in estimating cap exposures.

Universe Intercept Broad Market Small - Big Cap High - Low B/PWinners - Losers

(Momentum) RSQU.S. -0.50% 0.99 -0.15 0.39 -0.04 0.97

(-1.12) (81.27) (-6.34) (13.75) (-2.13)Europe 1.14% 0.96 -0.11 0.34 -0.06 0.95

(1.83) (62.14) (-4.60) (8.35) (-3.06)Developed 1.07% 0.72 -0.20 0.38 0.02 0.90

(0.95) (32.50) (-4.64) (7.48) (0.66)

Universe Intercept Parent IndexSmall Cap – Large Cap Value – Growth

Defensive – Dynamic RSQ

U.S. 0.74% 0.93 -0.01 0.25 0.39 0.97(1.51) (52.29) (-0.27) (8.42) (11.50)

Europe -0.22% 0.97 -0.11 0.07 0.52 0.98(-0.36) (69.65) (-4.60) (3.30) (26.29)

Developed 0.25% 0.98 0.08 0.19 0.58 0.98(0.49) (54.24) (3.20) (6.33) (21.24)

Emerging 1.07% 0.86 -0.11 0.01 0.25 0.95(0.90) (33.50) (-3.41) (0.16) (3.22)

Global 0.25% 0.98 0.06 0.18 0.58 0.98(0.46) (53.27) (2.35) (6.30) (20.48)

Figures in parentheses are t-ratios calculated using Newey–West robust standard errors. Intercepts are annualized.

Figures in parentheses are t-ratios calculated using Newey–West robust standard errors. Intercepts are annualized.

Table A-1: Fama-French exposures of High Efficiency Low Volatility Indexes (July 1996-March 2014)

Table A-2: Style exposures of High Efficiency Low Volatility Indexes (July 1996-March 2014)

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Universe Intercept Broad Market Small - Big Cap High - Low B/PWinners - Losers

(Momentum) RSQU.S. 0.24% 1.07 -0.02 0.01 0.17 0.97

(0.29) (43.07) (-0.48) (0.19) (8.14)Europe -0.56% 1.02 0.03 -0.02 0.23 0.97

(-0.79) (56.03) (0.42) (-1.08) (10.58)Developed -0.05% 1.06 -0.01 0.02 0.23 0.98

(0.95) (57.73) (-0.35) (0.51) (11.08)

Figures in parentheses are t-ratios calculated using Newey–West robust standard errors. Intercepts are annualized.

Figures in parentheses are t-ratios calculated using Newey–West robust standard errors. Intercepts are annualized.

Table A-3: Fama-French exposures of High Efficiency Momentum Indexes (July 1996-March 2014)

Universe Intercept Parent IndexSmall Cap – Large Cap Value – Growth

Defensive – Dynamic RSQ

U.S. 1.99% 0.97 0.09 -0.16 0.002 0.93(2.03) (36.26) (1.95) (-3.66) (0.02)

U.S. with WML 0.63% 1.00 -0.01 -0.02 -0.16 0.97

(0.94) (51.99) (-0.28) (-0.74) (-2.78)Europe 1.92% 1.04 0.14 -0.25 0.19 0.96

(1.94) (39.98) (3.32) (-3.95) (3.88)Europe with WML -0.89% 1.04 0.09 -0.07 0.04 0.99

(-1.34) (72.91) (3.17) (-3.24) (1.98)Developed 1.44% 1.08 0.23 -0.29 0.26 0.95

(1.59) (41.04) (4.26) (-5.54) (3.55)Dev. with WML -0.33% 1.07 0.09 -0.05 0.05 0.98

(-0.59) (50.38) (2.20) (-1.53) (1.07)Emerging 3.01% 0.97 -0.22 -0.12 -0.06 0.97

(2.52) (41.49) (-3.81) (-2.76) (-0.87)Global 1.72% 1.10 0.26 -0.32 0.29 0.95

(1.87) (39.76) (4.72) (-5.85) (3.82)

Table A-4: Style exposures of High Efficiency Momentum Indexes (July 1996-March 2014)

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Universe Intercept Broad Market Small - Big Cap High - Low B/PWinners - Losers

(Momentum) RSQU.S. 1.85% 0.95 -0.04 -0.06 0.01 0.98

(3.31) (93.44) (-1.92) (-3.37) (1.01)Europe 1.70% 0.92 0.02 -0.12 0.03 0.97

(2.13) (48.32) (0.59) (-3.71) (1.32)Developed 2.36% 0.97 -0.05 -0.14 0.03 0.98

(3.67) (91.97) (-1.69) (-6.51) (2.22)

Figures in parentheses are t-ratios calculated using Newey–West robust standard errors. Intercepts are annualized.

