Now that I have turned my book manuscript into my publisher, I can get back to doing some blog posts. My book's title is Dual Momentum Investing: An Innovative Strategy for Higher Returns with Lower Risk. McGraw-Hill will release it in late October.

The AQR posse (Asness, Frazzini, Israel, and Moskowitz) recently issued a working paper that disproves many often-repeated myths about momentum investing, particularly as it applies to individual stocks. The authors back up their reasoning with results from academic papers and publicly available data. Here are the myths they address: 

•   The momentum anomaly is small and sporadic 
• ·It works mostly on the short side
•   It works well only among small stocks
•   It does not survive trading costs
•   It does not work for a taxable investor
•   It is best used as a screen rather than as a regular investment factor
•   Its returns may not persist
•   It is too volatile to rely on
•   Different measures of momentum may give different results
•   There is no theory or reasonable explanation to support it

 Below is a quick summary of the authors' evidence-based counter arguments:
1)  There is overwhelming evidence from scores of studies showing that momentum returns are remarkably stable and robust.
2)  There is little difference in performance between the long and short sides of momentum based on factor model regressions. Based on average returns versus the market, the long side has contributed more to momentum profits.
3)  As for working only among small caps, this is true only if you replace the word "momentum" with the word "value". Two of the authors, Israel & Moskowitz, wrote an important paper last year called "The Role of Shorting, Firm Size, and Time on Market Anomalies" that clearly showed this. That paper was the subject of my post, "Momentum...the Only Practical Anomaly?"Momentum works well across all size stocks.
4)  A study last year by three of these authors called "Trading Costs of Asset Pricing Anomalies"looked at large institutional trades across nineteen developed markets from 1998-2013. They found the trading costs of momentum to be low, despite a higher turnover than from other factors. [1]
5) Several studies show that even though momentum with individual stocks has 5-6 times the annual turnover of value strategies, momentum actually has a similar tax burden. This is because momentum holds on to winners and sells losers, which avoids short-term gains in favor of long-term ones. Momentum also has a lower dividend exposure than value.
6)   The authors point to papers showing that momentum works better as a factor-based approach than as a screen-based one.
7)  As for momentum's returns disappearing, one can say the same of any anomaly. Abnormal momentum returns have survived, however, for the past 200+ years. Momentum has held up to considerable out-of-sample validation across time, geography, and asset type. The authors point out, "There is no evidence that momentum has weakened since it has become well-known and once many institutional investors embraced it and trading costs declined."

9)  The authors agree that different measures of momentum can give different results, but they point out that this is true of any strategy. They say that different measures of momentum giving good results are a sign of robustness and not a cause for concern.
10)Momentum can be explained by either risk based or behavioral factors. As long as risks, risk preferences, biases, and/or behaviors do not change, momentum profits should continue unabated as they have for the past 200+ years. (My forthcoming book shows how behavioral biases are part of our DNA and are unlikely to change.)
The authors point out that most of the above myths can be shattered by a quick visit to Kenneth French's data library website. It is refreshing to see the authors, brought up in the Chicago efficient markets tradition, take on the challenge of those who say momentum profits cannot persist (despite plenty of evidence to the contrary) because that would contradict the theory of efficient markets. The authors point out that rejecting data because of a theory (or a one-sided view of the world) can be dangerous. They point to Columbus, Galileo, and the Salem witch trials as examples. Bravo!

The only problem I have with their paper is that the authors, perhaps keenly aware that risk-adjusted momentum profits from individual stocks have been uninspiring over the past thirty years, repeatedly point out that momentum works best when it is combined with value because of low or negative correlations between the two. These low correlations were determined using long/short portfolios of both value and momentum. Very few actually invest that way. Long only investing is different. Also, we can see in the aforementioned Israel & Moskowitz study that commonly used measures of value hold up only among the smallest stocks that represent only 10% of total market capitalization, and these are impractical for most investors to hold. Maybe the AQR crew needs to take a closer look at the true value of the emperor's new clothes.

[1] The authors used a relatively small proprietary data set of long/short momentum portfolios. Lesmond et al. and Korajczyk and Sadka found that transaction costs could negate much of the profit from momentum using more focused stock portfolios.

Most academic research on momentum deals with individual stocks. Most applications of momentum are also oriented toward individual stocks. The three largest publically offered momentum programs (AQR momentum mutual funds, PowerShares DWA Momentum ETFs, and iShares MSCI USA Momentum Factor ETF) all use individual stock momentum. The only widely-available public program using momentum applied to asset classes was the ALPS Goldman Sachs Momentum Builder, which recently went out of business due to lack of interest.

Yet momentum applied to individual stocks is not the ideal way to use momentum. High transaction costs can negate much of the benefit of momentum investing, and most stock momentum programs dilute the momentum effect by selecting hundreds of stocks instead of just the ones showing highest relative strength. Momentum applied to indexes or sectors, rather than individual stocks, can capture high momentum profits with much lower transaction costs.

Here is a table from my new book Dual Momentum Investing: An Innovative Approach to Higher Returns with Lower Risk. This table shows the performance of the AQR Momentum Index composed of the top one-third of the 1000 highest capitalization U.S. stocks based on 12-month relative strength momentum with a one-month lag. AQR weights their index positions based on market capitalization and adjusts the positions quarterly. For comparison, we show the performance of the Russell 1000 index and from applying absolute momentum to the Russell 1000 by moving into aggregate bonds whenever 12-month absolute momentum is negative.

	AQR Momentum Index [1]	Russell 1000Index	Russell 1000 w/Abs Momentum
Annual Return	15.14	13.09	15.92
Annual Std Dev	18.27	15.51	12.57
Annual Sharpe	0.51	0.49	0.80
Max Drawdown	-51.02	-51.13	-23.41

	AQR Mom Index	Russell 1000 Value Index	50/50 AQR Mom with Value	Russell 1000 w/Abs Mom
Annual Return	15.14	13.52	14.33	15.92
Annual Std Dev	18.27	14.87	15.71	12.57
Annual Sharpe	0.49	0.53	0.55	0.80
Max Drawdown	-51.02	-55.56	-51.47	-23.41

	GEM	GEM w/Value
Annual Return	17.43	17.24
Annual Std Dev	12.64	12.52
Annual Sharpe	0.86	0.86
Max Drawdown	-22.72	-22.94

1) Value, as measured by the price-to-book ratio, is beneficial only with small stocks and not with large stocks. This is the same conclusion reached by Israel and Moskowitz who used data back to 1926, and who also found it to be true of other valuation measures that had data back to at least the 1930's.

2) Despite high momentum portfolio Sharpe ratios before transaction costs, the high transaction costs associated with momentum portfolios negates much of the difference in Sharpe ratios between large momentum and large value portfolios.

