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Low Volatility Portfolio Optimization Works Where Momentum Strategies Fail

Summary Momentum strategies have worked exceedingly well since 2008. It takes some effort to find a diversified portfolio for which momentum strategies fail. Adaptive asset allocation based on portfolio optimization with high volatility target also fails when momentum strategies fail. Adaptive asset allocation based on portfolio optimization with low volatility target performs well even when momentum strategies fail. Momentum strategies are very popular and are readily available at no cost on the internet. In fact, it takes some effort to find a well diversified portfolio of equities and bonds that would have failed. I used the “dual momentum” and the “relative strength” timing models on the portfoliovisualizer.com site and run a sequence of simulation on some ETF portfolios that included stocks, bonds, real estate and commodities. The portfolio I selected for the study is made up of six ETFs and it performed poorly for the momentum strategy with any look back period. As a benchmark we analyze the performance of the portfolio with equal weight targets, rebalanced when the allocation of any asset deviates by more than 20% from the target weight. That portfolio was subjected to 21 rebalancings within the time interval of the study from January 2007 to September 2015. In this article I compare the momentum strategy with the adaptive allocation strategies I described in many previously published articles. We investigate two versions of the strategy: a return maximization with a low volatility target, and another with a high volatility target. The version with low volatility target was subjected to 105 reallocations of the assets, virtually almost every month. The version with high volatility target was subjected to only 52 reallocations because it was allocated, on average, about two months to the same asset. Here is the list of securities used to build the portfolio: SPDR S&P 500 Trust ETF (NYSEARCA: SPY ) iShares U.S. Real Estate ETF (NYSEARCA: IYR ) SPDR Gold Trust ETF (NYSEARCA: GLD ) T he United States Oil ETF, LP (NYSEARCA: USO ) iShares 1-3 Year Treasury Bond ETF (NYSEARCA: SHY ) iShares 20+ Year Treasury Bond ETF (NYSEARCA: TLT ) The data for the study were downloaded from Yahoo Finance on the Historical Prices menu for SPY, IYR, GLD, USO, SHY and TLT. We use the daily price data adjusted for dividend payments. For the adaptive allocation strategy, the portfolio is managed as dictated by the mean-variance optimization algorithm developed on the Modern Portfolio Theory (Markowitz). The allocation is rebalanced monthly at market closing of the first trading day of the month. The optimization algorithm seeks to maximize the return under a constraint on the portfolio risk determined as the standard deviation of daily returns. In table 1 we list the total return, the compound average growth rate (CAGR%), the maximum drawdown (maxDD%), the annual volatility (VOL%), the Sharpe ratio and the Sortino ratio of the portfolios. Table 1. Performance of the portfolios from January 2007 to September 2015. TotRet% CAGR% maxDD% VOL% Sharpe Sortino Equal Weight 36.95 3.70 -35.85 10.46 0.32 0.42 AA LOW volatility 65.03 5.96 -11.05 6.02 0.99 1.32 AA HIGH volatility -4.73 -0.56 -55.18 23.19 -0.02 -0.03 The data in table 1 should be compared to the results of applying the dual momentum strategy as computed with the portfolio visualizer application. The dual momentum strategy investing monthly in the asset with the highest return over the previous 3 months had total return of -10.34%, with CAGR of -1.25%, maximum drawdown of -40.88% and volatility (St Dev) of 20.48%. There were two periods when the momentum strategy suffered huge losses; first in 2011-12 after gold topped, and the second in 2014-15 when oil prices tanked. The AA high volatility results are very similar to the dual momentum results. Most of the difference in drawdown and volatility is due to the fact that I use daily closing data while the portfolio visualizer site uses monthly data. That explains the slightly larger volatility and drawdown of the AA high volatility compared to the dual momentum. The small difference in the total return is due to a different allocation of the two strategies during a few months in 2011, as will be seen in figure 2. Of the three strategies, the AA with low volatility target performs the best both in return and risk. It produces a steady return of about 6% annually with a low volatility of only 6% and a maximum drawdown of -11%. The performance of the equal weight strategy falls in the middle; it returns on average almost 4% with low volatility of 10%, but still rather large drawdown of -36%. The equal weight strategy suffered steep losses during the 2008-09 bear market. In figures 1a and 1b we show the historical allocation of assets for the adaptive allocation strategy. (click to enlarge) Figure 1a. Historical asset allocation for the low volatility target portfolio. Source: All the charts in this article are based on calculations using the adjusted daily closing share prices of securities. As can be seen in figure 1a, the portfolio was allocated to SHY about 