Table A-5: Fama-French exposures of High Efficiency Quality Indexes (July 1996-March 2014)

Universe Intercept Parent IndexSmall Cap – Large Cap Value – Growth

Defensive – Dynamic RSQ

U.S. 1.75% 0.97 0.05 -0.16 0.10 0.98(3.83) (80.34) (2.00) (-10.24) (3.17)

Europe 0.81% 0.99 0.10 -0.19 0.20 0.98(1.60) (70.31) (4.40) (-9.16) (8.04)

Developed 1.48% 1.00 0.09 -0.24 0.13 0.99(3.46) (77.98) (3.56) (-12.70) (5.56)

Emerging 1.29% 0.99 -0.14 -0.14 0.07 0.99(2.19) (64.75) (6.07) (-5.95) (1.23)

Global 1.57% 1.01 0.12 -0.25 0.14 0.99(3.19) (71.69) (4.10) (-10.83) (5.30)

Universe Intercept Broad Market Small - Big Cap High - Low B/PWinners - Losers

(Momentum) RSQU.S. 2.03% 0.96 -0.06 0.41 -0.12 0.96

(2.20) (43.48) (-1.55) (11.65) (-5.03)Europe 1.13% 0.99 0.05 0.32 -0.13 0.98

(1.26) (81.47) (1.83) (11.45) (-3.70)Developed 1.62% 0.99 0.04 0.47 -0.11 0.97

(1.62) (69.50) (0.94) (14.97) (-2.79)

Figures in parentheses are t-ratios calculated using Newey–West robust standard errors. Intercepts are annualized.

Figures in parentheses are t-ratios calculated using Newey–West robust standard errors. Intercepts are annualized.

Table A-6: Style exposures of High Efficiency Quality Indexes (July 1996-March 2014)

Table A-7: Fama–French exposures of High Efficiency Value Indexes (July 1996-March 2014)

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Universe Intercept Parent IndexSmall Cap – Large Cap Value – Growth

Defensive – Dynamic RSQ

U.S. 0.74% 0.93 -0.01 0.25 0.39 0.97(1.51) (52.29) (-0.27) (8.42) (11.50)

Europe 0.58% 1.03 0.06 0.43 0.05 0.99(0.87) (77.18) (2.31) (10.24) (0.97)

Developed 1.88% 1.04 0.08 0.53 0.01 0.97(2.05) (45.64) (1.80) (9.44) (0.16)

Emerging 1.71% 0.99 -0.04 0.12 -0.06 0.97(1.48) (33.95) (-0.87) (2.17) (-1.00)

Global 1.94% 1.06 0.15 0.50 0.01 0.98(1.83) (39.26) (3.20) (8.65) (0.12)

Universe Intercept Broad Market Small - Big Cap High - Low B/PWinners - Losers

(Momentum) RSQU.S. 2.15% 0.78 -0.20 0.21 0.01 0.93

(2.66) (47.89) (-6.23) (6.33) (0.45)Europe 2.05% 0.83 0.003 -0.09 0.13 0.95

(1.66) (59.15) (0.04) (-1.70) (3.53)Developed 2.90% 0.77 -0.17 0.06 0.07 0.96

(3.27) (45.54) (-5.75) (1.12) (2.64)

Universe Intercept Parent IndexSmall Cap – Large Cap Value – Growth

Defensive – Dynamic RSQ

U.S. 0.98% 0.96 0.01 0.11 0.41 0.98(2.24) (72.79) (0.41) (5.11) (18.72)

Europe 1.33% 0.97 0.06 -0.02 0.47 0.99(2.59) (105.48) (2.88) (-0.83) (20.46)

Developed 1.50% 0.95 0.05 -0.03 0.47 0.99(4.76) (99.08) (3.48) (-1.20) (28.31)

Emerging 2.09% 0.95 -0.24 -0.04 0.48 0.99(3.85) (77.73) (-7.89) (-1.58) (16.80)

Figures in parentheses are t-ratios calculated using Newey–West robust standard errors. Intercepts are annualized.