 3) Since small stocks have even higher transaction costs than large stocks, the authors incorporated higher transaction costs to conclude that none of the small momentum portfolio Sharpe ratios are higher than the Sharpe ratios of the small market portfolios.

In other words, based on high transaction costs, individual stock momentum may not be very good with either small or large stocks [4]. So all we are left with that provides above market risk-adjusted returns are small value stocks that most investors (and particularly institutional ones) will find too expensive and difficult to trade. 

Researchers estimate that the worldwide cost of investment management is approximately $3 trillion per year. Some of this expense is unavoidable, such as the costs associated with custodial fees and for the periodic re-balancing of portfolios.

However, most of this high expense is in the form of compensation paid to the managers of actively managed mutual funds, hedge funds, and other managed programs. What do investors have to show for this large transfer of wealth from themselves to active money managers? The answer, unfortunately, is "not much." My book, Dual Momentum Investing: An Innovative Strategy for Higher Returns with Lower Risk, reveals an abundance of research that confirms the general lack of value-added from active investment management.

Lack of performance is due not just from the higher fees and transaction costs associated with active investment management. Institutional investors use disadvantageous periods for the evaluation and selection of their investments. Goyal and Wahal (2008) show that investment managers and their consultants tend to select investments based on performance over the prior three or so years. Yet momentum research papers show 3 to 5 years to be a relatively poor period for performance evaluation. Equity performance tends to be mean reverting over that time frame. A one-year relative evaluation period gives better results. Vanguard issued a research note last July also documenting poor future performance based on a past 3- year evaluation period.

Making matters worse, yearly reports from Dalbar Inc., a market research firm, indicate poor timing decisions on the part of the investing public. For example, the average US equity investor achieved an annualized return of 5.0% over the past 20 years ending in 2013, which is 4.2% less than the 9.2% average annualized return of the S&P 500.

Investors are emotionally influenced by market volatility when getting into and out of the markets. Equities have provided investors with the highest risk premium, but they also have been subject to high volatility and extreme drawdown, since few investors have been aware of the risk-reducing benefit of absolute momentum. When investors instead try to dampen volatility through diversification with fixed income or alternative investments with lower inherent risk premiums, they also dampen their long-run expected return.

Some try to boost returns by looking for more of an edge from their equity investments. Historically, investors have used value and small cap portfolio tilts in their attempts to achieve higher risk-adjusted returns.

Yet the latest research shows that small size and high value portfolios may not always provide the higher risk-adjusted returns that investors have been seeking (See our post "Momentum...the Only Practical Anomaly?") Momentum, on the other hand, does provide a proven edge, especially when dealing with indexes rather than individual stocks and when using both absolute and relative momentum together (dual momentum). Unfortunately though, the most popular momentum-based programs use only relative strength momentum, and they apply it to individual stocks, which necessitate higher transaction costs.

I know of only one public program that uses dual momentum applied to asset classes. However, their fees are very high, and their portfolio choices leave much to be desired (My book also goes into considerable detail about asset selection, especially with respect to momentum-based portfolios.)

In the future, when the advantages of dual momentum become better known, there may be other dual momentum investment opportunities. However, they may still have fees that are too high, portfolios that are less than ideal, or models based on too little data. The biggest mistakes I see others make are using models that overfit the data and drawing conclusions based on limited amounts (typically around 15 years) of data.

To give investors a better chance to earn decent risk-adjusted returns, my new book fully discloses my simple Global Equity Momentum (GEM) model (see the Performance page of my website) and shows how to easily use it. GEM has performed well over 40 years of past data under different market conditions using the same approach validated in numerous academic research papers. It has also avoided most bear market equity erosion. For only the cost of a book, any investor can easily utilize GEM to benefit from dual momentum while using a sensible, minimal expense portfolio.

Believe it or not, I was once a value investor. From an early age I was impressed with the long-run success of such value luminaries as John Neff, Bill Ruane, Walter Schloss, and Max Heine. Being of a contrary temperament, I also liked the idea of buying stocks that were out-of-favor and ignored by the multitudes. Supporting this approach, DeBondt and Thaler (1985) presented evidence that stocks overreact to bad news, and that poor performers over the past 3 to 5 years tend to outperform when moving forward. Value investing seemed to have representative bias going for it, whereby investors overreact to poor performance and project it far into the future while failing to account for long-run mean reversion.

In 1992, Fama and French presented their groundbreaking paper indicating that value and small cap stocks offered up a risk premium to investors. I, along with the rest of the civilized world, readily accepted this idea. In 1995, Kothari et al. discovered that the the Fama and French results may have been due to sample selection bias. Using a different data source, Kothari et al. found no significant evidence of a positive relationship between value and average returns. However, since Kothari et al. were challenging the work of the highly respected Fama and French, their discovery got very little attention.

What got me away from value wasn't anything about value itself, but rather was my discovery, after reading a plethora of research papers on momentum, of just how strong momentum is compared with anything else. Looking at relative strength momentum applied to individual stocks showed a higher premium. What I also found is that you can easily apply momentum to different asset classes in order to gain the benefits of diversification, while applying value to asset classes other than equities can be rather speculative and uncertain.

What also got me excited about momentum though was my own research showing that momentum can be applied not just across different investment opportunities, but to single investments themselves in the form of trend-following absolute momentum.  Absolute momentum not only enhances returns like relative momentum, but it can also dramatically reduce drawdown. As far as I could tell, no other anomaly could do this.

Reinforcing my view were the surprising results with respect to value in the research  last year by Israel and Moskowitz (See my post "Momentum…the Only Practical Anomaly?"). Working with U.S. equities data back to 1926, the authors said:

The value premium… is largely concentrated only among small stocks and is insignificant among the largest two quintiles of stocks (largest 40% of NYSE stocks). Our smallest size groupings of stocks contain mostly micro-cap stocks that may be difficult to trade and implement in a real-world portfolio.

So value, as popularly derived, was of limited practical benefit to investors. On another front, Chen et al. (2011) proposed an asset pricing model in which investment and profitability are the main explanatory variables, rather than value and size. Fama and French (2014) then expanded their established three-factor model to include investment (expected future changes in book equity) and profitability (expected future net income relative to book assets). When doing so, they concluded that value was made redundant.

VALUE METRICS

So did all this put a nail in the coffin with respect to value investing? Not necessarily. Israel and Moskowitz had looked at value using the popular book-to-market (or price-to-book) ratio. They found similar results using other value measures, such as dividend yield and long-term reversals that had data going back to at least the 1930s. However, these were only singular measures of value.

