Tag Archives: stocks

Noisy Market Hypothesis: Tilt Your Portfolio To Achieve Superior Returns (Part 1)

Summary In previous articles, we’ve shown how maintaining a diversified portfolio “beats the average retail investor”. In this articles, we will raise the bar and review the ways of “beating the market”. Initial building blocks (i.e. list of ETFs) for Satellite Portfolio are presented. This is the third article in the series that aims to develop portfolio investment approach that “beats the market”. The goal is to equip readers both with “knowledge about the path” and “confidence to stay on the path”. In the previous two articles, we’ve reviewed the ways of “beating the average retail investor”: These two articles serve as a practical guide to structuring core portfolio. We now move to the next step – satellite portfolio. We are raising the bar We saw what it takes to “beat average investor” and that doing so is pretty easy. All you need to do is maintain a diversified allocation to various asset classes. The key word is “maintain”; in other words, an investor should choose consistency over chasing the next “hot” stock or industry. As a reminder, please see the graph below; I hope that it will serve as a motivation: (click to enlarge) Source: J.P. Morgan and Dalbar Inc. Of course, managing emotions and staying the course is easier said than done. Especially, if your approach performed poorly for few years while your friend keeps on bragging about “that great stock” which made him a small fortune. How astonishing it is to see that few years of performance guide our long-term decisions. Just take a look at reactions that the second article in this series stirred up. It is true that commodities had very poor performance during last 4-5 years (and so did emerging market stocks). However, I wonder if half a decade performance warrants calling the commodities inappropriate for the portfolio [1]. History of the stock market is full with examples when the stock market pundits would conclude that some asset classes are no longer appropriate for portfolio, e.g. “stocks are dead” (typically, at the bottom of the market), just to observe market come back with a vengeance and prove all naysayers wrong. Putting short-termism aside, let’s go back to our long-term perspective. Commodity futures deliver equity-like returns (and risks as well) and have less than perfect correlation with stocks (i.e. provides diversification benefit). However, the focus of this article is not commodity futures, not even “Core Portfolio”. Our focus is “Satellite Portfolio” and how we can achieve even better returns through employing proven strategies. Our focus is on raising the bar. Noisy Market Hypothesis (NHM) and how to “beat the market” NHM provides a more realistic depiction of stock market dynamics when compared to Efficient Market Hypothesis (EMH). EMH claims that stock prices at every point in time represent the unbiased estimate of the true value of the firm. Such claims would have been true in ideal worlds where investors and speculators would not face liquidity constraints, tax considerations, institutional limitations, and many other externalities. Add to this list “popular delusions and madness of crowds” and you start questioning whether the even weak form of EMH is possible. I’m not suggesting to discard EMH. In the long term, information gets embedded in stock prices, but it may take a while. Quoting the “father of value investing” (Benjamin Graham): “In the short run, the market is a voting machine, but in the long run, it is a weighing machine.” In other words, in the short term, market “noise” might drive prices of particular stocks or even group of stocks significantly away from its intrinsic value and keep it there for a while. Just think of any stock market bubble. Unfortunately, taking advantage of such cases of mispricing is not easy. John Maynard Keynes reminds us that “the market can stay irrational longer than you can stay solvent.” As such one should expect that no trading strategy will consistently produce superior returns. This is one of the main implications of NMH. However, no need to despair. Based on academic research, Jeremy Siegel (father of NMH) concludes that over the long term it is