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Simple ETF Portfolio Performance With Monthly Reallocation By Mean-Variance-Optimization
Summary The simple ETF portfolio with monthly reallocation performed better than the equal weight portfolio in 2015. The low and mid risk portfolios had good positive returns, while the high risk portfolio had a very small loss. Even the high risk portfolio performed better than the equal weight portfolio. The simple ETF portfolio was introduced in an article published in August 2015. Since then the markets suffered a mini crash and a correction associated with high volatility and very negative market sentiment. Investors all over the world moved large amount of money out of the stock market and into other “perceived safer” asset classes such as bonds. It is appropriate, therefore, to ask ourselves how an adaptive strategy is dealing with this kind of market environment. In this article we analyze the performance of the simple ETF portfolio, emphasizing its results during the latest period of high market turbulence. For completeness, we will review the historical performance of the portfolio since January 2003, but will discuss in more detail its performance during the first nine months of 2015. The portfolio is made up of the following four ETFs: SPDR S&P MidCap 400 ETF (NYSEARCA: MDY ) PowerShares QQQ Trust ETF (NASDAQ: QQQ ) iShares 1-3 Year Treasury Bond ETF (NYSEARCA: SHY ) iShares 20+ Year Treasury Bond ETF (NYSEARCA: TLT ) Basic information about the funds was extracted from Yahoo Finance and marketwatch.com and it is shown in table 1. Table 1. Symbol Inception Date Net Assets Yield% Category MDY 5/04/1995 14.23B 1.41% Mid-Cap Blend QQQ 3/03/1999 36.93B 0.96% Large Growth SHY 7/22/2002 13.11B 0.48% Short Term Treasury Bond TLT 7/22/2002 6.41B 2.62% Long Term Treasury Bond The data for the study were downloaded from Yahoo Finance on the Historical Prices menu for MDY, QQQ, 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. The portfolios are optimized for three levels of risk: LOW, MID and HIGH. The corresponding annual volatility targets are 5%, 10% and 15% respectively. In Table 2 we show the performance of the strategy applied monthly from January 2003 to September 2015. Table 2. Performance of MVO algorithm applied monthly versus an equal weight portfolio. TotRet% CAGR% VOL% maxDD% Sharpe Sortino 2015 return LOW risk 167.65 8.03 5.60 -5.59 1.43 1.99 3.16% MID risk 399.09 13.45 10.61 -10.34 1.27 1.68 3.60% HIGH risk 697.85 17.70 16.40 -17.18 1.08 1.52 -0.33% Equal weight 204.71 9.14 9.58 -24.50 0.95 1.29 -1.33% Please notice that the realized volatilities are well correlated with the target values. In fact, the realized volatilities are just slightly greater that the target values. Also, as expected, the realized annual returns are also well correlated to the volatility targets. All the values in the CAGR% column are a little greater than the realized volatilities in the VOL% column. The 2015 returns column shows that all MVO strategies performed better than the equal weight portfolio. The LOW and MID risk portfolios achieved a positive return of over 3% while the equal weight portfolio lost 1.33%. The HIGH risk portfolio lost a minute 0.33%. The equity curves for all portfolios are shown in Figure 1. (click to enlarge) Figure 1. Equity curves of the portfolios with MVO monthly optimization and equal weight allocation. Source: All charts in this article are based on calculations using the adjusted daily closing share prices of securities. We see in figure 1 that the equity of the LOW risk portfolio had a constant, very stable, rate of increase over the entire time of the simulation. It was almost unaffected by any market event. By contrast, the equity of the equal weight strategy with rebalancing shows the highest variability and the highest loss during the 2008-09 crises. The equal weight strategy worked quite well during long bullish periods of the market such as during 2003-07 and 2009-14. The MID and HIGH risk strategies worked extremely well during the 2009-14 period with a very brief periods of mild correction in 2011. All strategies show a flattening of their equity curves during 2015. In Figures 2, 3 and 4 we show the time allocation for all MVO strategies from January 2014 to September 2015. We decided to display the allocations over a shorter most recent time interval in order to get graphs that are easy to read. (click to enlarge) Figure 2. In figure 2 we see that the LOW risk strategy allocated, on average, over 60% of the money to the bond funds. About 30% to 40% was allocated alternately to QQQ or MDY. (click to enlarge) Figure 3. In figure 3 we see that in 2014 the money was allocated alternately between TLT and QQQ. The first half of 2015 the allocation went to MDY and TLT. In July and August of 2015 the money was allocated to QQQ and SHY, switching all to TLT and SHY in September and October. (click to enlarge) Figure 4. In figure 4 we see that the HIGH risk strategy allocates the money to a single asset at any time. Since January 2014 it simply alternated between QQQ and TLT. This strategy worked very well most of the time, but in the first nine months of 2015 it suffered a very small loss. In table 4 we show the current allocations for all the strategies. Table 3. Current allocations for October 2015. MDY QQQ SHY TLT LOW risk 0% 0% 69% 31% MID risk 0% 0% 35% 65% HIGH risk 0% 0% 0% 100% As seen in table 3 all portfolios are invested only in bond funds, regardless of risk level. The low risk portfolio in mostly invested in the short term, while the high risk is 100% in long term treasuries. Conclusion The simple ETF portfolio with monthly reallocation performed better than the equal weight portfolio in 2015.The low and mid risk portfolios had good positive returns, while the high risk portfolio had a very small loss. Additional disclosure: The article was written for educational purposes and should not be considered as specific investment advice.