Figures in parentheses are t-ratios calculated using Newey–West robust standard errors. Intercepts are annualized.

Figures in parentheses are t-ratios calculated using Newey–West robust standard errors. Intercepts are annualized.

Table A-8:Style exposures of High Efficiency Value Indexes (July 1996-March 2014)

Table A-9: Fama-French exposures of High Efficiency Defensive Indexes (August 2001-March 2014)

Table A-10: Style exposures of High Efficiency Defensive Indexes (August 2001-March 2014)

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APPENDIX – PERFORMANCE MEASURES AND FACTOR MODELS

Universe Intercept Broad Market Small - Big Cap High - Low B/PWinners - Losers

(Momentum) RSQU.S.All-Company 2.41% 0.97 -0.02 0.43 -0.08 0.97

(3.33) (50.71) (-0.59) (10.01) (-4.26)Europe All-Company 2.45% 0.99 0.07 0.21 -0.07 0.97

(2.99) (45.09) (1.83) (6.03) (-4.22)Developed All-Company 1.68% 0.95 0.06 0.37 -0.07 0.98

(3.10) (72.64) (2.24) (12.01) (-4.87)

Universe Intercept Parent IndexSmall Cap – Large Cap Value – Growth

Defensive – Dynamic RSQ

U.S.All-Company 2.22% 1.00 0.05 0.42 0.04 0.97

(2.89) (51.16) (2.30) (17.25) (1.05)Europe All-Company 2.33% 0.98 0.11 0.31 0.03 0.98

(3.16) (40.91) (2.43) (12.47) (1.36)Developed All-Company 1.91% 0.99 0.12 0.37 0.05 0.98

(2.82) (46.89) (3.62) (13.74) (1.41)Emerging Large-Company 4.78% 0.93 0.001 0.30 -0.21 0.95

(2.60) (38.29) (0.03) (5.41) (-2.37)

Universe Intercept Broad Market Small - Big Cap High - Low B/PWinners - Losers

(Momentum) RSQU.S. Large Cap 4.98% 1.00 0.11 0.25 -0.13 0.93

(3.64) (24.77) (1.90) (4.09) (-3.29)U.S. Mid-Cap 5.68% 1.01 0.18 0.28 -0.14 0.92

(3.62) (21.72) (2.84) (4.20) (-3.04)U.S. Small Cap 0.47% 1.01 0.73 0.37 -0.11 0.95

(0.48) (36.38) (11.78) (8.10) (-3.47)

Figures in parentheses are t-ratios calculated using Newey–West robust standard errors. Intercepts are annualized.

Figures in parentheses are t-ratios calculated using Newey–West robust standard errors. Intercepts are annualized.

Figures in parentheses are t-ratios calculated using Newey–West robust standard errors. Intercepts are annualized.

Table A-11: Fama–French exposures of Fundamental Index Strategies (August 1996-March 2014)

Table A-12: Style exposures of Fundamental Index Strategies (August 2001-March 2014)

Table A-13: Fama-French exposures of Equal-Weight Indexes (February 2002-March 2014)

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APPENDIX – PERFORMANCE MEASURES AND FACTOR MODELS

Universe Intercept Parent IndexSmall Cap – Large Cap Value – Growth

Defensive – Dynamic RSQ

U.S. Large Cap 5.56% 0.98 0.07 0.29 -0.25 0.94(4.14) (24.59) (1.20) (4.46) (-3.53)

U.S. Mid Cap 6.55% 0.99 0.13 0.31 -0.28 0.93(4.26) (21.49) (1.88) (4.50) (-3.48)

U.S. Small Cap 1.99% 1.02 0.78 0.21 -0.15 0.97(2.01) (36.23) (11.62) (3.82) (-2.59)

