A PRACTICAL EVALUATION OF THE EMERGING MARKET EQUITY ETF LANDSCAPE AUTHOR: ERIC BIEGELEISEN, CFA ABSTRACT

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1 A PRACTICAL EVALUATION OF THE EMERGING MARKET EQUITY ETF LANDSCAPE AUTHOR: ERIC BIEGELEISEN, CFA ABSTRACT The vast majority of assets invested in emerging market equity exchange traded funds (ETFs) in the US are concentrated in just two products, Vanguard s VWO and BlackRock s EEM. This paper provides a practical examination of the entire broad-based emerging market equity ETF landscape. While acknowledging multiple types of potential biases, the experiments conducted place VWO and EEM in the fourth quartile on risk and return over the time period studied, suggesting that there may be better alternatives for investing in broad-based emerging market equity ETFs. OVERVIEW The Exchange Traded Fund (ETF) industry has exploded both in terms of the number of products and asset growth since the introduction of the Index Participation Shares for the S&P 500 in Besides helping investors gain access to markets previously available only through mutual funds or active managers at a fraction of the price and without the necessary high minimum asset levels, ETFs have also provided the means to gain real-time exposures to asset classes that were previously out of reach. Chief among these is emerging market equities. Interest in this asset class by retail and institutional investors alike has grown over the past decade along with the rise in terms such as BRICs, CIVETS, and the Next 11. Further, the explosion in ETF product listings has led to an investment universe where 164 of the 974 so-called Equity ETFs are considered Emerging Markets (as of 7/31/2013). Notwithstanding, of the roughly $130 billion in these 164 funds, $83 billion or nearly 65% of assets are in just two products: the Vanguard FTSE Emerging Markets fund (ticker: VWO) and the BlackRock ishares MSCI Emerging Markets fund (ticker: EEM) 2. This high concentration warrants further analysis of the remaining product set in comparison to these industry giants to shed light on whether there is any value in complementing an existing position in one of these two market leaders with another emerging market equity ETF or indeed whether a combination of any of these smaller funds can produce similar or better risk-adjusted performance compared with their market leaders. It is the intent of this paper to explore the universe of alternative investment options in the emerging market equity ETF space, from which many products may pair quite well with the Vanguard and BlackRock funds. EXPERIMENT CONFIGURATION In performing this analysis, it is important to establish some guidelines and assumptions that will be paramount in interpreting and/or relying on the results herein. It is necessary to define the universe of candidate emerging market (EM) equity products, the time period and frequency of analysis, an understanding of the underlying holdings, the tracking error between index and product, and the associated fund fees and transaction costs. Next, the experiment will pair two EM funds at a time and naively weight them, i.e., 50/50, with an annual rebalance. Every possible pair combination will be examined including examining a 50/50 weighting of the same ETF, i.e., 100% of one ETF. 1 Gastineau, Gary (2002). The Exchange-Traded Funds Manual. John Wiley and Sons. p. 32. ISBN as of 7/31/2013. Published October

2 CONSIDERATIONS Beyond the experimental configuration (as detailed above), it will also be critical to acknowledge and analyze several considerations rather than outright accepting or rejecting the risk and return performance statistics presented. Given the short-time frame of existence for many of these EM equity ETFs, using the fund s underlying index to perform this analysis provides for a longer data set. However, this comes with the tradeoff of introducing tracking error between the fund and its index beyond that explained by the fund s expense ratio. This is due to several pricing factors, principally that many of these funds are comprised of various equities from countries with non-overlapping trading hours and which may not trade during US hours. This leads to a fund price that may reflect the market s perception of the underlying securities values, along with currency movements while the corresponding index s price may have been struck separately leading to tracking error. Index sampling for exposures versus full index replication is another source of tracking error, albeit more so in EM fixed income rather than equities. Unfortunately, while there are several methods for calculating tracking error, the usefulness of many of them is questionable or unavailable. The goal would be to perform a tracking error analysis that is consistent across all examined portfolios regardless of the underlying country exposures. Not only did the tracking error data examined for this experiment from Bloomberg not provide any particular trend but the method used by Bloomberg does not account for these factors. 