Multi-Factor Investing
Multi-factor investing that combines value, momentum, quality (profitability), or low volatility factors is today’s hot new investment approach. There has been an explosion of multi-factor ETFs recently with nine of the fourteen existing U.S. multi-factor funds coming to market this year, and five of them showing up within the past 60 days. Multi-factor funds may be a good thing, since single factor funds can have some serious drawbacks. However, multi-factor funds can also have their own quirks and issues. If the large variety of factors is thought of as the “factor zoo,” then multi-factor approaches may be the “factor circus” with its own collection of silly clowns, dangerous acrobats, and amusing jugglers. Factor Investing Issues With factor investing in general there are three potential problem areas: tractability, scalability, and volatility. With respect to tractability, it is well-known that value investing can have long periods of serious under performance. This happened in the late 1990s and also somewhat during the past two years. Not all value investors may be willing to watch this happen without losing patience and giving up on their factor portfolios. To a lesser degree, momentum and other factors are also subject to sustained tracking error. Scalability has to do with too much money chasing after too few stocks. Factors perform best when you can focus on those stocks having the strongest factor characteristics. For example, Van Oord (2015) showed that from 1926 through 2014, only the top decile of U.S. momentum stocks outperformed the market. Stocks below the top decile added nothing to strategy results. Yet just two out of the twelve large-cap U.S. equities single factor ETFs only include stocks that are within the top decile of their factor rankings. For example, the oldest and largest single factor value ETFs are iShares S&P 500 Value (NYSEARCA: IVE ), iShares Russell 1000 Value (NYSEARCA: IWD ), and Vanguard Value (NYSEARCA: VTV ). They hold 72%, 69%, and 50% respectively of the stocks that are in their investable universes. This makes them, to a great extent, closet broad index funds with higher fees. Their large sizes ($8.3 billion, $23.5 billion, and $34.6 billion, respectively) may impede them from focusing on just fifty (the top decile of S&P 500 stocks) or one-hundred (the top decile of Russell 1000) value stocks. The same is true with respect to momentum. The largest momentum fund, with over $1 billion in assets, is the AQR Large Cap Momentum Style mutual fund with an expense ratio of 0.45. It holds 532 out of an investable universe of 1000 stocks. This is a far cry from the top decile of momentum stocks. Large amounts of investment capital may make it difficult for single factor funds of all types to focus exclusively on the relatively small number of stocks that appear in their top factor deciles. The third problem for single factor portfolios is increased volatility and high bear market drawdowns that accompany value, momentum, and small cap factors. Trend following filters, such as absolute momentum, can help reduce downside exposure with respect to long-term bear markets, but it does little to alleviate uncomfortable short-term volatility. Trend following is also less effective when applied to value factors than when applied to other factors like momentum. Multi-Factor Solutions All three of these problem areas for single factor investing – tractability, scalability, and volatility – can be significantly reduced by using intelligently constructed multi-factor portfolios. Multiple factors can obviously reduce tracking error, since it is unlikely that several factors will substantially under perform at the same time. As for scalability, if a fund uses four factors instead of just one, it can handle four times the investment capital without eroding its ability to enter and exit the markets. Finally, the volatility and large bear market drawdown associated with value and momentum factors can be reduced by combining these factors with less volatile ones, such as quality and low volatility. However, I intentionally included the words “intelligently constructed” when I referred to the potential benefits of multi-factor portfolios. It surprises me that six out of the fourteen U.S. multi-factor funds include small size as a factor. Sponsors of these funds must have been asleep during the past 25 years when abundant academic research showed that small cap stocks, while giving higher returns, add nothing positive on a risk-adjusted basis because of their high volatility. When combined with value or with value and momentum, which is what all six funds of these funds do, small cap can be particularly undesirable, since it can aggravate already high portfolio volatility and bear market drawdown exposure. It is also surprising that the “premier anomaly,” price momentum, is included in only