Tag Archives: gary-antonacci

Why Does Dual Momentum Outperform?

Those who have read my momentum research papers, book, and this blog should know that simple dual momentum has handily and consistently outperformed buy-and-hold. The following chart shows the 10- year rolling excess return of our popular Global Equities Momentum (GEM) dual momentum model compared to a 70/30 S&P 500/U.S. bond benchmark [1] Results are hypothetical, are NOT an indicator of future results, and do NOT represent returns that any investor actually attained. Indexes are unmanaged, do not reflect management or trading fees, and one cannot invest directly in an index. Please see our Performance and Disclaimer pages for more information. GEM has always outperformed this benchmark and continues to do so now, although the amount of outperformance has varied considerably over time. In 1984 and 1997-2000, those who might have guessed that dual momentum had lost its mojo saw its dominance come roaring right back. In Chapter 4 of my book, I give a number of the explanations why momentum in general has worked so well and has even been called the “premier anomaly” by Fama and French. Simply put, reasons for the outperformance of momentum fall into two general categories: rational and behavioral. In the rational camp are those who believe that momentum earns higher returns because its risks are greater. That argument is harder to accept now that absolute momentum has clearly shown the ability to simultaneously provide higher returns and reduced risk exposure. The behavioral explanation for momentum centers on initial investor underreaction of prices to new information followed later by overreaction. Underreaction likely comes from anchoring, conservatism, and the slow diffusion of information, whereas overreaction is due to herding (the bandwagon effect), representativeness (assuming continuation of the present), and overconfidence. Price gains attract additional buying, which leads to more price gains. The same is true with respect to losses and continued selling. The herding instinct is one of the strongest forces in nature. It is what allows animals in nature to better survive predator attacks. It is built in to our brain chemistry and DNA as a powerful primordial instinct and is unlikely to ever disappear. Representativeness and overconfidence are also evident and prevalent when there are strong momentum-based trends.Investors’ risk aversion may decrease as they see prices rise and they become overconfident. Their risk aversion may similarly increase as prices fall and investors become more fearful. These aggregate psychological responses are also unlikely to change in the future. One can easily make a logical argument for the investor overreaction explanation of the momentum effect with individual stocks. Stocks can have high idiosyncratic volatility and be greatly influenced by news related items, such as earnings surprises, management changes, plant shutdowns, employee strikes, product recalls, supply chain disruptions, regulatory constraints, and litigation. A recent study by Heidari (2015) called, ” Over or Under? Momentum, Idiosyncratic Volatility and Overreaction “, looked into the investor under or overreaction question with respect to stocks and found evidence that supported the overreaction explanation as the source of momentum profits, especially when idiosyncratic volatility was high. A number of economic trends, not just stock prices, get overextended and then have to mean revert. The business cycle itself trends and mean reverts. Since the late 1980s, researchers have known that stock prices are long-term mean reverting [2]. Mean reversion supports the premise that stocks overreact and become overextended, which is what leads to their mean reversion. We will show that overreaction, in both bull and bear market environments, provides a good explanation for why dual momentum has worked so well compared to buy-and-hold. Dual Momentum Performance Earlier we posted Dual, Relative, & Absolute Momentum , which highlighted the difference between dual, relative, and absolute momentum. Here is a chart of our GEM model and its relative and absolute momentum components that were referenced in that post. GEM uses relative momentum to switch between U.S. and non-U.S. stocks, and absolute momentum to switch between stocks and bonds. Instructions on how to implement GEM are in my book, ‘ Dual Momentum Investing: An Innovative Strategy for Higher Returns with Lower Risk’ . Results are hypothetical, are NOT an indicator of future results, and do NOT represent returns that any investor actually attained. Indexes are unmanaged, do not reflect management or trading fees, and one cannot invest directly in an index. Please see our Performance and Disclaimer pages, linked previously, for more information. Relative momentum provided almost 300 basis points more return than the underlying S&P 500 and MSCI ACWI ex-US indices. It did this by capturing profits from both indices rather than from just from a single one. We can tell from the above chart that some of these profits were due to price overreaction, since