3 specific making their inclusion in this analysis unfair as it would lead to an undesirable tilt in one s overall emerging markets equity exposure. Therefore, using IndexUniverse s ETF classification system utility 4, setting [Asset Class] to Equity, [Economic Development], [Region], and [Geography] to Emerging Markets, and [Niche] to Broad Based while eliminating leveraged and inverse products pruned this down to 48 candidate products. This was further pruned down to 34 by eliminating the Sector option under [Category], leaving Size and Style as well as Strategy as potential candidate products. Size and Style was then further pruned to only include [Segments] qualified as Total Market or Large-Cap to be reasonably close in design to the industry beta s which are composed of more than 60% large-cap 5. This classification filtering left a final universe size of 29 products. Next, the time period for analysis was then determined as a function of the availability of data for the funds underlying indexes. In an attempt to have the longest time period possible while accommodating as many candidates as possible in addition to capturing the full 2008 calendar year, five products were eliminated leaving 24 for the final analysis, as shown in Table 1. Other considerations to be examined for potential portfolio biases include transaction cost analysis, underlying ETFs AUM, portfolio expense ratios, underlying market capitalization, underlying country exposures, and underlying sector exposures. INVESTMENT UNIVERSE Although there are 164 so-called Emerging Markets Equity funds, many of these are niche and/or sector 3 As determined by Bloomberg field RK764: NAV_TRACKING_ERROR with parameter RK500: RISK_MEASURES_TIME_FRAME=2 meaning 3 months. 4 as of 7/31/2013. Developed by IndexUniverse, it is a hierarchical, rules-based, non-overlapping system for classifying ETFs. Overview and Description document for this classification system is available on their website. 5 Source: Bloomberg. Published October

3 Table 1: Investment Universe (sorted by Index Start Date Oldest to Newest) as of 7/31/2013 Ticker Bloomberg Index Start AUM (in mm) Date Expense 1 PXH PowerShares FTSE RAFI Emerging Markets Portfolio TFREMU 12/31/1993 $ % 2 SCHE Schwab Emerging Markets Equity ETF FTAG01 12/31/1993 $ % 3 GMM SPDR S&P Emerging Markets ETF STBMEMU 12/30/1994 $ % 4 IEMG ishares Core MSCI Emerging Markets ETF MIMUEMRN 1/31/1995 $ 1, % 5 EEHB PowerShares S&P Emerging Markets High Beta Portfolio SPEMHBIT 9/30/1997 $ % 6 EELV PowerShares S&P Emerging Markets Low Volatility Portfolio SPEMLVUT 9/30/1997 $ % 7 EWEM Guggenheim MSCI Emerging Markets Equal Weight ETF M2EFEWGT 12/31/1998 $ % 8 EDIV SPDR S&P Emerging Markets Dividend ETF SPGTEDUN 8/31/1999 $ % 9 EEM ishares MSCI Emerging Markets ETF NDUEEGF 12/29/2000 $ 35, % 10 EEME ishares MSCI Emerging Markets EMEA ETF NDDUEMEA 12/29/2000 $ % 13 EEMV ishares MSCI Emerging Markets Minimum Volatility ETF M00IEF$O 11/30/2001 $ 2, % 11 DBEM db X-trackers MSCI Emerging Markets Hedged Equity Fund M0EMHUSD 12/31/2001 $ % 12 TLTE FlexShares Morningstar Emerging Markets Factor Tilt Index Fund MEMMFT 12/31/2001 $ % 14 EMDR VelocityShares Emerging Markets DR ETF BKDEMT 12/31/2001 $ % 15 ADRE BLDRS Emerging Markets 50 ADR Index Fund BKTEM 12/31/2001 $ % 16 VWO Vanguard FTSE Emerging Markets ETF TGPVAN30 12/31/2002 $ 50, % 17 EMCR EGShares Emerging Markets Core ETF SPEMCRT 12/30/2005 $ % 18 AGEM EGShares GEMS Composite ETF DJEEGT 12/31/2005 $ % 19 DVYE ishares Emerging Markets Dividend ETF DJEMDIVR 12/31/2005 $ % 23 DEM WisdomTree Emerging Markets Equity Income Fund WTEMHYTR 5/31/2007 $ 4, % 22 HILO EGShares Low Volatility Emerging Markets Dividend ETF IHILOT 6/29/2007 $ % 20 BBRC EGShares Beyond BRICs ETF IBBRCT 9/28/2007 $ % 21 EMDD EGShares Emerging