Dhatt et al. (2001) found that composite measures of value were superior to any individual metric. Moreover, in their book Quantitative Value: A Practitioner's Guide to Automating Intelligent Investment and Eliminating Behavioral Errors, Gray and Carlisle identified a valuation metric based on enterprise multiple, defined as total enterprise value (TEV), divided by earnings before interest, taxes, depreciation, and amortization (EBITDA). Here is a table of valuation metric comparisons by Gray using equal weight portfolios sorted into quintiles. Data is from 1971 through 2010 and excludes micro caps. Perhaps use of the enterprise multiple instead of price-to-book (P/B) could restore confidence in the value premium? The folks at AQR have also tried to improve upon value by incorporating more timely information into the equation.


VALUE AND QUALITY

Some researchers have found that adding a financial strength or quality metric may improve the risk-adjusted results of value portfolios. Paying attention to these additional factors can help overcome the problem of the "value trap," which happens when stocks remain depressed (and may go bankrupt) because their poor fundamentals warrant it. We should keep in mind though that there is always the potential of overfitting and selection bias when combining multiple factors.

Using global data from 1988 through 2012 and U.S. data from 1963 through 2012, Kozlov and Petajisto (2013) found that going long stocks with high quality earnings (based on high return on equity, high cash flow, and low leverage) and short stocks with low quality earnings gave higher Sharpe ratios than a similar value only strategy. They also found earnings quality to be negatively correlated with value. The best Sharpe ratio came from combining high quality with value, since there were significant diversification benefits.

Using a different approach, Piotroski and So (2012) came up with a multi-factor scoring method (F-Score) to measure a firm's financial strength. This method was positively correlated with profitability and earnings growth. Piotroski and So found that strategies formed jointly on F-Score and value outperformed traditional value only strategies.

Novy-Marx (2012) found he could simplify the quality factor to just gross profitability, defined as revenues minus cost of goods sold, scaled by assets. Novy-Marx (2013) found on U.S. stock data from 1963 through 2012 that profitable firms generated significantly higher returns (0.31% per month) than unprofitable firms, despite having higher price-to-book ratios. Novy-Marx (2012) also found that joint strategies combining value with Piotroski/So's F-Score, Greenblatt's magic formula, or gross profitability outperformed traditional value, with profitability plus value being the strongest combination.




WHAT TO DO

Despite the above potential enhancements to value investing, given the enormous advantages of dual momentum investing, if I were ever to get the urge to do value investing, I would just lie down until the urge went away. However, if I were actually going to add a value component to my portfolio, it would need to be calculated on a more robust basis than price-to-book, and it would probably need to be combined with profitability/quality to mitigate the potential value trap problem. More specifically, here is what I might look for in a value fund:

1)    It should combine value with quality and/or profitability screens.
2)    It should determine value based on multiple value metrics and/or a value metric that incorporates the enterprise multiple.
3)    It should not dilute returns by having an overly broad portfolio. In Chapter 6 of my new book, Dual Momentum Investing: An Innovative Strategy for Higher Returns with Lower Risk, I show that many so-called smart beta funds are like closet index funds with modest stylistic tilts. Unfortunately, most value funds are the same. Even though the value effect is more pronounced in the top 10-20% of value rated stocks, most funds dilute this effect by using the top third or top half of value stocks instead of the more profitable top 10-20%.
5)    The expense ratio should be reasonable. (This is true of all investments.)
6)    For taxable accounts, ETFs are preferred over mutual funds and hedge funds. Capital gains usually occur only when you sell your ETF holdings, whereas mutual funds have yearly taxable distributions of capital gains.

CANDIDATES

If value and quality actually do well together, it is natural to think of actively managed mutual funds when considering investment candidates based on such factors. This is because most active managers use fundamental analysis (which takes into account profitability in the form of financial strength), managerial acumen, competitiveness, quality of earnings, and other judgmental factors, in addition to valuation.

However, most active managers lack transparency. This gives them the appearance of possessing proprietary knowledge that may be worth paying a premium to access, but it also means it is often difficult to tell if they meet the first two criteria listed above. In addition, active mangers usually charge high fees. The Morningstar average large cap value fund annual expense ratio is 1.16%. Mutual funds also have a drag on their performance because of their reserves that are held for redemption. Also, as mentioned above, these funds are generally not tax efficient.

There is one mutual fund, however, that may be worth looking at more closely. It is the AQR Core Equity Fund (QCELX).[1]  This fund meets our first two requirements above, since it uses three indicators of profitability along with five indicators of value. For good measure, it adds three momentum indicators. QCELX also has a reasonable annual expense ratio of 58 basis points. However, it is not tax efficient, although AQR has plans for a more tax efficient version of the fund. On the negative side, QCELX currently holds 413 stocks, which is more than 40% of the stocks in its 1000 mid/large cap stock selection universe.[1] Investors are paying a high fee for the index portion of this fund.

In the ETF realm, until this past week there was only one fund that qualified using the above criteria. It was the PowerShares Dynamic Large Cap Value ETF (PWV) based on the Intellidex methodology. PWV uses a ranking selection method with four indicators of value and four indicators of growth. They subtract their value from their growth rankings and then select the largest negative scores. This process automatically gives PWV some profitability exposure, according to the research of Bridgeway Capital Management. Bridgeway found that a multi-indicator value approach (adding price/cash flow, price/earnings, and price/sales) provides greater exposure to gross profitability than a portfolio based only on price/book.

To help further with profitability exposure, after doing their basic screens, PWV adds weightings for price momentum, earnings momentum, quality, and management action. PWV selects the top 20% of the largest 250 stocks from a potential universe of 2000. This gives them a focused portfolio of 50 stocks that PWV rebalances quarterly. (I would prefer that they derived their portfolio of 50 stocks by selecting the top 10% of the largest 500 stocks instead of the top 20% of the 250 largest stocks.) They allocate half their capital equally to the top 15 ranked stocks, and the other half of their capital is divided equally among the remaining 35 stocks. Their annual expense ratio is a reasonable 58 basis points. PWV's performance since inception has been attractive compared to the iShares S&P 500 Value ETF.

                                                  Past performance is no assurance of future success.

This week another ETF got on to the short list of qualified candidates. The new kid on the block is the Value Shares U.S. Quantitative Value ETF (QVAL) offered by Alpha Architect. QVAL incorporates quality in several ways. First, it has forensic accounting screens to avoid firms at risk for financial distress or manipulation. Then it filters for financial strength using a modified Piotroski/So F-Score. Finally, QVAL checks for sound business fundamentals through what it calls an "economic moat." This is a screen for firms having sustainable competitive advantages, ala Warren Buffett.