possible to achieve better risk-adjusted return than holding very broadly diversified a portfolio. Jeremy Siegel mentions that taking advantage of “noise” might be achieved through “fundamental indexation” (i.e. weighting your holdings based on “fundamental factors”) instead of capitalization-weighted indexation. In other words, if the investor is able to stomach underperformance of his/her portfolio in short- and medium-term (which would be years), they might be well compensated in the long term for taking advantage of “fundamental factors”. We will discuss two of such fundamental factors – size (small caps) and style (value stocks) – in this article. Why value stocks and small capitalization stocks “beat the market”? Efficient Market Hypothesis (EMH) implies that strategy achieving higher absolute return is likely to be higher risk strategy. In other words, investors are compensated for taking the risk (only systematic risk, according to MPT) and, therefore, high risk equals potentially high return. As such, EMH advocates would claim that long-term outperformance of small caps and value stocks is due to higher risk. One can see why small caps would be a riskier proposition, however, value stocks are already selling at discount – how can they represent increased risk? One would expect that high-flying “hot” stocks with high multiples would expose investors to larger potential crash in price, compared to already “cheap” value stocks. However, EMH advocates would remind us about “value” trap. It’s when value stock continues to remain cheap for years and potentially keeps on getting worse. Instead of presenting you with arguments and counterarguments of various schools of thought, let me present you my version of why value and small-caps outperform. Small caps: Are riskier: typically higher volatility, higher chance to experience financial troubles (i.e. small to secure stable funding sources or access markets during rough patches). Are less liquid: low float, low trading volume, and higher bid-ask spreads. Are “under the radar”: not enough analyst coverage and institutional limitations (big asset managers or speculators might find it hard to establish meaningful exposure to single small-cap stock due to the limited amount of available issuance; at the end of the day, we are talking about small-cap stock). Value stocks: Might experience “value trap” (we will discuss how to address this concern in our next article). Are not “hot” names: typically boring names with seemingly mediocre stories. In “Stocks For the Long Run”, Jeremy Siegel presents information regarding the historical performance of small caps and value stocks from 1926-2012. For more details, please refer to his book; here, I’ve provided relevant excerpts: (click to enlarge) Source: Jeremy Siegel (click to enlarge) Source: Jeremy Siegel How do I know that small caps and value stocks will continue outperforming? Past performance is not a guarantee of future performance, isn’t it? “History does not repeat itself, but it often rhymes”. And, I think that’s the blessing for those who will follow the recommendations in these articles consistently and disregard short-term market gyrations. Just because history does not exactly repeat itself, investors tend to lose confidence in proven strategy after few years of underperformance. Some of the main reasons are thought to be human nature and memory. It is only human to throw away proven strategies and jump on the bandwagon as they face “this time it’s different” environment. This was the case during tulip mania of early 1600s and in recent history (just recall peak of the dot-com bubble in 2000). How many of such cases of mass disillusionment were experienced during these 400 years? And what lessons we learned? It either we believe that ” this time it’s different” or memories faded away since the last roller-coaster. Or, perhaps, we remember that experience vividly and will try to outsmart the market this time, by jumping off the train just before it falls into the abyss. There is, of course, an argument that market participants realized the existence of small cap and value phenomenon and traded up these stocks. Supporters of such arguments claim that due to “arbitraging away” these