Markets Domestic Demand ETF IEMDDT 9/28/2007 $ % 24 PIE PowerShares DWA Emerging Markets Technical Leaders Portfolio DWATREM 9/28/2007 $ % Source: Bloomberg RESULTS With 24 candidates available and given the experiment configuration as described above, this leads to 300 portfolios 6 to be examined using monthly data from 12/31/2007 through 7/31/2013 (67 months). From this, four key risk-return statistics can be calculated for each portfolio: compound annual growth rate (CAGR), annual standard deviation, Sharpe Ratio, and portfolio maximum drawdown. The results of this analysis are shown in Table 2 where each portfolio was sorted by statistic and placed into quintiles, i.e., 60 portfolios to one quintile. Next, the portfolios within a quintile are averaged to produce the results shown below. 6 Using the all combinations formula: n!/(k!(n-k)!) + n, this is the number of combinations of n things taken k at a time, while allowing for the same 2 to also be picked, where n = 24, and k = 2. Table 2: Experiment Results of 300 Portfolios Broken down by Risk-Return Statistic and Averaged by Quintile (12/31/2007 7/31/2013) Quintile Sharpe Standard Maximum CAGR Average: Ratio Deviation Drawdown Top 4.5% % -48.5% Second 1.8% % -53.1% Third -0.2% % -56.2% Fourth -2.1% % -58.6% Bottom -4.0% % -60.8% Source: Bloomberg This table highlights significant differences from top to bottom quintile performance in each risk-return statistic being examined. Interestingly, the portfolios appearing in the top quintile for CAGR were also present in the other risk-return statistics. This suggests there are properties of many of these products found in the upper quintiles that benefit not only return, but the other risk statistics as well. By way of comparison, both VWO and EEM fall into the fourth quintile for all four statistics. This suggests Published October

4 (absent the aforementioned considerations detailed above) that over this time period an investor would have been considerably better off by combining one of the other ETFs in this universe with one of these leading emerging market betas. Table 3 illustrates the number of products available in a naïve split that would have produced (by risk-return statistic) a portfolio in one of the top 3 quintiles when paired with either VWO or EEM. Table 3: Enhancing Risk-Adjusting Returns by Complementing a VWO or EEM Allocation Number of products available that when paired with the industry leader product produce a result for the listed statistic that is in the top three quintiles over the 12/31/2007 to 7/31/2013 time period. VWO EEM CAGR 10 9 Sharpe 11 9 Standard Deviation Max Drawdown Source: Bloomberg Finally, Table 4 lists the 100% portfolio of each ticker from best to worst performing over the time period studied for each risk-return statistic as well as showing its respective place out of the 300 portfolios studied by statistic and color coded by quintile. Table 4: Single Ticker Portfolio Performance by Risk-Return Statistic with Corresponding Overall Rank out of the 300 Portfolios Studied Rank Ticker CAGR Rank Ticker Sharpe Rank Ticker Standard Deviation Rank Ticker Maximum Drawdown 2 DVYE 7.8% 1 EELV EELV 18.8% 2 HILO -42.4% 3 EELV 7.5% 6 DVYE DBEM 20.4% 3 EELV -42.4% 6 HILO 6.7% 8 HILO EEMV 21.7% 14 DEM -46.9% 17 EEMV 5.3% 16 EEMV HILO 22.7% 28 EEMV -49.1% 22 EMDD 5.0% 22 EMDD DEM 23.1% 29 DBEM -49.1% 37 DEM 3.8% 33 DEM EMDD 23.8% 43 DVYE -50.0% 44 BBRC 3.2% 48 BBRC BBRC 25.1% 45 EMDD -50.2% 82 EMCR 2.0% 87 EMCR DVYE 25.4% 137 BBRC -55.2% 128 EDIV 0.5% 128 EDIV ADRE 26.7% 144 ADRE -56.0% 150 EWEM -0.1% 150 EWEM EDIV 26.9% 149 PXH -56.3% 154 PIE -0.2% 152 PIE GMM 27.5% 173 EDIV -57.3% 192 PXH -1.7% 192 PXH SCHE 27.6% 184 EMCR -57.9% 196 DBEM -1.7% 196 GMM VWO 27.6% 190 GMM -58.0% 197 GMM -1.8% 209 IEMG EEM 27.6% 202 EWEM -58.4% 215 VWO -2.1% 211 VWO AGEM 27.8% 212 VWO -58.7% 218 IEMG -2.1% 225 EEM PXH 27.8% 220 EEM -58.8% 224 EEM -2.4% 230 DBEM IEMG 28.0% 231 IEMG -59.1% 260 TLTE -3.5% 242 EEHB PIE 28.1% 253 SCHE -59.8% 272 EEHB -3.8% 260 TLTE EMDR 28.2% 258 AGEM -59.9% 274 AGEM -3.8% 272 EEME TLTE 28.3% 260 TLTE -60.0% 278 EEME -4.1% 276 AGEM EMCR 28.3% 279 EMDR -61.1% 287 SCHE -4.6% 285 SCHE EWEM 29.1% 282 EEHB -61.3% 293 EMDR -5.2% 293 EMDR EEME 30.0% 297 EEME -63.1% 300 ADRE -7.5% 300 ADRE EEHB 35.8% 300 PIE -64.9% First Quintile Second Third Bottom Quintile Fourth Source: Bloomberg, 12/31/2007 7/31/2013 Published October