QVAL's management uses the enterprise multiple to determine value. They select  the top 10% of their large cap universe based on value, then drop the bottom half of these based on quality. The remaining 50 stock portfolio is equal weighted and rebalanced quarterly. QVAL's annual expense ratio of 79 basis points is higher than PVW and QCELX, but QVAL is the most focused fund among the three, selecting only the top 5% of quality/value stocks in their mid/large cap universe, compared to PVW's 20% and QCELX's 40%..

WHAT CAN GO WRONG

Value investing in general poses risks that all investors should be aware of. One of these is high tracking error relative to the overall market. Value can go through sustained periods of under performance, such as during the 1990s. From 1994 through 1999, value underperformed growth by over 10% per year! With focused portfolios of just 50 stocks, PVW and QVAL can potentially have much higher tracking error than other value funds. Value investors need to have a long-term investment horizon and a high tolerance for prolonged periods of underperformance.

Value investing with focused portfolios, such as those of PWV and QVAL, is also subject to high volatility and extremely high maximum drawdowns. Investors should be prepared for these as well.[2] Unfortunately though, investors sometimes lose sight of this during bull markets. They panic and act counter to their best interests when confronted with severe drawdowns once bear markets arrive. In a survey of its members since 1988, AAII found that the highest weight to cash and the lowest weight to equities was in March 2009, right at the bottom of the worst bear market since the 1930s.

There is a way, though, that one may mitigate this harmful behavior. It is by using absolute momentum. (You didn't really think I would write a long post like this without mentioning momentum, did you?) My research paper on absolute momentum and my new book show how to use trend following absolute momentum to reduce the expected drawdown of most any investment opportunity. The chart below of absolute momentum applied to the S&P 500 illustrates this. One could incorporate value into a dual momentum-based model, such as the one featured in my new book. In fact, given the high potential downside exposure associated with value investing, I would never employ it without a trend-following filter such as absolute momentum. Even so, there may still be long periods of underperformance relative to one's benchmark and high short term volatility.

Today is the official release day of  Dual Momentum Investing: An Innovative Strategy for Higher Returns with Lower Risk. I wrote the book to help as many people as possible earn attractive returns and minimize bear market drawdowns.

 Here is an excerpt from a thoughtful review by Reading the Markets:

Antonacci's extensive research and his clear-headed thinking have led to a book that every investor should read. The academically oriented reader will be grateful for his occasional excursions into the weeds, his carefully laid-out data, and his lengthy bibliography. The practically oriented investor will find a road map for moving ahead and staying out of really big trouble ...This one's a keeper!

Scott's Investments wrote:

Dual Momentum Investing: An Innovative Strategy for Higher Returns with Lower Risk is a must-read for individual investors and financial professionals…Antonacci has done the heavy lifting for his readers by thoroughly researching the history and data behind momentum investing. The result is a well-researched and overwhelming argument for momentum investing. Readers are rewarded with a simple, robust strategy that anyone can implement.

And from Alpha Architect...:

Antonacci demonstrates returns to Dual Momentum and the empirical evidence through extensive backtesting across multiple decades; the analysis includes various risk metrics (returns, standard deviations, Sharpe, drawdowns, etc.), and robustness studies, the interpretation of which he explains in detail, so that the reader can have an informed view of the data.

The evidence culminates in a simple but powerful applied momentum model: Antonacci’s Global Equities Momentum (GEM) strategy, which uses these dual momentum ideas to tactically allocate across and among domestic and international equity and bonds. And the results are nothing short of spectacular: superior returns, with low volatility.

The Kindle version of the book was released on October 9, so there are also a dozen reader reviews on Amazon, as well as endorsements from prominent industry professionals. Click here to read these, find out more about the book, or to order it now.

Dead or dying academic ideas latched on to by unwary institutional investors litter the investment graveyard landscape. My new book, Dual Momentum Investing: An Innovative Strategy for Higher Returns with Lower Risk, describes some of these, such as the small cap premium, portfolio insurance, and portfolio diversification with passive commodities. Most of these occurred because of incomplete information or omitted variables. 

Shortly after Rolf Banz published a paper based on his University of Chicago PhD dissertation that identified a small cap premium from 1936 through 1975, Dimensional Fund Advisors (DFA) and others quickly tried to exploit this "small cap anomaly." They learned later that this apparent anomaly was perhaps driven by a mistake in how researchers treated missing data for delisted stocks, many of which were small caps. As noted in my book, studies since then have shown that the small cap premium by itself may no longer exist and may never have existed.

Portfolio insurance was based on the elegant idea that you could synthesize protective options through a combination of stock buying and selling combined with short-term borrowing and lending. You could "insure" your portfolio using these synthetic options. However, what portfolio insurers failed to take into account was the short- term mean reverting nature of stocks. At that time, most academics thought stocks followed a random walk and had little or no auto-correlation, even though for many years stock exchange specialists made handsome mean reversion profits by trading against public order flow. Portfolio insurers quietly packed up their bags and disappeared not long after they first appeared.

In the mid-2000s, academics published studies showing that passive commodities could be a decent portfolio diversifier. Investors jumping on to that bandwagon failed to realize that very large inflows of speculative capital from institutional investors could eradicate the premium that had previously been flowing from hedgers to speculators. Front-running costs of over 3% annually from simultaneous rolling over index future contracts would also take its toll on speculative investor profits. After half a dozen years, there were new research papers showing that adding commodities to a stock/bond portfolio was no longer beneficial.

It may very well be that relative strength momentum applied to individual stocks is the latest academic concept doomed to failure because of investors blindly following academics without seeing the bigger picture. Many academic studies of momentum ignore transaction costs, which can be significant.

A trading costs study by Frazzini et al. (2012) of AQR, covering 13 years of data in 19 developed markets,stated that "...the main anomalies to standard asset pricing models [including momentum] are robust, implementable, and sizeable." The authors were looking at long/short momentum using proprietary data over a relatively short period using broad range of  large cap momentum stocks.Lesmond et al.(2004) found that bid/ask spreads were much higher for momentum stocks, and they concluded that "… the magnitude of the abnormal returns associated with these trading strategies [stock momentum] creates an illusion of profit opportunity when, in fact, none exists."Korajczyk and Sadka (2004) found the profit breakeven allocation  to be only $2-5 billion forlong-only strategies using the top 10% of momentum stocks.

Transaction costs, however, are only half the story. Academic researchers validate cross-sectional relative strength momentum by looking at winners versus losers and segmenting the stock market into deciles, quintiles, quartiles, or terciles. According to Siganos (2007), beyond the first few extreme winners and losers, there is a continuous decline of momentum gains from larger momentum portfolios. Siganos found maximum momentum returns using a portfolio limited to the 40 top and bottom performing stocks.