opportunities – small caps do not offer any alpha, it’s purely higher beta play and value stocks correctly reflect the valuation of less than stellar companies (again, no alpha here). We will review if such arguments are warranted in the future articles when we finalize our proposed allocations for a satellite portfolio. Before we discuss execution, let us draw a preliminary conclusion. As a group of investors continue jumping from one bandwagon to another in search of alpha, another more passive investors might benefit from staying put. Unless, you have a crystal ball, it’s advisable to identify portfolio allocation and don’t deviate materially from these target allocations. In the long term, tilting your portfolio in the direction of small caps and value stocks is expected to lead to superior returns. However, it might take years before you achieve superior return; markets might favor large caps and/or growth stocks for long stretches of time. List of ETFs For core portfolio, recommended allocations are presented in previous two articles. For satellite portfolio, I suggest tilting portfolio to small-cap stocks and value stocks. Following are ETFs that I recommend to achieve this goal: (click to enlarge) Source: Vanguard, and my own recommendations As you can notice, all four are Vanguard ETFs. I recommend Vanguard ETFs mainly because of their low fees (I am not affiliated with Vanguard and do not receive any compensation for recommending its products). There are other low-cost ETFs as well; typically, I use other ETFs for very specific tax reason. I will plan to cover this topic in my book (expected to publish in Amazon in December 2015 or January 2016) or potentially in the future Seeking Alpha articles. Following table provides a brief summary about the recommended ETFs: (click to enlarge) Source: Vanguard Size (i.e. small cap) and style (i.e. value) are not the only factors that historically proved to generate superior returns. We will discuss “other” factors in the next articles and determine sensible allocation to various factors. At that point, I will present detailed execution plan (i.e. the list of all ETFs and allocations to each). To conclude, the superior performance of small cap and value stocks (and some other factors that we will discuss in the next article) has been identified decades ago. However, the opportunity is still there. Maybe sometime in the future large portions of stock investors develop longer-term approach, bid up the prices, and bring systematic alpha of small cap and value stocks to zero. That “sometime in the future” could be a so distant phenomenon that might not even happen during my lifetime. To quote from John Maynard Keynes: “In the long run we are all dead.” In a meantime, I don’t mind additional 2-4% return compounding for decades. References/Bibliography Jeremy Siegel, The Noisy Market Hypothesis , Wall Street Journal, June 14, 2006 Jeremy Siegel, The Future for Investors: Why the Tried and the True Triumph Over the Bold , 2005 Jeremy Siegel, Stocks for the Long Run 5/E: The Definitive Guide to Financial Market Returns & Long-Term Investment Strategies , 2014 Next article: Noisy Market Hypothesis: Tilt Your Portfolio to Achieve Superior Returns (Part 2) Disclaimer: I’m not a tax advisor, please consult your tax advisor for any tax related matters. ETFs covered: The Vanguard Mega Cap Value ETF (NYSEARCA: MGV ), the Vanguard Value ETF (NYSEARCA: VTV ), the Vanguard Mid-Cap Value ETF (NYSEARCA: VOE ), the Vanguard Small Cap Value ETF (NYSEARCA: VBR ), the Vanguard Small Cap ETF (NYSEARCA: VB ) and the Vanguard Small Cap Growth ETF (NYSEARCA: VBK ) [1] Once again, I would like to highlight that I’m not supporter of buying spot commodities (e.g. gold bars, silver coins) – I suggest using commodity futures. I will plan to write an article on this topic in the future.

Low Vol U.S. Equity ETFs: 5 Risk Weighted Offerings