Global Large Cap 3.05% 0.99 0.38 0.003 -0.12 0.98(3.22) (44.64) (12.39) (0.05) (-1.89)

Universe Intercept Broad Market Small - Big Cap High - Low B/PWinners - Losers

(Momentum) RSQU.S. Large Cap 3.61% 0.74 -0.13 0.45 0.01 0.80

(2.04) (16.52) (-1.74) (7.08) (0.16)U.S. Small Cap 0.14% 0.68 0.58 0.57 0.03 0.81

(0.07) (13.79) (11.11) (5.51) (0.69)

Figures in parentheses are t-ratios calculated using Newey–West robust standard errors. Intercepts are annualized.

Figures in parentheses are t-ratios calculated using Newey–West robust standard errors. Intercepts are annualized.

Table A-14: Style exposures of Equal-Weight Indexes (U.S.: February 2000-March 2014); (Global: August 2001-March 2014)

Table A-15: Fama-French exposures of High Dividend Yield Indexes (July 1996-March 2014)

Universe Intercept Parent IndexSmall Cap – Large Cap Value – Growth

Defensive – Dynamic RSQ

U.S. Large Cap 2.62% 0.92 0.05 0.52 0.34 0.85(1.93) (25.16) (1.18) (7.47) (3.54)

U.S. Small Cap 0.16% 0.92 0.86 0.43 0.51 0.88(0.10) (17.78) (12.90) (4.46) (5.51)

Figures in parentheses are t-ratios calculated using Newey–West robust standard errors. Intercepts are annualized.

Table A-16: Style exposures of High Dividend Yield Indexes (July 1996-March 2014)

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APPENDIX – PERFORMANCE MEASURES AND FACTOR MODELS

Universe Intercept Broad Market Small - Big Cap High - Low B/PWinners - Losers

(Momentum) RSQDeveloped Large Cap -0.81% 1.18 0.12 -0.19 -0.07 0.98

(-0.55) (45.49) (1.93) (-1.89) (-2.37)Europe Large Cap -0.12% 1.15 0.05 -0.25 -0.03 0.98

(-0.06) (38.54) (0.59) (-3.31) (-0.61)U.S.Large Cap -0.82% 1.17 0.01 -0.25 -0.15 0.95

(-0.38) (34.59) (0.17) (-2.58) (-0.38)

Universe Intercept Parent IndexSmall Cap – Large Cap Value – Growth

Defensive – Dynamic RSQ

DevelopedLarge Cap 0.41% 1.05 -0.05 -0.32 -0.37 0.99

(0.27) (32.47) (-0.75) (-5.13) (-4.77)EuropeLarge Cap 1.56% 1.04 -0.14 -0.41 -0.27 0.99

(1.22) (39.23) (-1.79) (-5.16) (-4.40)U.S.Large Cap -0.18% 1.02 -0.16 -0.40 -0.31 0.97

(-0.12) (26.82) (-2.66) (-4.79) (-4.53)

Figures in parentheses are t-ratios calculated using Newey–West robust standard errors. Intercepts are annualized.

Figures in parentheses are t-ratios calculated using Newey–West robust standard errors. Intercepts are annualized.

Table A-17: Fama-French exposures of GeoExposure Indexes (July 2008-March 2014) (U.S.: July 2007-March 2014)

Table A-18: Style exposures of GeoExposure Indexes (July 2008-March 2014) (U.S.: July 2007-March 2014)

Universe Intercept Broad Market Small - Big Cap High - Low B/PWinners - Losers

(Momentum) RSQU.S. Large Cap 2.27% 0.78 -0.14 -0.03 0.08 0.89

(2.02) (24.87) (-2.39) (-0.34) (3.50)U.S. Small Cap -1.54% 0.85 0.56 0.02 0.13 0.93

(-0.96) (17.52) (9.86) (0.32) (4.48)

Figures in parentheses are t-ratios calculated using Newey–West robust standard errors. Intercepts are annualized.