5 ANALYSIS This experiment is predicated on a number of assumptions and considerations as detailed above. These must be examined for each risk-return statistic to understand these results more completely. One of the most important to be mindful of is the time period being studied. Rather than reducing the entire time period into a single number (as is shown in Table 2), examining three year rolling periods of each statistic for each quintile should help illuminate whether there are any shorter term biases. More specifically, the quintiles as determined over the whole time period are used for each three year period, and each point on the chart below represents a 3 year window of CAGR. Interestingly, it appears that there is no time period bias, i.e., every data point for each quintile follows the same trend as the aggregated data shown in Table 2, where the top quintile outperforms the second quintile, and the second quintile outperforms the third, etc. Source: Bloomberg, 12/31/2007 7/31/2013 Finally, the same behavior is also shown in the chart below for portfolio maximum drawdown. While each of these results suggests that by examining the whole time period does not introduce a time period bias, it is not enough to suggest that the time period preceding this analysis and the time period in the future will follow the trends found in this analysis. Source: Bloomberg, 12/31/2007 7/31/2013 Source: Bloomberg, 12/31/2007 7/31/2013 Although not shown, the chart for Sharpe Ratio is nearly identical to the CAGR one above in shape and order of quintile performance. The chart below showing 3 year rolling periods for standard deviation also shows the same behavior of persistent lower standard deviation for the higher ranked quintiles. Although not a perfectly uniform trend, Table 5 depicting the average bid/ask spread as a percentage by quintile, shows that the top relative to the bottom quintile showed a tighter bid/ask spread 7. This is only a relative (and not absolute) proxy to examining transaction costs, as many factors must go into performing an in depth transaction cost analysis. This is largely dictated by the overall size of the trade and more importantly the exact securities within the published fund creation basket. However, presumably the tighter the spread, the more efficient it is to perform the annual rebalance on the portfolio as is required for this experiment. Notwithstanding, the finding is interesting and suggestive that the 7 As determined by Bloomberg field PY001: AVERAGE_BID_ASK_SPREAD_% with parameters PX393: CALC_INTERVAL=30d and PX392: END_DATE_OVERRIDE= Published October

6 securities found in the higher quintiles are either better covered by the market makers or more likely just more cost efficient for authorized participants to create and redeem. However, that being stated, VWO and EEM have very tight spreads of 0.03% each. While this helps explain continued investor demand for these securities, these tight spreads are not consistent with the notion that that tighter the spread the better the performance. Table 5: Portfolio Average Bid/Ask Spread Quintile Standard Maximum CAGR Sharpe Ratio Average: Deviation Drawdown Top 0.98% 0.99% 1.11% 0.96% Second 1.61% 1.60% 1.50% 1.34% Third 2.16% 2.18% 1.47% 1.67% Fourth 1.84% 1.82% 2.29% 2.81% Bottom 3.14% 3.14% 3.36% 2.95% Next, examining the assets under management (AUM) for each of the underlying portfolio s ETFs in Table 6 deceptively depicts that the smaller the AUM, the better the performance for each statistic. While this is true when comparing the top to bottom quintiles, the fourth quintile is an outlier whose AUM numbers dwarf all others. This pattern is explainable due to the increased presence of both VWO and EEM in this quintile whose combined AUM accounts for north of 87% of the investment universe being examined, i.e., the 24 products listed in Table 1. Similarly, the entire trend of the table may be explainable by the number of appearances of VWO and EEM within each quintile which is depicted by way of example for the CAGR statistic to the right in this table which embodies this trend. Table 6: Portfolio Average AUM (MM) Quintile Average: CAGR Sharpe Ratio Standard Deviation Maximum Drawdown Appearances of VWO and EEM in CAGR Top $ 1,859 $ 1,843 $ 2,083 $ 2,186 3 Second $ 3,804 $ 3,820 $ 3,787 $ 4,083 9 Third $ 3,478 $ 3,460 $ 3,155 $ 2,773 8 Fourth $ 6,233 $ 6,970 $ 8,757 $ 7, Bottom $ 4,781 $ 4,062 $ 2,374 $ 3, Expense