Summary This article examines 5 ETFs that strive to offer lower volatility and downside protection against the broad U.S. equity market. Each of the 5 ETFs considers prior volatility in selecting and weighting constituents. Three performance criteria and fees are analyzed. This article will examine 5 low/minimum volatility ETFs tracking indices whose goal is to create less risky portfolios in relation to their cap weighted equivalent. The way each underlying index builds a portfolio differs, but the common theme is that they use some measure of volatility as the sole basis for portfolio construction (with the exception of things like maximum weight for a stock and sector constraints). Selected constituents are then weighted based on their prior volatility, not their market cap. The recent market selloff of August and September provides us with some real life data for these funds. The oldest ETF discussed here is less than 5 years old, so real life data is limited. Although most of the underlying indices tracked go back farther, we will limit our analysis to their ETF manifestations and avoid back-tested un-investable indices. The following table introduces the ETFs with some basic information. They will be compared to the S&P 500, represented by the Vanguard S&P 500 ETF (NYSEARCA: VOO ). Name Ticker Inception AUM MER Vanguard S&P 500 ETF VOO September 7, 2010 $39.56 billion 0.05% PowerShares S&P 500 Low Volatility ETF SPLV May 5, 2011 $5.12 billion 0.25% iShares MSCI USA Minimum Volatility ETF USMV October 18, 2011 $6.82 billion 0.15% SPDR Russell 1000 Low Volatility ETF LGLV February 20, 2013 $30.16 million 0.12% iShares MSCI USA Size Factor ETF SIZE April 16, 2013 $201.90 million 0.15% Janus Equal Risk Weighted Large Cap ETF ERW July 29, 2013 $2.57 million 0.65% Source: Morningstar.com on November 27, 2015 A consideration of the methodologies and some basic portfolio characteristics will provide insightful background before we begin our analysis. The source for the methodology information is the respective ETF provider and underlying index provider websites. Vanguard S&P 500 ETF Methodology: The S&P 500 tracks 500 large U.S. companies that are weighted on a float-adjusted market cap basis. Probably the most popular benchmark in the world, we will use VOO as our benchmark and consider the ETFs in relation to it. Top Holdings Weight Apple Inc (NASDAQ: AAPL ) 3.70% Microsoft Corp (NASDAQ: MSFT ) 2.29% Exxon Mobil Corporation (NYSE: XOM ) 1.87% General Electric (NYSE: GE ) 1.59% Johnson & Johnson (NYSE: JNJ ) 1.52% Source: Morningstar.com on November 27, 2015 PowerShares S&P 500 Low Volatility ETF Methodology: The 100 stocks from the S&P 500 with the lowest standard deviation over the prior 252 trading days are weighted by the inverse of their volatility (lower volatility stocks get higher weights). Rebalancing and reconstitution occurs in February, May, August, and November. Top Holdings Weight Plum Creek Timber Co Inc (NYSE: PCL ) 1.26% Coca-Cola Co (NYSE: KO ) 1.26% Airgas Inc (NYSE: ARG ) 1.22% Clorox Co (NYSE: CLX ) 1.22% Waste Management Inc (NYSE: WM ) 1.16% Source: Morningstar.com on November 27, 2015 iShares MSCI USA Minimum Volatility ETF Methodology: Not much detail is given for the construction of the underlying MSCI index. We do know that the index is constructed using the proprietary Barra Optimizer to achieve the lowest absolute volatility with a certain set of constraints. The constraints include minimum and maximum constituent weights and sector weights relative to the original MSCI USA index. Rebalancing occurs in May and November. Top Holdings Weight McDonald’s Corp (NYSE: MCD ) 1.74% AT&T Inc (NYSE: T ) 1.66% Public Storage (NYSE: PSA ) 1.64% Paychex Inc (NASDAQ: PAYX ) 1.52% PepsiCo Inc (NYSE: PEP ) 1.49% Source: Morningstar.com on November 27, 2015 SPDR Russell 1000 Low Volatility ETF Methodology: Up to 200 stocks from the Russell 1000 with the lowest standard deviation over the past 252 trading days are weighted by the inverse of their volatility. Rebalancing occurs monthly. Top Holdings Weight Home Depot (NYSE: HD ) 2.17% Henry Schein Inc (NASDAQ: HSIC ) 2.10% Aflac Inc (NYSE: AFL ) 2.07% McDonald’s Corp 2.06% Travelers Companies Inc (NYSE: TRV ) 2.06% Source: Morningstar.com on November 27, 2015 iShares MSCI USA Size Factor ETF Methodology: This ETF tracks the MSCI USA Risk Weighted Index. The index considers the variance of the 3-year weekly historical local return of the MSCI USA Index. The weighting is computed as the ratio of the inverse of the security variance to the sum of the inverse of the security