Yet all publicly available stock momentum funds so far use more than 40 stocks, and some use ten times more than that! The iShares MSCI USA Momentum Factor ETF holds 125 stocks, and the PowerShares DWA Momentum Portfolio has 100 holdings. The AQR Momentum Fund, AQR Small Cap Momentum Fund, and AQR International Momentum Fund hold 479, 953, and 440 stocks, respectively. These represent nearly 50% of the underlying indexes from which these momentum funds draw their holdings. 

The annual expense ratios of AQR's momentum funds range from 0.50 to 0.90%. In contrast to this, you can invest in the Russell 1000 broad-based index from which AQR draws their dilute momentum holdings for a cost of only 0.11% per year. When you add in another 0.7% per year that AQR estimates as transaction costs for their quarterly rebalanced large/midcap momentum index, it raises serious questions about how investors can really capture momentum profits from individual stocks.

Yet, as they say, the proof is in the pudding. In Chapter 6 of my new book, I show readers a simple way to find style-based alternatives to so-called smart beta funds. In many cases, style-based ETFs with lower expense ratios and lower transaction costs offer similar or better performance than their "smart beta" counterparts.  I thought it would be interesting to use the same technique to look at lower cost style-based alternatives to the largest and longest existing publicly available funds that use momentum applied to individual stocks. 

The following comparative charts using momentum funds that can be accurately matched up with stylistic index funds begin at each momentum fund's inception date using the lowest cost class of momentum shares.
The stylistic equivalent fund to the AQR Momentum Fund (AMOMX), with an annual expense ratio of 0.40%, is the Vanguard U.S. Large Cap Growth ETF (VUG), with an expense ratio of .09%.


The stylistic equivalent fund to the PowerShares DWA Momentum Portfolio (PDP), with an annual expense ratio of 0.65%, is the Vanguard U.S. Mid Cap Growth ETF (VOT), with an expense ratio of .09%
.
 
The stylistic equivalent fund to the AQR Small Cap Momentum Fund (ASMOX), with an annual expense ratio of 0.60%, is the Vanguard U.S. Small Cap Growth ETF (VBK), with an expense ratio of .09%.


The stylistic equivalent fund to the AQR International Momentum Fund (AIMOX), with an annual expense ratio of 0.55%, is the iShares MSCI EAFE Growth ETF (EFG), with an expense ratio of .40%.


The above charts give some evidence of why I am not a fan of using momentum with individual stocks. It should also be mentioned that relative strength stock momentum does little or nothing to reduce portfolio drawdown. To accomplish that, you would need something like absolute momentum and/or cross asset diversification. In terms of both risk and return, momentum is more effective when it is used with asset classes or broad indexes having lower transaction costs, and when it incorporates trend-following absolute momentum, as described in my book.

Most of us learned long ago that diversification is a good thing. In fact, it is often called the closest thing to a “free lunch” in the world of investing. This is because, when used wisely, diversification can reduce portfolio volatility with little or no diminution in return. But the key phrase is “when used wisely.”

Working with individual stocks, diversification is important in reducing company-specific (idiosyncratic) risk that comes from earnings surprises or other bad news that can adversely affect individual companies. A carefully selected (no strong biases) portfolio of 50 or more stocks will diversify away most idiosyncratic risk. The main benefits from additional diversification are reduced benchmark tracking error and an increased ability by active managers to handle larger amounts of capital.

Investment managers often want to reduce tracking error for reasons of job security and to trade larger amounts of capital to receive more compensation. But from an investor’s point of view, larger portfolios are no better than smaller ones, once you eliminate most idiosyncratic risk. Larger portfolios may, in fact, be worse than smaller ones in terms of offering up profit opportunities. Active managers might better serve their clients’ interests by having more focused portfolios of their best holdings rather than diluting their portfolios with less attractive issues. Investors wanting broader-based portfolios can purchase less costly index funds.

Over diversification is also a problem for momentum investors because studies show that momentum profits are highest in the most concentrated momentum ranked cross-sections of the market. Top momentum deciles outperform top momentum quintiles, which outperform top momentum terciles. Yet, as I point out in my blog post “Individual Stock Momentum – that Dog Won’t Hunt”, there are some momentum funds that own nearly half their benchmark universe of individual stocks. Investors in those funds are paying for what amounts to an index fund plus a modest exposure to momentum.

Over diversification can also be a problem with respect to asset class momentum. To better understand this, you need to consider how investors earn their profits. Investors are compensated for giving up use of their capital (which earns them the risk-free rate) and for bearing risk (which earns them a risk premium above the risk-free rate).

Companies receive and employ invested capital for productive purposes when equity investors become beneficial owners of these companies. Stockholders share in the fortunes or misfortunes of such companies and are compensated with a relatively high risk premium. In fact, among all investment opportunities, stocks (especially U.S. stocks) have historically offered the highest risk premium. Those who have trouble accepting the evidence of this that I present in my book, Dual Momentum Investing: An Innovative Strategy for Higher Returns with Lower Risk, should read Stocks for the Long Runby Jeremy Siegel. He devotes his entire book to that subject.

Bonds also provide a risk premium, but one that is substantially lower than stocks because bond investors have a senior claim on company assets and are guaranteed a return of capital when their bonds mature. It is uncertain what kind of risk premium, if any, investors in assets other than stocks and bonds receive. For example, investors in aggregate commodity futures (a zero-sum game, less transaction costs) once received risk premium from commercial interests looking to hedge their business risks by using those markets. But with the proliferation of speculative commodity trading, as well as a substantial number of institutions adding passive commodities to their portfolios, that risk premium has largely vanished. One might sometimes still earn short-term speculative trend following profits from assets other than stocks and bonds, but the odds are much better having a proven risk premium behind you as a tailwind.

The main reasons investors continue to hold assets other than stocks and bonds is the mistaken belief that more is always better with respect to diversification and that holding less correlated assets will lessen portfolio volatility and reduce bear market exposure.

However, many markets that were once normally non-correlated now move together under economic stress. Diversification can then fall short when it is needed the most. With increased globalization, the world is now much more inter connected, and widespread diversification is no longer as useful as it once was in reducing downside risk exposure. What is useful for that purpose is trend-following absolute momentum, which has shown the ability to both enhance returns and reduce downside exposure among different assets going back to the turn of the last century.[1] The effective downside protection offered by absolute momentum is all the more reason why over diversification is unnecessary for momentum investors.

A better approach, as presented in my book, is to invest in stocks when they are strong, according to absolute momentum, in order to capture the highest amount of risk premium.[2]  When stocks are weak, you can switch to bonds, which offer a more modest risk premium than stocks. Since the stock market is a leading economic indicator, a weak stock market can indicate a future economic slowdown, declining interest rates, and a healthy bond market. So stocks and bonds may complement each other at the most appropriate times. This is a more effective approach than having a permanent allocation to both.