ratios are shown in Table 7. As may be expected, the top quintile carries the highest expense ratio relative to the bottom for each statistic; however again with no discernible trend in between. Interestingly, VWO carries an 18 bps expense ratio, which is lower than anything in Table 7; while EEM carries an expense ratio of 66 bps, higher than anything in this table. This fact alone suggests that expense ratio is not the leading driver of asset flows into emerging market equity fund products. The top quintile s outperformance (shown in Table 2) in CAGR (+4.5%) versus VWOs (-2.1%) is more than made up by the difference in the increased expense ratio, i.e., 57 versus 18 bps. Table 7: Portfolio Average Expense Ratio Quintile Standard Maximum CAGR Sharpe Ratio Average: Deviation Drawdown Top 0.57% 0.58% 0.55% 0.57% Second 0.58% 0.57% 0.60% 0.54% Third 0.59% 0.59% 0.56% 0.57% Fourth 0.54% 0.52% 0.53% 0.52% Bottom 0.45% 0.47% 0.51% 0.54% Differences in the underlying holdings average market capitalization may help explain the source of additional return for the top versus the bottom quintiles. Specifically, Table 8 depicts the portfolio Published October

7 fund average underlying market capitalization 8. This consideration shows a strong trend for the better performing quintiles having significantly smaller underlying average fund market capitalization than the worse performing quintiles for CAGR and Sharpe. The trend is not as strong for standard deviation or maximum drawdown, but still present. By way of comparison, VWO and EEM have fund average market capitalizations of $39b and $43b, which generally puts them squarely in the fourth and fifth quintiles across the statistics, respectively. This consideration may suggest that the funds underlying average market capitalization is responsible for its outperformance, i.e., the smaller the average underlying capitalization, the better the performance. Table 8: Portfolio Average Market Capitalization (MM) Quintile Standard Maximum CAGR Sharpe Ratio Average: Deviation Drawdown Top $ 22,542 $ 22,620 $ 30,284 $ 27,289 Second $ 30,074 $ 29,997 $ 32,652 $ 32,305 Third $ 33,006 $ 32,468 $ 32,726 $ 36,889 Fourth $ 38,470 $ 38,270 $ 42,051 $ 39,423 Bottom $ 45,920 $ 46,658 $ 32,300 $ 34,107 Next, the country exposures within the portfolios must be examined as although EM equities are considered a so-called asset class space, each of the many countries within often exhibit a wide variation in terms of culture and socioeconomic status amongst many other significant differences that manifest in variations within their respective stock and bond market s performance. Table 9 details the portfolio average of top 15 countries exposure by quintile and by risk/return statistic. There are many uniform trends across the risk/return statistics. First, the top 15 on average make up over 90% of the total exposure of the portfolios. At the same time, 14 of these countries are found across all risk statistics for the top quintile. This suggests that the weighting of these 14 of the nearly 50 countries found throughout all examined portfolios should largely dictate the differences throughout the top and bottom performers. More specifically, across all statistics, the top quintile relative to the bottom quintile showed larger allocations to Taiwan, Malaysia, and Thailand with smaller relative allocations to China, Russia and South Korea. However, several countries did not show uniformity across risk statistics or across quintiles, e.g., greater Turkey exposure led to better CAGR and Sharpe while it led to worse Standard Deviation and was largely neutral with respect to Maximum Drawdown. On a country weighted basis, both EEM and VWO s country exposures place it in the fourth quintile (based on the table below) on a CAGR and Sharpe measure, while in the third quintile for Standard Deviation and Maximum Drawdown with significant variation in terms of having some top quintile country exposures, e.g., VWO has Taiwan at over 13%, as well several bottom quintile exposures. Given the lack of consistent uniform trends across all countries and statistics, it would appear that certain country exposures during the time period studied had more impact than others. It should also be noted that many of these indexes rebalance and/or reconstitute in some cases more radically indicating that this analysis is highly time period dependent and may not be useful over a long time period as is done here. Further, some products may shift their benchmark index from time to time, as was the case for VWOs transition from MSCI to FTSE. In this case however, even with the dramatic shift in country exposure, both VWO and EEM in their current make-ups had similar outcomes. 8 As determined by Bloomberg field FD102: FUND_AVG_MKT_CAP. Published October