variances of all constituents in the parent index. Rebalancing occurs in May and November. Top Holdings Weight Synchrony Financial (NYSE: SYF ) 0.68% Chubb Corp (NYSE: CB ) 0.57% Arch Capital Group Ltd (NASDAQ: ACGL ) 0.53% Clorox Co 0.50% PepsiCo Inc 0.49% Source: Morningstar.com on November 27, 2015 Janus Equal Risk Weighted Large Cap ETF Methodology: Beginning with the S&P 500, stocks are weighted using a proprietary method such that the expected risk contribution of each stock is equal. Rebalancing occurs in January, April, July, and October. Top Holdings Weight Best Buy Co Inc (NYSE: BBY ) 2.43% L Brands Inc (NYSE: LB ) 1.67% Sysco Corp (NYSE: SYY ) 1.48% Motorola Solutions Inc (NYSE: MSI ) 0.88% Keurig Green Mountain Inc (NASDAQ: GMCR ) 0.86% Source: Morningstar.com on November 27, 2015 The sector makeup of the six ETFs differs substantially. Relative to the S&P 500, an underweight to energy and technology and overweight to basic materials, real estate, consumer defensive, and utilities are present in all of the low volatility ETFs. Sectors VOO SPLV USMV LGLV SIZE ERW Cyclical Basic Materials 2.79% 4.50% 3.46% 3.12% 4.43% 4.69% Consumer Cyclical 11.49% 3.10% 7.13% 5.89% 12.43% 18.55% Financial Services 14.97% 17.23% 10.75% 19.81% 18.85% 9.91% Real Estate 2.13% 6.71% 7.78% 12.75% 6.42% 4.61% Sensitive Communication Services 4.19% 4.10% 5.89% 5.82% 2.89% 2.42% Energy 7.11% 0.00% 2.52% 0.83% 3.60% 6.38% Industrials 10.96% 19.69% 9.44% 16.40% 14.02% 13.23% Technology 18.76% 0.00% 9.79% 5.68% 9.42% 11.51% Defensive Consumer Defensive 9.61% 20.13% 15.60% 11.97% 10.61% 11.54% Healthcare 15.05% 13.38% 19.80% 14.11% 9.78% 9.48% Utilities 2.93% 11.16% 7.84% 3.61% 7.54% 7.67% Source: Morningstar.com on November 27, 2015 The following holdings overlap matrix shows that these different approaches result in significantly different underlying holdings, even though the methodologies may seem similar. Holdings VOO SPLV USMV LGLV SIZE ERW VOO 100% 26% 38% 27% 50% 49% SPLV 26% 100% 43% 42% 29% 22% USMV 38% 43% 100% 35% 36% 25% LGLV 27% 42% 35% 100% 18% 12% SIZE 50% 29% 36% 18% 100% 66% ERW 49% 22% 25% 12% 66% 100% Source: ETF Research Center Overlap Analysis The correlation between them is noteworthy in that it is somewhat close to 1 with the exception of ERW. Correlation VOO SPLV USMV LGLV SIZE ERW VOO 1.00 0.85 0.93 0.90 0.96 0.33 SPLV 0.85 1.00 0.95 0.94 0.92 0.45 USMV 0.93 0.95 1.00 0.93 0.96 0.39 LGLV 0.90 0.94 0.93 1.00 0.94 0.46 SIZE 0.96 0.92 0.96 0.94 1.00 0.41 ERW 0.33 0.45 0.39 0.46 0.41 1.00 Source: Yahoo! Finance, monthly returns based on adjusted closing prices, 8/1/2013-10/31/2015 Evaluation Criteria Now that we have reviewed some of the basics, it is time to take a closer look at these ETFs in the context of past performance, with emphasis on their behavior in negative market periods. The measures chosen for evaluation are an attempt to answer the question: “What does an investor who chooses a low volatility fund care about?” The funds will be evaluated based on three performance criteria and their fees: Risk-adjusted returns relative to the S&P 500 as represented by VOO Up and down period performance relative to VOO Performance in periods where the S&P 500 faced a significant drawdown Fees Methodology: I used adjusted closing prices (adjusted for both dividends and splits) from Yahoo! Finance. Since this uses prices and not the NAV of the funds, I think it skews some of the results, mainly for the small and thinly traded ERW. With low volume, the underlying value of the fund’s holdings can deviate from its last traded price materially. This likely explains its low correlation to the other ETFs as well. Although prices describe the real investor experience, I would keep this in mind when evaluating the results, with particular emphasis on ERW. Criteria 1: Risk-adjusted returns relative to the S&P 500 as represented by VOO Low volatility ETFs should be held to a standard of exhibiting lower standard deviation than their relevant benchmark. However, the return side is important as well. If a fund produces low volatility but also low returns such that the risk-adjusted return is lower, the investor would have been better off holding the benchmark and some cash. We will divide the