both indices pulled back sharply following strong run ups. Even though relative momentum can give us substantially increased profits, it does nothing to alleviate downside risk. Relative momentum volatility and maximum drawdown are comparable to the underlying indices themselves. However, we see in the above chart that absolute momentum applied to the S&P 500 created almost the same terminal wealth as relative momentum, and it did so with substantially less drawdown. Absolute momentum accomplished this by side stepping the severe downside bear market overreactions in stocks. As with relative momentum, there is ample evidence of price overreaction here, since there were sharp rebounds from oversold levels following most bear market lows. We see that overreaction comes into play twice with dual momentum. First, is when we exploit positive overreaction to earn higher profits from the strongest index selected by relative momentum. Trend following absolute momentum can help lock in these overreaction profits before the markets mean revert them away. Second is when we avoid negative overreaction by standing aside from stocks when absolute momentum identifies the trend of the market as being down. Based on this synergistic capturing of overreaction profits while avoiding overreaction losses, dual momentum produced twice the incremental return of relative momentum alone while maintaining the same stability as absolute momentum. We should keep in mind that stock market overreaction, as the driving force behind dual momentum, is not likely to disappear. Distribution of Returns Looking at things a little differently, the following histogram shows the distribution of rolling 12-month returns of GEM versus the S&P 500. We see that GEM has participated well in bull market upside gains while truncating left tail risk representing bear market losses. Dual momentum, in effect, converted market overreaction losses into profits. Market Environments We can also gain some insight by looking at the comparative performance of GEM and the S&P 500 during separate bull and bear market periods. BULL MKTS BEAR MKTS Date S&P 500 GEM Date S&P 500 GEM Jan 71-Dec 72 36.0 65.6 – – – Oct 74-Nov 80 198.3 103.3 Jan 73-Sep 74 -42.6 15.1 Aug 82-Aug 87 279.7 569.2 Dec 80-Jul 82 -16.5 16.0 Dec 87-Aug 00 816.6 730.5 Sep 87-Nov 87 -29.6 -15.1 Oct 02-Oct 07 108.3 181.6 Sep 00-Sep 02 -44.7 14.9 Mar 09-Nov15 225.7 89.4 Nov 07-Feb 09 -50.9 -13.1 Average Return 277.4 289.9 Average Return -36.9 3.6 Results are hypothetical, are NOT an indicator of future results, and do NOT represent returns that any investor actually attained. Indexes are unmanaged, do not reflect management or trading fees, and one cannot invest directly in an index. Please see our Performance and Disclaimer pages, linked previously, for more information. During bull markets, GEM produced an average return somewhat higher than the S&P 500. This meant that relative momentum earned more than absolute momentum gave up on those occasions when absolute momentum exited stocks prematurely and had to reenter stocks a month or several months later [3]. Relative momentum also overcame lost profits when trend-following absolute momentum temporarily kept GEM out of stocks as new bull markets were just getting started. Absolute momentum on its own can lag during bull markets, but relative momentum can alleviate the aggregate bull market underperformance of absolute momentum. Relative and absolute momentum therefore complement each other well in bull market environments. What really stand out though are the average profits that GEM earned in bear market environments when stocks lost an average of 37%. Absolute momentum, by side stepping bear market losses, is what accounted for much of GEM’s overall outperformance. Large losses require much larger gains to recover from those losses. For example, a 50% loss requires a subsequent 100% gain to get back to breakeven. By avoiding large losses in the first place, GEM has avoided being saddled with this kind of loss recovery burden. Warren Buffett was right when he said that the first (and second) rule of investing is to avoid losses. Increased profits through relative strength and loss avoidance through absolute momentum are only half the story though. Avoiding losses also contributes greatly to investor peace of mind and helps prevent us from becoming irrationally exuberant or uncomfortably depressed, which can lead to poor timing decisions. Not only does dual momentum help capture overreaction bull market profits and reduce overreaction bear market losses, but it gives us a disciplined framework to keep us from overreacting to the wild vagaries of the market. [1] GEM has been in stocks 70% of the time and in aggregate or intermediate government/credit bonds around 30% of the time since January 1971. See the Performance page of our website for more information. [2] See Poterba and Summers (1988) or Fama and French (1988). [3] Since January 1971, there have been 9 instances of absolute momentum causing GEM to exit stocks and then reenter them within the next 3 months, foregoing an average 3.1% difference in return.