Diversification into asset classes with lower risk premiums dilutes long-run returns and leads to investment mediocrity. Stocks and bonds are all one really needs for effective investing, especially momentum-based investing.

[1] See "A Century of Evidence on Trend Following Investing" by Hurst, Ooi, and Pedersen.
[2] Our Global Equities Momentum (GEM) model stays mostly in U.S. stocks, where risk premium has been the highest. It switches to non-U.S. stocks when the odds shift in their favor according to relative strength momentum. For validation of this switching approach, see http://awealthofcommonsense.com/avoiding-recency-bias-foreign-stock-markets

Momentum is most commonly applied to stocks. But it works just as well, if not better, when applied to bonds. Our Dual Momentum Fixed Income (DMFI) model switches monthly between the strongest one of the following indexes: Barclays Capital U.S. Credit Bonds, Barclays Capital U.S. Corporate High Yield Bonds, Barclays Capital U.S. Mortgage Backed Securities, and 90 day U.S. Treasury bills.

	HI YIELD	CREDIT	MBS	TBILLS	DMFI
Annual Return	9.8	8.6	7.9	4.1	12.0
Annual Std Dev	8.5	5.5	4.0	0.8	5.8
Sharpe Ratio	0.73	0.94	1.13	1.10	1.43
Max Drawdown	-33.3	-7.3	-7.8	0	-5.7
% of DMFI Profits	59	20	14	7	100
% of Occurrences	47	24	16	13	100
Avg Credit Rating	B	BBB	AAA	AAA	*
Avg Yrs Duration	4.5	7.1	0.3	0.3	*

The more common type of momentum, based on relative strength or cross-sectional momentum, has little or no ability to reduce bear market drawdown. It may even increase volatility and downside risk. As I show in my book, Dual Momentum Investing, using both absolute and relative momentum together is the best approach in that it lets you benefit from the return enhancing characteristics of both types of momentum while incorporating the risk reducing benefits of absolute momentum.

But absolute momentum has possible uses on its own for those who simply want to limit the potential downside risk and enhance the expected return of single asset or fixed asset portfolio. That is why I wrote the paper, “Absolute Momentum: A Simple Rule-Based Strategy and Universal Trend-Following Overlay.” In it, I show how absolute momentum can be applied to a number of different indexes and assets, as well as to some common portfolio configurations, such as balanced stock/bond or simple risk parity portfolios.

Absolute momentum is easy to calculate and apply. It is positive if an asset’s excess return (return less the Treasury bill rate) over a specified look back period is positive. One then holds that asset until absolute momentum turns negative. If absolute momentum is negative, then one stands aside.

In my research papers, I use data going back to January 1973, since bond index data began at that time and international stock index data began close to it in January 1970. In my book, I also use January 1973 as the start date for my analysis, since my book’s featured Global Equities Momentum (GEM) model relies on the same fixed income and international stock indexes. Those wanting to see additional momentum result history can consult the references I give in the book showing attractive profits from relative strength and absolute momentum back to 1801 and 1903, respectively.

However, I now think it would be a good idea now to extend my back testing of absolute momentum, since some investors are especially attracted to absolute momentum for several reasons. First, absolute momentum trades less frequently then dual momentum, which may be important for taxable accounts, even though higher profits are usually earned using dual momentum. Absolute momentum applied to just the U.S. stock market can give mostly long-term capital gains. The second reason absolute momentum may be worth looking at in more depth is that some investors have only a single investment approach that they are comfortable using. They may want to hold a portfolio that focuses solely on value plus profitability (see my earlier post, “Value Investing Redux”), quality, hedge fund cloning, stock buy backs, dividend appreciation, or other factors. 

So it might be helpful to see how absolute momentum looks when applied to aggregate U.S. stocks using the long-term Kenneth French data library that is available online. I compare results using a 10-month absolute momentum filter to the market index without the use of absolute momentum from May 1927 through December 2014, a period of nearly 87 years. (For those who are curious, a 10-month moving average filter gives a 0.69% lower annual return and a similar maximum drawdown compared to 10-month absolute momentum before transaction costs. The moving average also trades 1.43 times/year, versus 1.06 for absolute momentum over these 87 years. This means absolute momentum has fewer false signals and whipsaw trades. It would look even better with respect to the moving average approach after transaction costs.) When we are out of stocks, assets are invested in one month Treasury bills. Here are the results with monthly readjusting of positions:
       
         
ANN RETURN       11.48           11.76
ANN STD DEV      12.88           18.69
SHARPE  RATIO      0.58             0.42
MAX DD             -41.40         -83.70


Dual momentum, which uses both relative and absolute momentum, is still the premier momentum strategy for most investors, but absolute momentum may be a useful tool for some.

I used to always cut fruits and veggies in the wrong directions. I finally got around this problem by  turning them in the opposite direction to the way I initially wanted to cut them. Similarly, many investors and investment managers are making investment decisions the wrong way and need to reverse how they are going about this.

This problem began with the random walk hypothesis (RWH). That idea, popular in the 1960s and 1970s, said that stocks fluctuate randomly (in statistical terms, are independent and identically distributed). RWH is synonymous with the concept of efficient markets. As such, it eliminated serious interest in tactical asset allocation, trend following, or momentum investing among both academics and most institutional investors.

Some practitioners, however, were creating a substantial body of anecdotal evidence that stock fluctuations were not random, but instead showed short and long-term mean reversion, as well as intermediate- term serial correlation. 

Stock exchange specialists and brokerage firm trading desks made large profits going against short-term customer order flow, which gave them high short-term mean reversion profits. The success of momentum traders like Jack Dreyfus and Richard Driehaus showed that stocks could also exhibit price continuation (momentum). Successful long-term value investors, buying depressed stocks that would eventually recover and outperform the market, indicated that one can also earn long-term mean reversion profits from stocks.

In the mid to late 1980, academics began to catch up with practitioners in discovering the flows of RWH. Ironically, Fama and French (1988), two of the pioneers of efficient market theory, were among the first to show that stocks mean revert based on a 3 to 5-year time horizon. Around the same time, Lo and MacKinlay (1988) and Poterba and Summers (1987) came up with compelling evidence to reject RWH. In the early 1990s, Jegadeesh and Titman (1993) in their seminal papers demonstrated convincingly that price continuation momentum exists on a 3 to 12-month basis. Furthermore, they and others showed that stocks are mean-reverting when looking at one-month returns. They therefore skipped those returns when looking at intermediate-term stock momentum.

Showing just how far academics have come in accepting  12-month momentum (indicating positive serial correlation) , one-month mean reversion, and 3 to 5-year mean reversion, all three of these factors are now in the online Ken French data library for researchers to use in their studies.