8 Table 9: Portfolio Average Top 15 Countries Exposure by Quintile and Risk/Return Statistic (Sorted by Top Quintile Country Exposure) CAGR Top 2nd 3rd 4th Bottom Sharpe Top 2nd 3rd 4th Bottom Taiwan 12.9% 9.9% 9.1% 10.3% 8.5% Taiwan 12.6% 10.2% 9.0% 10.4% 8.5% South Africa 11.3% 11.1% 11.2% 9.6% 10.6% South Africa 11.3% 11.1% 11.3% 9.4% 10.7% China 10.2% 13.2% 13.0% 15.5% 16.4% China 10.2% 13.2% 13.1% 15.6% 16.2% Brazil 9.7% 10.0% 11.1% 12.7% 14.1% Brazil 9.6% 10.1% 11.0% 12.7% 14.2% Malaysia 7.9% 5.5% 5.0% 3.7% 2.2% Malaysia 7.9% 5.5% 4.9% 3.7% 2.2% Mexico 5.6% 6.0% 6.4% 5.3% 5.5% Mexico 5.6% 5.9% 6.3% 5.3% 5.5% Thailand 5.4% 4.7% 3.7% 3.0% 2.3% Thailand 5.5% 4.6% 3.8% 3.2% 2.1% Russia 5.0% 7.3% 6.9% 7.6% 9.8% Russia 5.1% 7.2% 6.9% 7.3% 10.1% South Korea 4.9% 6.2% 6.3% 8.4% 8.5% South Korea 4.9% 6.2% 6.3% 8.5% 8.4% Turkey 4.5% 4.0% 3.5% 3.0% 2.9% Turkey 4.5% 3.9% 3.6% 3.0% 2.8% India 4.4% 5.5% 5.4% 5.7% 5.8% India 4.5% 5.4% 5.3% 5.8% 5.8% Chile 3.1% 2.3% 2.3% 1.7% 1.4% Chile 3.1% 2.3% 2.3% 1.7% 1.4% Indonesia 3.1% 3.2% 3.0% 2.6% 2.3% Indonesia 3.1% 3.2% 3.0% 2.8% 2.2% Poland 2.8% 2.5% 2.9% 2.4% 2.0% Poland 2.9% 2.5% 3.0% 2.2% 2.1% Philippines 2.2% 2.0% 1.6% 1.2% 1.0% Philippines 2.3% 1.9% 1.6% 1.3% 0.9% Total in Top 15 93% 93% 91% 93% 93% Total in Top 15 93% 93% 91% 93% 93% Standard Deviation Top 2nd 3rd 4th Bottom Maximum Drawdown Top 2nd 3rd 4th Bottom Taiwan 12.0% 9.4% 11.1% 10.6% 7.6% Taiwan 11.6% 12.0% 8.9% 9.9% 8.3% China 11.8% 13.3% 12.3% 17.0% 14.0% China 11.2% 13.3% 13.6% 15.3% 14.9% Brazil 10.9% 10.5% 14.1% 13.4% 8.7% South Africa 11.0% 9.5% 10.4% 9.2% 13.7% South Africa 9.5% 11.4% 10.5% 9.4% 13.0% Brazil 10.0% 12.2% 12.8% 13.4% 9.1% South Korea 8.2% 5.0% 5.0% 6.4% 9.7% Malaysia 7.5% 5.3% 4.5% 3.7% 3.3% Malaysia 7.7% 5.1% 4.7% 3.8% 3.0% Russia 6.3% 6.7% 6.6% 6.9% 10.0% Mexico 5.6% 6.3% 6.5% 6.1% 4.4% South Korea 6.0% 6.5% 6.2% 6.8% 8.8% Russia 5.5% 8.3% 5.6% 7.7% 9.5% Thailand 5.5% 4.0% 3.3% 2.7% 3.5% India 4.4% 5.7% 4.5% 7.0% 5.3% Mexico 5.2% 5.3% 7.5% 5.6% 5.1% Thailand 4.2% 5.0% 3.5% 2.8% 3.6% India 4.8% 4.9% 5.3% 6.9% 4.8% Chile 3.2% 2.0% 2.1% 1.9% 1.6% Turkey 4.7% 3.2% 3.0% 2.7% 4.3% Turkey 3.1% 4.0% 3.8% 2.2% 4.8% Poland 2.9% 2.7% 2.5% 2.3% 2.4% Poland 2.8% 2.6% 3.2% 1.5% 2.7% Chile 2.8% 2.4% 2.0% 2.2% 1.3% Indonesia 2.6% 3.1% 2.8% 2.3% 3.5% Indonesia 2.7% 3.1% 3.1% 2.8% 2.5% Hong Kong 2.6% 2.5% 3.3% 4.1% 4.4% Philippines 2.3% 1.6% 1.3% 1.3% 1.5% Total in Top 15 94% 94% 93% 96% 96% Total in Top 15 94% 93% 91% 92% 94% Finally, examining Table 10 highlights the differing composition on a sector basis between the portfolios within each quintile. Recognizing that the sector exposures used in this analysis are the current exposures and does not account for the fact that many of these strategies may have had very different sector exposures historically, and/or may change on a more frequent basis, it is nonetheless instructive to examine the trends across quintiles as well as relative to VWO and EEM. The common trends across the statistics by quintile are that the better performing portfolios had less exposure to basic materials, energy, technology and industrials, and more exposure to communications, consumer cyclicals and non-cyclicals, and utilities. No discernible trend from diversifieds or financials was present. Interestingly, these same trends held when comparing the top quintile average sector exposures to either Published October

9 VWO or EEM. Again, this consideration may suggest that the underlying funds sector exposure is responsible for its outperformance, i.e., the more overweighted and underweighted exposures to the aforementioned sectors relative to VWO and EEM, the better the performance. However, this may also simply suggest a heavy time period bias, i.e., over the time period studied sectors that are more consumer focused outperformed those that are more resource focused. Table 10: Portfolio Average Sector Exposure by Quintile and Risk/Return Statistic CAGR Top 2nd 3rd 4th Bottom Sharpe Top 2nd 3rd 4th Bottom Basic Materials Basic Materials Communications Communications