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. Under no circumstances does this information represent a recommendation to buy, sell or hold any security. Users should be aware that all investments carry risk and may lose value. Users of these sites are urged to consult their own independent financial advisors with respect to any investment.

Value And Momentum Are Highly Correlated

One of the most popular research papers on momentum is ” Value and Momentum Everywhere ” by Asness, Moskowitz, and Pedersen. In June 2013, this was published in the prestigious Journal of Finance . I have an earlier blog post which discussed that paper. However, one important item slipped by me then. It was a statement by the authors that value and momentum strategies are negatively correlated. They cited a negative monthly correlation coefficient between value and momentum of -0.24. Asness and his crew have brought up this negative correlation in subsequent writings regarding the merits of momentum and value investing.[1] Other writers and speakers have also been expounding this idea of negative correlation between value and momentum strategies. Long/Short Versus Long Only However, some of us, including myself, did not carefully consider the fact that the Asness et al. study dealt only with long/short momentum and value. This is where you are long high book-to-value and high momentum stocks, while simultaneously short low book-to-value and low momentum stocks. As we will see, the correlations between long/short value and momentum are substantially different than the correlations between long-only value and momentum. The vast majority of the investing world uses long-only rather than long/short portfolios. This applies to both value and momentum strategies. In looking at dozens of mutual and exchange traded funds, I am not aware of any value/growth oriented funds (other than those from AQR using muti-assets or multi-factors) that use a balanced long/short approach. With momentum, I know of only a single public fund [the QuantShares U.S. Market Neutral Momentum ETF (NYSEARCA: MOM ) ] that uses a long/short approach, and it is tiny with only $1.23 million in assets. Therefore, correlations between value and momentum using long/short portfolios are largely irrelevant and may be misleading to most investors. We will show the correlations between U.S. value and momentum stocks using long-only portfolios from the Kenneth French Data Library . We will use the value weighted top one- third of book-to-market value stocks and the top one-third of momentum stocks measured over their prior 2-12 month’s performance during the past 87 years. We will use stocks above the median NYSE in market capitalization. These are the ones that are most commonly traded. By using only large and mid-cap stocks, we avoid the problems associated with micro-cap liquidity. Besides looking at separate value and momentum portfolios, we will also examine a portfolio allocated 50/50 to value and momentum with monthly rebalancing. Our benchmark will be all stocks above the median NYSE market capitalization. No transaction costs or other expenses are deducted. Correlations Here are the monthly correlations from February 1927 to June 2015: MOM VALUE 50/50 MKT MOM 1.00 0.81 0.94 0.90 VALUE 1.00 0.96 0.92 50/50 1.00 0.96 MKT 1.00 The correlations of value and momentum to the market index are 0.92 and 0.90, respectively. As expected, these correlations are very high. What may not be expected is that the correlation between long-only value and long-only momentum is also very high at 0.81. This is dramatically different from the Asness et al. -0.24 monthly correlation between idiosyncratic long/short momentum and value. This difference has important implications for what long-only investors might expect if they invest in both value and momentum. Performance Statistics The return of any blended portfolio is a weighted average of the component returns regardless of the correlations. However, the risk exposure of a blended portfolio can differ greatly based on the correlations between the components. If those components have low or negative correlation, then there should be a substantial reduction in portfolio volatility. However, if the component correlations are strongly positive, as they are here with long-only value and momentum, then there may be little reduction in risk by combining them. We see this is the case looking at results from February 1927 to June 2015: MOM VALUE 50/50 MKT ANN RTN 15.70 15.23 15.46 11.73 STD DEV 19.21 24.75 20.95 20.44 SHARPE 0.59 0.44 0.53 0.38 MAX DD -77.4 -89.0 -83.9 -88.0 Results are hypothetical, are NOT an indicator of future results, and do NOT represent returns that any investor actually attained. Please see our Disclaimer page for more information. The momentum portfolio has the highest return and the highest Sharpe ratio. However, a momentum portfolio of individual stocks also has very high turnover and associated high transaction costs that are not accounted for in the data. See Novy-Marx and Velikov (2014) for an up-to-date analysis of these costs and a review of earlier cost studies. High transaction costs is one reason why I prefer to use momentum with indices and sectors. These work very well with momentum while having substantially lower transaction costs. Value shows almost the same return as momentum and also a higher Sharpe ratio than the large/mid-cap market benchmark.We should understand that if value and momentum had a low or negative correlation, then the standard deviation of a 50/50 mix of value with momentum would likely show a lower volatility than either value or momentum individually. That is not the case here. The standard deviation of the blended portfolio is higher than the standard deviation of the momentum portfolio. It is, in fact, almost identical to the volatility of the market portfolio. Drawdown The same is true with respect to maximum drawdown. The market and value portfolios show around the same maximum drawdown of -88 to -89%. This is based on month-end values. Intra-month drawdowns would be higher. I cannot imagine any investor who would be comfortable losing more than 90% of the value of their portfolio. The maximum drawdown of the momentum portfolio is a little better at -77.4%, but the maximum drawdown of the value/momentum blended portfolio is back up to -83.9%. So should there be value and momentum everywhere? We didn’t think so before, and we don’t think so now, at least not for long-only investors. Momentum without value shows the highest return, highest Sharpe ratio, lowest volatility, and lowest maximum drawdown. But its -77.4% maximum drawdown is still uncomfortably high, and high transaction costs may substantially reduce momentum returns from individual stocks. Summary The way to reduce large downside exposure as well as boost expected returns in the long-run is by using dual momentum as explained in my book and throughout this blog. The absolute momentum component of dual momentum boosts the Sharpe ratios of all the above portfolios and cuts their maximum drawdowns almost in half. Perhaps what we can say is, “Dual Momentum Everywhere!” [1] See reports by the AQR posse, ” Fact, Fiction, and Momentum Investing ” and ” Investing with Style “.