50% over the entire time. It was also allocated about 25% each to SPY and TLT. There were only small allocations to gold, oil and real estate. (click to enlarge) Figure 1b. Historical asset allocation for the high volatility target portfolio. Here one sees that the high volatility target portfolio was allocated alternately to one asset only, the same as in the momentum strategy. Only for a few months in 2009 was the portfolio invested in two assets simultaneously. In figure 2 we show the equity curves of the adaptive allocation portfolios. (click to enlarge) Figure 2. Equity curves for the adaptive allocation (NYSE: AA ) portfolios. We see in figure 2 that the high volatility target portfolio performed well until the fall of 2011. Since then, the equity either went down or oscillated in a range. Recently the equity fell below the initial investment. In figure 3 we show the equity curves of the low volatility and equal weight portfolio. (click to enlarge) Figure 3. Equity curves of the adaptive allocation with low volatility target and the equal weight portfolios. We see in figure 3 that the equal weight portfolio suffered large losses during the 2008-09 financial crises. It performed well between 2009 and 2012, but it fluctuates in a range since 2013. Still, overall, the equal weight portfolio performed better than the adaptive allocation or momentum strategy, as can be seen in figure 4. (click to enlarge) Figure 4. Equity curves of the adaptive allocation with high volatility target and the equal weight portfolios. Source: All the charts in this article are based on calculations using the adjusted daily closing share prices of securities. Conclusion The adaptive allocation by portfolio optimization with low volatility target performs satisfactorily during all market environments. Over a long investment horizon, it beats the equal weight as well as the momentum strategies.

Ivy Portfolio October Update

The Ivy Portfolio spreadsheet track the 10-month moving average signals for two portfolios listed in Mebane Faber’s book The Ivy Portfolio: How to Invest Like the Top Endowments and Avoid Bear Markets . Faber discusses 5, 10, and 20 security portfolios that have trading signals based on long-term moving averages. The Ivy Portfolio spreadsheet tracks both the 5 and 10 ETF Portfolios listed in Faber’s book. When a security is trading below its 10-month simple moving average, the position is listed as “Cash.” When the security is trading above its 10-month simple moving average the positions is listed as “Invested”. The spreadsheet’s signals update once daily (typically in the late evening) using dividend/split adjusted closing price from Yahoo Finance. The 10-month simple moving average is based on the most recent 10 months including the current month’s most recent daily closing price. Even though the signals update daily, it is not an endorsement to check signals daily or trade based on daily updates. It simply gives the spreadsheet more versatility for users to check at his or her leisure. The page also displays the percentage each ETF within the Ivy 10 and Ivy 5 Portfolio is above or below the current 10-month simple moving average, using both adjusted and unadjusted data. If an ETF has paid a dividend or split within the past 10 months, then when comparing the adjusted/unadjusted data you will see differences in the percent an ETF is above/below the 10-month SMA. This could also potentially impact whether an ETF is above or below its 10-month SMA. Regardless of whether you prefer the adjusted or unadjusted data, it is important to remain consistent in your approach. My preference is to use adjusted data when evaluating signals. The current signals based on September 30th’s adjusted closing prices are below. This month Vanguard Total Bond Market ETF (NYSEARCA: BND ) is above its moving average and the balance of the ETFs, Vanguard FTSE All-World ex-US ETF (NYSEARCA: VEU ), Vanguard Small Cap ETF (NYSEARCA: VB ), Vanguard Total Stock Market ETF (NYSEARCA: VTI ), SPDR DJ International Real Estate ETF (NYSEARCA: RWX ), Vanguard Emerging Markets Stock ETF (NYSEARCA: VWO ), PowerShares DB Commodity Index Tracking (NYSEARCA: DBC ), S&P GSCI Commodity-Indexed Trust (NYSEARCA: GSG ) Vanguard REIT Index ETF (NYSEARCA: VNQ ) and iShares Barclays TIPS Bond (NYSEARCA: TIP ), are below their 10-month moving average. The spreadsheet also provides quarterly, half year, and yearly return data courtesy of Finviz. The return data is useful for those interested in overlaying a momentum strategy with the 10-month SMA strategy: (click to enlarge) I also provide a “Commission-Free” Ivy Portfolio spreadsheet as an added bonus. This document tracks the 10-month moving averages for four different portfolios designed for TD Ameritrade, Fidelity, Charles Schwab, and Vanguard commission-free ETF offers. Not all ETFs in each portfolio are commission free, as each broker limits the selection of commission-free ETFs and viable ETFs may not exist in each asset class. Other restrictions and limitations may apply depending on each broker. Below are the 10-month moving average signals (using adjusted price data) for the commission-free portfolios: (click to enlarge) (click to enlarge) Disclosures: None.