So how does all this relate to how investors and investment managers are making poor investment decisions? First, there is still a cultural affinity to RWH despite all the evidence to the contrary. This leads many investors to ignore the profit opportunities inherent in momentum investing.

Next, investors and investment professionals often focus on the wrong time frames in judging investments. Goyal and Wahal (2008) report  that plan sponsors and institutional asset managers choose investment managers based greatly on performance over the past 3 years. Yet we know now that 3-year performance is mean reverting, and strong performance over that time frame is not indicative of similarly strong future results.

As another example, the Morningstar rating methodology weights 3-year performance more heavily than 5 or 10-year performance. If longer term performance is unavailable, ratings are based entirely on 3 year performance. The Vanguard Research report "Mutual Fund Ratings and Future Performance" (2010) found that from February 1992 through August 2009, there was no systematic outperformance by funds rated 4 or 5 stars by Morningstar or underperformance by funds rated 1 or 2 stars. The median 5-star fund's excess return was not consistently higher than the median 1-star fund's excess return.  

Vanguard also reported that investment committees typically use a 3-year window to evaluate the performance of their portfolio managers.  Yet we know that investors and asset managers should focus more on performance outside the 3 to 5-year performance window due to mean reversion using that time frame.

The other problem in performance evaluation is often found among individual investors who overreact to short-term results. When I managed investment partnerships in the 1970s and 1980s, my investors would invariably want to add funds after a single month of strong performance, and, conversely, they would almost never add to their accounts following a significant down month. Short-term mean reversion implies that they should have been doing just the opposite. Dalbar's annual "Quantitative Analysis of Investor Behavior" supports the idea that investors overreact to short-term performance by buying highs and selling lows instead of keeping the big picture in mind, which seriously harms their long term returns. [1]

Doing what may be the wrong thing has even been adopted as an investment strategy by the Global X JP Morgan Sector Rotation ETF (SCTO). This fund buys the strongest U.S. stock market sectors based on  the prior one month’s performance. 

So there you have it. Investment committees, institutional asset managers, Morningstar, and others emphasize 3-year past performance as an indicator of future success, when the just opposite is likely to be true. Adding to this confusion, individual investors and others chase after strong 1-month performance by buying these short-term rallies when they would be better off buying dips.  

Investors and investment managers take heed. Do the right thing. Read the literature. And, if you need to, don’t forget to turn your fruits and veggies in the right direction.

[1] One of the advantages of using a trend following filter like absolute momentum (which is half of dual momentum) to identify regime change and reduce drawdown is that can also reduce investors' loss aversion, ambiguity aversion, and the flight-to-safety heuristic. It may therefore give investors more confidence to stay with the trend and even buy dips.

Socially Responsible Investing (SRI), also known by the more recent name Sustainable Responsible Impact investing or just Impact Investing, is the major application of ethical and social criteria, as well as financial considerations, in making investment decisions. SRI recognizes and incorporates societal needs and benefits in the selection and management of investment portfolios.

SRI has a "feel good" aspect to it, but investors also want to know if they are sacrificing potential returns by adhering to their social ideals. We will look to see if SRI makes sense economically, as well as socially and ethically, and, if it does, then how investors can use SRI for momentum-based investing.

Early History of SRI

SRI first dates back to the Quakers who, in their 1758 yearly meeting, prohibited members from participating in the slave trade. Their Friends Fiduciary investment service has existed since 1892 and continues to manage Quaker assets following SRI guidelines.
 
Another early adopter of SRI was John Wesley (1703-1791), one of the founders of the Methodist Church. Wesley’s sermon on “The Use of Money” outlined the basic tenets of social investing – do not harm others through your business practices and avoid industries which can harm the health of others. In the 1920s, the Methodist Church of Great Britain adopted this policy by investing in the UK stock market while avoiding companies involved with alcohol and gambling.

The first public offering of a socially-screened investment fund was in 1928 when an ecclesiastical group in Boston established the Pioneer Fund. In 1971, a Methodist group organized the PAX World Fund, which appealed to investors who wanted to be sure their profits were not coming from weapons production.  Two years later, SRI went mainstream when Dreyfus, a major mutual fund marketer, launched the Third Century Fund, which grouped together companies noted for their sensitivity to the environment and their local communities.

Social Change through SRI

In the 1980s, SRI became more widespread due to its negative screening of investments in South Africa. SRI practitioners were able to put pervasive pressure on the South African business community that eventually forced a group of businesses representing 75% of South African employers to draft a charter calling for the end of apartheid. Nelson Mandela himself remarked that the University of California's multi-billion dollar divestment was particularly significant in ending white minority rule in South Africa. .

SRI Performance

Although SRI has effectively used market forces to help bring about social change and has been emotionally rewarding to its participants, there has been a long-standing question of whether SRI performance has suffered due to the restricted opportunities available to SRI investors. Over the years, there have been many studies and meta-studies of SRI versus conventional investment performance. A number of studies from the 1990s and 2000s showed no statistical significance between the returns of socially responsible mutual funds and those of conventional funds. One objective survey and assessment of the subject that was the Royal Bank of Canada's 2012 report "Does Socially Responsible Investing Hurt Investment Returns?" The conclusion they reached, based on all the available evidence to date, was that investors had been no worse off with SRI investing than with conventional investing.

Evolution of SRI

In recent years, SRI evolved from exclusionary screening out of investments to include a more proactive approach toward Corporate Social Responsibility (CSR). CSR is a blend of both negative screening and positive selection in order to maximize financial return within a socially aligned investment strategy. Examples of negative screening factors are company involvements with gambling, alcohol, tobacco, weapons, under-age workers, animal testing, and damage to the environment. Examples of positive selection criteria are pollution control, community involvement, energy conservation, consumer protection, human rights, product safety, favorable employee working conditions, and renewable energy utilization. CSR oriented investment programs might also vote their proxies to advance ethical business practices, such as diversity, fair pay, and environmental protection.

CSR further evolved and expanded to include a broader set of Environmental, Social, and Governance (ESG) factors. Interestingly, ESG was soon seen to have practical benefits for the companies that employed these criteria, as well as for investors in those companies. Good citizenship has proven to be good for business.

Performance of ESG Companies

There are a number of reasons that can explain improvements in performance due to ESG. Corporate responsibility can create good relationships with governments and communities, as well as reduce the risks of onerous regulations and conflicts with advocacy groups. It can also influence how consumers perceive a brand and therefore serve a similar role to advertising. This can lead to higher sales and more loyal customers. In addition, corporate responsibility can have a positive influence on companies’ ability to attract and retain talented employees and maintain productive workforces.