Consumer, Cyclical Consumer, Cyclical Consumer, Non-cyclical Consumer, Non-cyclical Diversified Diversified Energy Energy Financial Financial Industrial Industrial Technology Technology Utilities Utilities Standard Deviation Top 2nd 3rd 4th Bottom Maximum Drawdown Top 2nd 3rd 4th Bottom Basic Materials Basic Materials Communications Communications Consumer, Cyclical Consumer, Cyclical Consumer, Non-cyclical Consumer, Non-cyclical Diversified Diversified Energy Energy Financial Financial Industrial Industrial Technology Technology Utilities Utilities CONCLUSION In this experiment, 300 portfolios were examined that were naively composed to gain a better understanding of the universe of so-called broad-based emerging market equity ETFs relative to the two market leading products, i.e., Vanguard s VWO and BlackRock s EEM. The results over the time period studied show that there are several other products available to complement an existing holding of either of the two industry giant products; however while acknowledging a variety of considerations, it may be that this increased performance is due simply to a smaller capitalization, sector biased, and countrytilted portfolio relative to these industry leaders. Another commonality between the top performers versus the bottom performers is the weighting scheme as classified by IndexUniverse s ETF classification system utility. When examining the top half of the twenty four 100% portfolios ranked by CAGR, there was no consistent weighting scheme type other than the lack of Market-Cap appearing (except in two cases: one for more of a specific set of sectors play and the other for more of a specific exclusion of specific countries play, yet both of which still satisfied the broad enough diversification to be characterized by the utility for use in this study). Alternatively, the bottom half of these portfolios were all listed as Market-Cap (except in two cases: one was proprietary and the other is weighted by beta, neither of which appear anywhere else in the list). It was those portfolios weighted by Published October

10 volatility, dividends, equal-weight, multi-factor, fundamental, and in special cases market-cap, that outperformed those that were simply vanilla marketcap indexes. While this may offer an instant clue to expectations about future performance, it may be that these findings are simply time-period biased, and that which occurred in the past may not necessarily be indicative of future performance. Regarding time period bias, it is worth considering that this study was entirely in-sample and the outperformance that persisted during this time period may not necessarily be indicative of persistence into the future, e.g., it may be the case that the resource driven companies outperform the consumer based one s going forward. This idea may help explain some of the findings. Given that VWO and EEM are market-cap industry benchmarks, it would be a reasonable hypothesis a priori to assume that these two products would have fallen in the third quintile with an even distribution of risk/return metrics of other products around these benchmarks, as certain sector, country and size biased factors determines which side of the distribution they fall in. However, VWO and EEM fell squarely in the fourth quintile suggesting perhaps that it is only recent outperformance of these other index-based products that has led product issuers to launch them into the marketplace while avoiding those index-based products with sub-par performance thereby pushing VWO and EEM down a quintile. Therefore, it is possible that mean reversion will play a role at some point, in which case, those that recently outperformed will underperform; which may have the opposite effect of pushing VWO and EEM back into the third or even second quintile. Alternatively, while this study did not examine the underlying index construction methodologies of the various indexes, many do adapt over time thus it is possible that continued outperformance may persist as size, sector, and country rotations in these strategies allow these portfolios to remain on top. IMPORTANT DISCLOSURES This paper was originally published in the March/April 2014 issue of the Journal of Indexes. This material is provided for limited purposes. It is not intended as an offer or solicitation for the purchase or sale of any financial instrument, or any Broadmeadow Capital, LLC ( Broadmeadow ) product or