Table A-19: Fama-French exposures of Russell-Axioma Low Beta Indexes (February 2005-March 2014)

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APPENDIX – PERFORMANCE MEASURES AND FACTOR MODELS

Universe Intercept Parent IndexSmall Cap – Large Cap Value – Growth

Defensive – Dynamic RSQ

U.S. Large Cap 1.76% 0.89 -0.02 -0.15 0.38 0.91

(1.66) (23.89) (-0.45) (-1.62) (4.34)U.S. Small Cap -1.08% 0.88 0.66 -0.22 0.24 0.92

(-0.64) (18.89) (8.82) (-2.10) (1.86)

Universe Intercept Broad Market Small - Big Cap High - Low B/PWinners - Losers

(Momentum) RSQU.S. Large Cap -0.38% 1.09 -0.10 -0.03 0.26 0.96

(-0.37) (39.27) (-2.29) (-0.60) (10.03)U.S. Small Cap -0.32% 1.12 0.83 0.01 0.29 0.97

(-0.23) (28.40) (17.19) (0.15) (16.52)

Universe Intercept Parent IndexSmall Cap – Large Cap Value – Growth Defensive – Dynamic RSQ

U.S. Large Cap 0.09% 1.00 -0.08 -0.29 0.07 0.90

(0.05) (16.71) (-0.95) (-2.80) (0.34)U.S. LC with WML 0.04% 1.00 -0.17 -0.02 -0.21 0.97

(0.05) (42.49) (-3.65) (-3.04) (-3.03)U.S. Small Cap 0.78% 1.07 0.93 -0.40 0.19 0.93

(0.33) (18.70) (8.50) (-3.50) (0.93)U.S. SC with WML 0.73% 1.06 0.84 -0.12 -0.09 0.97

(0.66) (30.98) (13.29) (-2.24) (1.45)

Figures in parentheses are t-ratios calculated using Newey–West robust standard errors. Intercepts are annualized.

Figures in parentheses are t-ratios calculated using Newey–West robust standard errors. Intercepts are annualized.

Figures in parentheses are t-ratios calculated using Newey–West robust standard errors. Intercepts are annualized.

Table A-20: Style exposures of Russell-Axioma Low Beta Indexes (February 2005-March 2013)

Table A-21: Fama-French exposures of Russell-Axioma Momentum Indexes (February 2005-March 2014)

Table A-22: Style exposures of Russell-Axioma Momentum Indexes (February 2005-March 2014)

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APPENDIX – PERFORMANCE MEASURES AND FACTOR MODELS

Universe Intercept Broad Market Small - Big Cap High - Low B/PWinners - Losers

(Momentum) RSQU.S. Large Cap 1.55% 0.78 -0.03 -0.04 0.09 0.96

(1.41) (27.84) (-0.87) (-0.88) (2.94)U.S. Small Cap 1.20% 0.83 0.58 0.06 0.07 0.93

(0.97) (31.58) (10.58) (1.17) (1.80)

Universe Intercept Parent IndexSmall Cap – Large Cap Value – Growth

Defensive – Dynamic RSQ

U.S. Large Cap 0.99% 0.91 -0.03 -0.04 0.47 0.95

(1.17) (35.10) (-0.87) (-0.88) (6.71)U.S. Small Cap 1.26% 0.96 0.79 -0.13 0.45 0.96

(1.43) (30.19) (17.38) (-1.99) (6.28)

Universe Intercept Broad Market Small - Big Cap High - Low B/PWinners - Losers

(Momentum) RSQU.S. Large Cap 1.25% 0.93 -0.06 0.18 0.04 0.98

(2.00) (81.26) (-1.83) (7.02) (2.17)U.S. Small Cap 1.45% 0.93 -0.08 0.17 0.02 0.95

(2.54) (73.69) (-2.96) (7.26) (1.54)

Figures in parentheses are t-ratios calculated using Newey–West robust standard errors. Intercepts are annualized.

Figures in parentheses are t-ratios calculated using Newey–West robust standard errors. Intercepts are annualized.

Figures in parentheses are t-ratios calculated using Newey–West robust standard errors. Intercepts are annualized.