FSTA: This Little Gem Of An ETF Is Beautiful When You Look Inside

Summary FSTA tracks one of my favorite sectors and there is nothing to hold against it. From the market capitalizations of the companies down to the top 10 holdings, everything looks intelligently designed. While many consumer staples ETFs would be scared to go overboard on tobacco, FSTA gets it. Consumer Staples funds should be loaded up on companies producing addictive products. I’m a little concerned about the sheer size of the allocations to Coca-Cola and Pepsi because of a movement towards healthier foods. I wouldn’t want to cut out those holdings because I think the distribution and branding systems give them moats for competing in healthy foods. One of the sectors I’ve come to like is the consumer staples sector. Unfortunately, many investors seem to be catching on to how desirable the sector allocation is when there are concerns of a new correction or recession. One of the funds that I’m considering is the Fidelity MSCI Consumer Staples Index ETF (NYSEARCA: FSTA ). I’ll be performing a substantial portion of my analysis along the lines of modern portfolio theory, so my goal is to find ways to minimize costs while achieving diversification to reduce my risk level. Expense Ratio The expense ratio on the Fidelity MSCI Consumer Staples Index ETF is only .12%. This fund gets my stamp of approval for giving investors consumer staples exposure at a very reasonable expense ratio. Market Cap The ETF has a focus on large capitalization companies, but investors should be worried if this chart looked different. The idea is to load up the portfolio on big companies that are designed to withstand negative events in the economy. I think large companies make more sense than smaller companies in that aspect because I want to see companies that are market leaders with strong pricing power in an established industry. Geography There really isn’t much to talk about here. This is all domestic equity. Sector This sector breakdown is excellent. Personally, I have a moral objection to companies that sell tobacco products because they cause cancer. On the other hand, I don’t have a moral objection to risk adjusted returns. The result of that conflict is that I have to admire the structure of this portfolio. I’d love to see a further breakdown in some categories such as beverages because I’d value having some alcohol in the portfolio as well. When I’m looking at consumer staples, I want companies that sell products that are absolutely addictive. This is just cold hard logic. Market leaders that can dictate pricing on addictive products are in the ideal position to survive recessions without a major drop in sales or earnings. Largest Holdings This is a solid batch of holdings. I don’t see a single company on the list that looks exposed to a recession. I’ll admit that having both Coca-Cola (NYSE: KO ) and Pepsi (NYSE: PEP ) at the top of the portfolio feels a little heavy. If I was going to tweak the portfolio a little, I might drop those two in favor of having a little more alcohol. My big concern about those companies is that I believe we are in a very long term shift towards healthier food and some of their branding value is going to be lost. The reason I would still want a significant allocation is because they are both masters of building brands and have established enormous distribution networks across the world. When (or if) that sustained shift to healthier foods does occur, I expect both Pepsi and Coke to be in position to buy up smaller companies with the right products and then run the products through their branding and distribution product. Simply put, even if they don’t have the right products yet, they have incredible economic moats that should help them acquire the right products and utilize those products better than smaller competitors could. As I’ve been going through consumer staples ETFs, I’ve noticed that Wal-Mart (NYSE: WMT ) is suspiciously absent from some of them. I think that is a mistake. I really like Wal-Mart as a dividend growth company and I think the employee wage issues are overblown . Building the Portfolio The sample portfolio I ran for this assessment is one that came out feeling a bit awkward. I’ve had some requests to include biotechnology ETFs and I decided it would be wise to also include in the related field of health care for a comparison. Since I wanted to create quite a bit of diversification, I put in 9 ETFs plus the S&P 500. The resulting portfolio is one that I think turned out to be too risky for most investors and certainly too risky for older investors. Despite that weakness, I opted to go with highlighting these ETFs in this manner because I think it is useful to show investors what it looks like when the allocations result in a suboptimal allocation. The weightings for each ETF in the portfolio are a simple 10% which results in 20% of the