annualized return by the annualized standard deviation to determine risk-adjusted returns. This is essentially a Sharpe ratio, but ignores the risk free return because short term cash yields are so low (under 0.10% for 3 month T-bills for most of the period under examination). ETFs with a higher/lower value than VOO will receive a pass/fail on this criterion. VOO SPLV USMV LGLV SIZE ERW Return 11.72% 10.55% 12.51% 11.67% 11.50% 8.11% Std Dev 11.32% 10.42% 9.39% 10.53% 10.04% 7.62% Return/Std Dev 1.04 1.01 1.33 1.11 1.15 1.06 Result Fail Pass Pass Pass Pass Source: Yahoo! Finance, annualized monthly data based on adjusted closing prices, 8/1/2013-10/31/2015 Every fund exhibited lower standard deviation over the period examined. USMV even achieved higher returns, a nice bonus and a help in driving its return/standard deviation figure to be the highest of the bunch. Although SPLV managed a lower standard deviation than VOO, it was more than offset by its weaker performance. ERW is a concern here. The return of the fund is the lowest by far, and the only in single digits. In addition, its lack of trading volume has likely understated the true standard deviation of the NAV of the fund. The numbers say it still gets a pass, but extra caution should be placed on its results. Criteria 2: Up and down period performance relative to VOO This measure will provide detail on how the ETFs do in up and down periods. The ideal low volatility fund doesn’t go down very much in market declines but can hang in the market rallies. A passing grade will be given to a fund that outperforms in more than half of the months in which VOO had a negative return. The percentage outperformance in positive months for VOO will be presented as well, but will not be scored. Months SPLV USMV LGLV SIZE ERW Outperformance vs. VOO in up months 17 41% 35% 47% 41% 12% Outperformance vs. VOO in down months 10 80% 70% 80% 60% 100% Result Pass Pass Pass Pass Pass Source: Yahoo! Finance, monthly returns based on adjusted closing prices, 8/1/2013-10/31/2015 All funds outperformed more than half of the time against negative return months for the S&P 500 ETF. It is noteworthy that USMV had a higher return with lower standard deviation over the period (see Criteria 1) than VOO despite only outperforming in roughly a third of positive months and 70% of negative months. In contrast, both SPLV and LGLV had better up and down performance but lower returns than VOO. Clearly, this metric doesn’t tell the whole story, but is helpful in assessing tendencies of relative performance as the broader market goes through positive and negative periods. Criteria 3: Performance in periods where the S&P 500 faced a significant drawdown Since the time period in question is relatively short, there aren’t any decreases in VOO that are particularly steep. Regardless, we will examine the three largest drawdown periods since August 2013. This deeper look into the magnitude of out or underperformance relative to the benchmark will focus on performance when it matters most for low volatility investors. Three months stick out since August 2013. The total losses in each month aren’t particularly deep, but the lowest points in each drawdown are significant. To pass, the ETF in question will need to both outperform and have a smaller maximum drawdown in at least two of the three months. Intraday high and low prices for the respective month will be considered in determining the maximum drawdown. VOO SPLV USMV LGLV SIZE ERW August 2013 Month Return -3.08% -5.04% -3.26% -4.71% -3.30% -3.03% Drawdown -4.65% -6.19% -4.43% -6.26% -4.35% -4.25% January 2014 Month Return -3.53% -2.57% -3.04% -1.61% -1.95% -1.88% Drawdown -4.34% -3.53% -3.83% -2.89% -2.81% -3.23% August 2015 Month Return -6.14% -5.01% -4.53% -6.18% -5.59% -4.56% Drawdown -13.25% -48.35% -38.18% -8.96% -9.59% -6.95% Result Fail Fail Fail Pass Pass Source: Yahoo! Finance, monthly returns based on adjusted closing prices, 8/1/2013-10/31/2015 The August 2015 numbers may have caused a double take. It is well known that the carnage of August 24, 2015 brought many ETFs down well below their NAVs. Although it didn’t take long for the massive discounts to correct themselves, this experience highlights a real concern for ETF