Table A-23: Fama-French exposures of Russell-Axioma Low Volatility Indexes (February 2005-March 2014)

Table A-24: Style exposures of Russell-Axioma Low Volatility Indexes (February 2005-March 2014)

Table A-25: Fama-French exposures of equal combination of High Efficiency Low Volatility, Momentum, Quality and Value Indexes (July 1996-March 2014)

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APPENDIX – PERFORMANCE MEASURES AND FACTOR MODELS

Universe Intercept Parent IndexSmall Cap – Large Cap Value – Growth

Defensive – Dynamic RSQ

DevelopedLarge Cap 1.26% 1.02 0.12 0.05 0.25 0.99

(3.17) (81.33) (4.32) (2.78) (8.38)U.S.Large Cap 1.55% 0.97 0.02 0.10 0.11 0.99

(3.92) (26.82) (0.93) (5.40) (3.33)

Universe Intercept Parent IndexSmall Cap – Large Cap Value – Growth

Defensive – Dynamic RSQ

DevelopedLarge Cap 0.97% 1.03 -0.04 -0.13 -0.33 0.99

(0.63) (35.96) (-0.76) (-1.92) (-4.42)U.S.Large Cap 0.57% 1.01 -0.12 -0.19 -0.29 0.98

(0.47) (35.69) (-2.77) (-2.77) (-5.08)

Universe Intercept Broad Market Small - Big Cap High - Low B/PWinners - Losers

(Momentum) RSQFundametal Developed All-Company + Momentum

1.24% 0.98 0.04 0.28 0.01 0.99

(2.85) (87.73) (1.62) (12.14) (0.38)Fundamental U.S. All-Company + Momentum

1.85% 0.96 -0.02 0.32 -0.02 0.98

(3.08) (63.00) (-0.59) (10.33) (-1.02)Equal-Weighted U.S. Large-Cap + Momentum

3.81% 1.02 0.07 0.18 -0.05 0.95

(3.29) (28.91) (1.37) (4.00) (-1.43)

Figures in parentheses are t-ratios calculated using Newey–West robust standard errors. Intercepts are annualized.

Figures in parentheses are t-ratios calculated using Newey–West robust standard errors. Intercepts are annualized. Date ranges are 2008:07-2014:03 for Developed and 2007:07-2014:03 for U.S.

Figures in parentheses are t-ratios calculated using Newey–West robust standard errors. Intercepts are annualized.

Table A-26: Style exposures of equal combination of High Efficiency Low Volatility, Momentum, Quality and Value Indexes (July 2007-March 2014)

Table A-28: Style exposures of 75/25 combination of GeoExposure and High Efficiency Value Indexes (DEV: July 2008-March 2014); (U.S.: July 2007-March 2014)

Table A-27: Fama-French exposures of 75/25 combination of Fundamental Developed, Fundamental U.S. and Equal-Weighted U.S. with High Efficiency Momentum (FDM:July 1996-March 2014); (EW: February 2000-March 2014)

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Indexes and Universes are unmanaged and cannot be invested in directly. Returns represent past performance, are not a guarantee of future performance, and are not indicative of any specific investment.Unless otherwise noted, source for the data in this presentation is Russell Investments.

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The Russell High Efficiency Defensive Indexes™ make use of Westpeak Global Advisors, LLC and Goldman Sachs Asset Management, L.P. patented ActiveBeta® portfolio construction techniques (Methods and Systems for Building and Managing Portfolios based on Ordinal Ranks of Securities (U.S. Patent Numbers 8,285,620 and 8,473,298)).Westpeak Global Advisors, LLC and Goldman Sachs Asset Management, L.P. are developers of technologies used in the Russell HEFI Indexes. Russell Indexes has independently developed intellectual property that is used to construct and maintain the Russell HEFI Indexes.

The Russell Geographic Exposure Indexes are based on the geographic exposure screening data provided by Revere Data, LLC. Revere Data, LLC is not a registered adviser or broker/dealer, and it does not have any affiliated entities or subsidiaries that are registered adviser or broker dealer. Revere Data is primarily a provider of financial data and analytics to institutional investors, index developers and corporations. Revere Data is the owner of the trademarks, service marks and copyrights related Revere GeoRev. Revere Data, LLC is a Delaware Limited Liability Corporation located in 1 California St. CA 94111.

First used November 2014.

CORP-9906-11-2016

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