portfolio going to the combined Health Care and Biotechnology sectors. Outside of that we have one spot each for REITs, high yield bonds, TIPS, emerging market consumer staples, domestic consumer staples, foreign large capitalization firms, and long term bonds. The first thing I want to point out about these allocations are that for any older investor, running only 30% in bonds with 10% of that being high yield bonds is putting yourself in a fairly dangerous position. I will be highlighting the individual ETFs, but I would not endorse this portfolio as a whole. The portfolio assumes frequent rebalancing which would be a problem for short term trading outside of tax advantaged accounts unless the investor was going to rebalance by adding to their positions on a regular basis and allocating the majority of the capital towards whichever portions of the portfolio had been underperforming recently. Because a substantial portion of the yield from this portfolio comes from REITs and interest, I would favor this portfolio as a tax exempt strategy even if the investor was frequently rebalancing by adding new capital. The portfolio allocations can be seen below along with the dividend yields from each investment. Name Ticker Portfolio Weight Yield SPDR S&P 500 Trust ETF SPY 10.00% 2.11% Health Care Select Sect SPDR ETF XLV 10.00% 1.40% SPDR Biotech ETF XBI 10.00% 1.54% iShares U.S. Real Estate ETF IYR 10.00% 3.83% PowerShares Fundamental High Yield Corporate Bond Portfolio ETF PHB 10.00% 4.51% FlexShares iBoxx 3-Year Target Duration TIPS Index ETF TDTT 10.00% 0.16% EGShares Emerging Markets Consumer ETF ECON 10.00% 1.34% Fidelity MSCI Consumer Staples Index ETF FSTA 10.00% 2.99% iShares MSCI EAFE ETF EFA 10.00% 2.89% Vanguard Long-Term Bond ETF BLV 10.00% 4.02% Portfolio 100.00% 2.48% The next chart shows the annualized volatility and beta of the portfolio since October of 2013. (click to enlarge) Risk Contribution The risk contribution category demonstrates the amount of the portfolio’s volatility that can be attributed to that position. You can see immediately since this is a simple “equal weight” portfolio that XBI is by far the most risky ETF from the perspective of what it does to the portfolio’s volatility. You can also see that BLV has a negative total risk impact on the portfolio. When you see negative risk contributions in this kind of assessment it generally means that there will be significantly negative correlations with other asset classes in the portfolio. The position in TDTT is also unique for having a risk contribution of almost nothing. Unfortunately, it also provides a weak yield and weak return with little opportunity for that to change unless yields on TIPS improve substantially. If that happened, it would create a significant loss before the position would start generating meaningful levels of income. A quick rundown of the portfolio I put together the following chart that really simplifies the role of each investment: Name Ticker Role in Portfolio SPDR S&P 500 Trust ETF SPY Core of Portfolio Health Care Select Sect SPDR ETF XLV Hedge Risk of Higher Costs SPDR Biotech ETF XBI Increase Expected Return iShares U.S. Real Estate ETF IYR Diversify Domestic Risk PowerShares Fundamental High Yield Corporate Bond Portfolio ETF PHB Strong Yields on Bond Investments FlexShares iBoxx 3-Year Target Duration TIPS Index ETF TDTT Very Low Volatility EGShares Emerging Markets Consumer ETF ECON Enhance Foreign Exposure Fidelity MSCI Consumer Staples Index ETF FSTA Reduce Portfolio Risk iShares MSCI EAFE ETF EFA Enhance Foreign Exposure Vanguard Long-Term Bond ETF BLV Negative Correlation, Strong Yield Correlation The chart below shows the correlation of each ETF with each other ETF in the portfolio. Blue boxes indicate positive correlations and tan box indicate negative correlations. Generally speaking lower levels of correlation are highly desirable and high levels of correlation substantially reduce the benefits from diversification. (click to enlarge) Conclusion FSTA has a great expense ratio, great sector, and great allocations within the sector. This ETF is a slam dunk for long term holdings. The only concern I have about the sector right now is that other investors have caught on and started bidding up the price. There is one other worrying factor for the ETF. The average volume on it is quite dreadful. There are two ways to look at that issue. One is to bemoan the weak trading volume increasing the bid-ask spread. The other option is to look for ways to trade the ETF without commissions and then to keep using limit orders to try to enter at an attractive price. The ETF has far more liquidity problems than the underlying securities and the low expense ratio is fairly attractive for investors looking for a long term holding. The biggest caution here is that investors should avoid using any “market” orders. Only trade this one with limit orders.