investors. Anyone caught with a stop loss or market order sell would have been at risk for a nasty surprise. Interestingly enough, it was the two largest ETFs that were affected. Only SIZE and ERW managed to pass this test. The August 2013 drawdown was particularly challenging for the group, while the opposite is true for the one in January 2014. Besides the deviation between price and NAV for SPLV and USMV, the August 2015 drawdown provides positive evidence of the effectiveness of low volatility strategies. I would be inclined to give more value to this drawdown, as it was significantly larger than the other two. Criteria 4: Fees Nothing eats away at returns quite like fees. The table below takes a look at several factors that will affect how expensive these funds are to hold and trade. VOO SPLV USMV LGLV SIZE ERW MER 0.05% 0.25% 0.15% 0.12% 0.15% 0.65% Average Volume 2,000,000 1,500,000 1,200,000 3,632 12,475 1,064 Spread 0.02% 0.03% 0.05% 0.49% 0.21% 1.19% Premium/Discount -0.09% -0.07% -0.07% 0.33% -0.31% -0.82% Result Pass Pass Pass Pass Fail Source: Morningstar.com on November 27, 2015 Fortunately, most of the ETFs are very reasonably priced, even against the super cheap VOO. Only ERW’s expense ratio is uncomfortably high. The spread and discount are also troublesome, although not entirely surprising given the small assets of the ETF. All in all, fees need only be a consideration for those interested in ERW. Although it would be nice to see SPLV come down to the 0.15% range, all four other ETFs are priced fairly. The spread and discount may seem a little high on some of the ETFs in the table, but keep in mind I was taking these down on a holiday shortened trading day, so they are likely understating the liquidity of a regular trading day. Conclusion Examining the four criteria gave valuable insight beyond the basic characteristics of the ETFs. SIZE was the only ETF to pass all four criteria. SPLV was the only to fail two, while the remaining three ETFs failed one each. Criteria SPLV USMV LGLV SIZE ERW 1. Risk-adjusted returns Fail Pass Pass Pass Pass 2. Up and down performance Pass Pass Pass Pass Pass 3. Drawdown performance Fail Fail Fail Pass Pass 4. Fees Pass Pass Pass Pass Fail Does this mean I think SIZE is the best of the bunch and should outperform the others in the future, at least in negative market environments? I would hesitate to go that far. For one, the available data only goes back a few years and doesn’t include many strong drawdown periods. However, based on the characteristics of the funds and the behaviour exhibited in our examined timeframe, I would feel comfortable using a low volatility product in a supporting capacity within the U.S. equity allocation of a portfolio. These products may be even more appropriate for somebody who is concentrated in a sector that is underrepresented in the funds, such as energy or technology. The only ETF I have reservations about is ERW. This small ETF trades thinly, with high bid ask spreads and a high expense ratio. It has done well in the performance criteria but this was influenced by the fact that we were looking at prices and not NAV. With ERW not trading some days and having low volume on the others, sizable discounts and premiums are common. I have nothing against the methodology of the underlying benchmark, but unless liquidity improves, it would be hard to place it above any of the other options. My recommendation is to consider combining any of SPLV, USMV, LGLV, or SIZE within your U.S. equity allocation. Of those four, there is no clear winner at this point. I will leave it to the reader to choose among them, and they are certainly differentiated in sector allocation, holdings similarity, and correlation. I deem all four suitable for lowering volatility and protecting on the downside as part of a larger U.S. allocation in a portfolio. Disclaimer: This article was not intended to be taken as investment advice. Please conduct due diligence of any ETF investment you are considering, including but not limited to a review of the prospectus, underlying benchmark methodology (if applicable), portfolio characteristics, holdings, performance since inception, role in your existing portfolio, and outlook for future performance.