eight of the fourteen U.S. multi-factor funds. Abundant research has shown that momentum is the most powerful factor for generating positive returns. More sleepy time among fund sponsors? The final issue associated with multi-factor funds is their average annual expense ratio of 42 basis points for what are enhanced index funds. This is higher than the Morningstar US ETF Large Blend Strategic Beta expense ratio of 38 basis points and the Morningstar US ETF Large Blend Index expense ratio of 36 basis points. Until just recently, an investor who wanted multi-factor exposure would have been better off creating it herself by combining the single factor iShares MSCI USA Value Factor, USA Momentum Factor, USA Quality Factor, and USA Minimum Volatility ETFs, since these all have expense ratios of only 15 basis points. New Solution This situation changed dramatically last month when Goldman Sachs entered the ETF business with an offering called Goldman Sachs Active Beta U.S. Large Cap Equity (NYSEARCA: GSLC ). GSLC is the only multi-factor fund having what I consider an optimal mix of factors: value, momentum, quality, and low volatility. Here is a description of how they determine these factors: • Value: The value measurement is a composite of three valuation measures, which consist of book value-to-price, sales-to-price and free cash flow-to-price (earnings-to-price ratios are used for financial stocks or where free cash flow data are not available). • Momentum: The momentum measurement is based on beta- and volatility-adjusted daily returns over an 11-month period ending one month prior to the rebalance date. • Quality: The quality measurement is gross profit divided by total assets or return on equity (ROE) for financial stocks or when gross profit is not available. • Low Volatility: The volatility measurement is defined as the inverse of the standard deviation of past 12-month daily total stock returns. Even though the fund holds 432 stocks out of an investable universe of 500, it uses a weighting scheme (most multi-factor funds with a large number of holdings do the same) that allocates substantially more of its capital to stocks with high factor ratings. GSLC rebalances positions quarterly and uses a turnover minimization technique (especially useful for momentum stocks) of buffer zones to reduce the number of portfolio transactions. I use a similar buffer zone technique myself with some of my more active momentum models. What is especially appealing about GSLC is its low cost structure. The fund came into existence because some of Goldman’s largest clients wanted to invest this way using an ETF wrapper to minimize their tax consequences. Because of this sponsorship, the fund was set up with an annual expense ratio of only 9 basis points. This is the same expense ratio as the biggest and most popular ETF in the world, the SPDR S&P 500 ETF Trust (NYSEARCA: SPY ). GSLC already has $78 million invested in it since coming to market one month ago. GSLC is not an ideal investment from our point of view, since it doesn’t have a trend following filter like absolute momentum to help it avoid severe bear market drawdown. GSLC is also unable to benefit from international diversification during those times when international stocks show greater relative strength than U.S. stocks. However, because of its low cost structure, GSLC might be a good asset to consider along with the S&P 500. If GSLC continues to attract considerable assets so that it has good liquidity and if it performs well relative to the S&P 500 over the next year, I may add GSLC to my dual momentum models. Multi Factor Funds Symbol Factors Assets Stocks Exp Ratio 4 Factor Goldman Sachs Active Beta U.S. Large Cap GSLC Value, Mom, Quality, LoVolty $78 m 432 0.09 ETFS Diversified Factor U.S. Large Cap SBUS Value, Mom, Size, LowVolty $17 m 492 0.40 iShares Factor Select MSCI USA LRGF Value, Mom, Size, LowVolty $5 m 135 0.35 3 Factor SPDR MSCI USA Quality Mix QUS Quality, Value, LowVolty $6 m 624 0.15 JP Morgan Diversified Return U.S. Equity JPUS Value, Mom, Quality $11 m 561 0.29 John Hancock Multifactor Large Cap JHML Size, Value, Profit $79 m 772 0.35 AQR Large Cap Multi-Style (non-ETF) QCELX Value, Mom, Profit $1.2 b 338 0.45 iShares Enhanced U.S. Large Cap IELG Value, Quality, Size $71 m 109 0.18 PowerShares Dynamic Large Cap Value PWV Value, Quality, Mom $927 m 50 0.58 FlexShares U.S. Quality Large Cap Index QLC Quality, Value, Mom $3 m 120 0.32 Gerstein Fisher Multi-Factor Growth Equity (non-ETF) GFMGX Size, Value, Mom $227 m 298 1.03 2 Factor ValueShares Quantitative Value QVAL Value, Quality $47 m 41 0.79 FlexShares Morningstar U.S. Market Factor Tilt TILT Value, Size $740 m 2249 0.27 Cambria Value and Momentum VAMO Value, Mom $3 m 100 0.59 Nothing contained herein should be interpreted as personalized investment advice. 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