strategy. References to specific asset classes and financial markets are for illustrative purposes only and are not intended to be, and should not be interpreted as, recommendations or investment advice. Data presented for specific ETFs is included for analytical purposes only and should not be considered a recommendation of any security mentioned herein. The opinions expressed in this article represent the current, good-faith views of the author(s) at the time of publication (Originally, March 2014). The views are provided for informational purposes only and are subject to change. This material does not take into account any investor s particular investment objectives, strategies, tax status, or investment horizon. Investors should consult a financial advisor for advice suited to their individual financial needs. Data is provided as of the date indicated. Broadmeadow cannot guarantee the accuracy or completeness of any statements or data contained in the article. Predictions, opinions, and other information contained in this article are subject to change. Any forward-looking statements speak only as of the date they are made, and Broadmeadow assumes no duty to update them. Forward-looking statements are subject to numerous assumptions, risks, and uncertainties. Actual results could differ materially from those anticipated. Past performance is not a guarantee of future results. As with any investment, there is a potential for profit as well as the possibility of loss. ABOUT THE AUTHOR Eric Biegeleisen, CFA, is a Co-Founder and the Chief Investment Officer of Broadmeadow Capital, LLC an investment firm providing alternative investment strategies to individual and institutional investors. Prior to co-founding Broadmeadow, he was the director of research at Windhaven Investment Management, an asset allocation firm incorporating ETFs and quantitative methodologies. While at Windhaven, he was responsible for the firm s investment model, global investment research, Published October

11 and product due diligence. As an ETF industry expert, he often spoke on panels at industry conferences. Windhaven was formed at the sale of Windward to Charles Schwab & Co in 2010 where he was a senior investment analyst. Previously, Biegeleisen worked in the defense contracting industry with General Dynamics C4 Systems and Textron Defense Systems working on the modeling and simulation of next-generation networking and communications devices and intelligent munitions, respectively. He holds a B.S. in electrical engineering from Trinity College, an M.S. in electrical engineering from the University of Southern California and an MBA from Boston University. APPENDIX A: DEFINITIONS BRIC: An acronym that refers to the countries of Brazil, Russia, India and China, which are all deemed to be at a similar stage of newly advanced economic development. CIVETS: An acronym given to the countries Colombia, Indonesia, Vietnam, Egypt, Turkey and South Africa, which are predicted by some to be among the next emerging markets to quickly rise in economic prominence over the coming decades. Next 11: Eleven countries that, according to a Goldman Sachs analysis, have the potential to become the world's largest economies in the 21st century. These are: Bangladesh, Egypt, Indonesia, Iran, Mexico, Nigeria, Pakistan, the Philippines, Turkey, South Korea and Vietnam. Bid-Ask Spread: The Bid-Ask Spread is the amount by which the ask price exceeds the bid. This is essentially the difference in price between the highest price that a buyer is willing to pay for an asset and the lowest price for which a seller is willing to sell it. CAGR: Compound Annual Growth Rate (CAGR) is the mean annual growth rate of an investment over a specified period of time longer than one year. Max Drawdown: Max Drawdown measures the peak-to-trough percentage decline in portfolio value. Max drawdown is an indicator of the risk of a portfolio. Sharpe Ratio: A ratio developed by William F. Sharpe defined as return above the risk-free rate divided by standard deviation. It is meant to provide a risk-adjusted measure of investment performance. Higher Sharpe Ratio is better, all else being equal. When comparing investment approaches using the Sharpe Ratio it is important to use the same risk-free rate in both calculations. Standard Deviation: Standard deviation measures the dispersion of returns; a large dispersion shows higher volatility. Published October