According to DB Climate Change Advisors in their 2012 meta-analysis of more than 100 academic studies, “Sustainable Investing: Establishing Long-Term Values and Performance,” 100% of studies showed that companies with high ESG ratings exhibited financial outperformance and had a lower cost of capital, indicating they were less risky than companies with lower ESG ratings. Seeing all these benefits, the number of companies issuing sustainability reports has  skyrocketed. According to the 2013 KPMG Study of Corporate Responsibility Reporting, 93% of the world's 250 largest companies and 86% of the largest U.S. companies (by revenue) now publish annual sustainability reports.

However, what interests us most as investors is how investments in ESG oriented companies have performed relative to other companies. According to the DB Climate Change Advisors report, 89% of highly-rated ESG companies exhibited market-based outperformance and superior risk-adjusted stock returns.

A typical study by Eccles et al. (2011) compared the performance of 180 large U.S. firms by matching 90 high sustainability firms with 90 low sustainability firms. Beginning in 1993, $1 invested in the high sustainability portfolio would have grown to $22.60 by 2010, while the low sustainability portfolio grew to only $15.40.

Rapidly Growing Investor Interest

Companies doing well by doing good have not gone unnoticed by investors. The outperformance in the stocks of high sustainability firms has been attracting considerable investor interest. According to a 2015 survey by the Morgan Stanley Institute for Sustainable Investing, over 70% of active individual investors describe themselves as interested in sustainable investing, and nearly 2 in 3 believe sustainable investing will become more prevalent over the next 5 years.

Looking at recent growth, the global sustainable market has risen from $13.1 trillion at the start of 2012 to $21.4 trillion at the start of 2014, and from 21.5% to 30.2% of all professionally managed assets. Europe has the highest percentage of sustainable assets at 63.7%. But the U.S. has been the fastest growing region over this period and now has 30.8% of all global sustainable assets. The amount of funds invested in the U.S. using social criteria grew from $40 billion in 1984 to $625 billion in 1991 and to $1.5 trillion in 1999.

According to the most recent biennial "Report on U.S. Sustainable, Responsible, and Impact Investing Trends" by the Forum for Sustainable and Responsible Investing (US SIF Foundation), the number of ESG mutual funds in the U.S. was 456 at the start of 2014, up from 333 two years earlier. Assets in U.S. sustainable funds were $6.57 trillion at the start of 2014, up from $3.74 trillion at the start of 2012. This is a growth of 76% in just two years. Assets held in some form of sustainable investment now account for more than $1 out of every $6 under professional management, which is up from $1 out of every $9 in 2012. Investors realizing that ESG oriented funds, which in the past showed no disadvantage to conventional funds, have evolved into ESG funds that now offer superior investment performance when compared with conventional funds.

Dual Momentum with ESG

In my book and on my website I show how dual momentum (a combination of relative strength momentum and trend-following absolute momentum) can enhance and improve the performance of different kinds of investment portfolios, such as global equities, balanced stocks and bonds, equity sectors, and fixed income. We will see now what happens when we apply dual momentum to the world of sustainable investing.

I usually prefer to use low cost index ETFs as investment vehicles. However, that may not be the best approach with ESG funds. There are two reasons for this. First, the difference between a conventional index ETF's annual expense ratio and the average equity mutual fund's annual expense ratio of 1.08 is not nearly as great for sustainable index funds. For example, the  annual expense ratios for the Vanguard and iShares S&P 500 conventional index fund ETFs are .05 and .07, respectively. The annual expense ratios of the two KLD 400 Social Index ETFs, on the other hand, are much higher at .50. Based on expense ratios, sustainability index ETFs are at a decided disadvantage to their conventional index counterparts.

The second reason that sustainability index funds can be problematic is because of their short performance records. The earliest U.S. based SRI index is the Domini 400 Social Index, now known as the MSCI KLD 400 Social Index. It did not begin until May 1990, and data for it is not readily available. The oldest SRI index fund (Vanguard FTSE Social Index) was established only 15 years ago in May 2000.[1]

For these reasons, as well as the reason that active management might add some value in an area such as sustainability, where more informed choices might be better than the mechanical rules, we will apply dual momentum to the oldest, actively managed, sustainable equities-based mutual funds.

Funds Used

The two impact equity funds that have track records longer than 25 years are Parnassus (PARNX) that started in May 1985, and Amana Income (AMANX), that began in July 1986.[2] 

Looking at the details of these funds, Parnassus Fund has an annual expense ratio of .86. This fund screens out companies involved with alcohol, tobacco, gambling, nuclear power, weapons, and Sudan. Parnassus engages in shareholder activism and community investment. The fund has a strong ESG orientation with its mandate to invest in companies having sustainable competitive advantages and ethical business practices. Parnassus also prefers to buy out-of-favor stocks.

Besides incorporating ESG factors and exclusions for alcohol, tobacco, gambling, and pornography, Amana Income (AMANX) avoids companies with high debt-to-equity ratios and large receivables compared to total assets. Their emphasis on companies with stable earnings, high quality operations, and strong balance sheets free of excessive debt gives Amana a tilt toward quality, which is now recognized in academic circles as a beneficial risk premium factor.[3]

In addition, Amana prefers to hold shares in companies where management has a sizable stake, and the fund will sell shares in companies where insiders are selling. There is a large body of academic literature confirming that insiders are better informed and earn abnormal profits from their trades.[4]  Amana Income has an expense ratio of 1.14, plus .25 in 12b-1 marketing fees. However, institutional shares (AMINX) requiring a minimum investment of $100,000 are available with an expense ratio of .90 and no 12b-1 fees.

Here are performance figures through February 2015 for these two sustainability funds starting from July 1986, when performance data first became available for Amana Income. We also include the Vanguard 500 Index fund (VFINX), based on the S&P 500 index, as a benchmark. Vanguard 500 Index has an expense ratio of .17 [5]

		PARNX	AMANX	VFINX
Ann Return		12.63	9.71	11.35
Std Dev		21.71	12.02	15.32
Sharpe		0.39	0.48	0.47
Max DD		-47.98	-34.70	-50.97

We see that Parnassus has a higher return than the S&P 500 with around the same maximum drawdown, while Amana Income has about the same Sharpe ratio as the market with a lower maximum drawdown. The lack of performance homogeneity among these funds is a good thing for relative strength momentum investing. More diversity in performance creates more opportunities for profit. So let us see what happens now when we apply dual momentum to these funds.

First though, I should mention a potential problem of using higher cost actively managed funds. The performance of actively managed funds may revert toward the mean of all funds and be overtaken by the performance of lower-cost index funds. However, this may not be such a problem here for two reasons.

First, we are not selecting actively managed funds based on superior past performance that might subsequently mean revert. We are simply using the three sustainability funds that have the longest track records. Second, we can easily include a low-cost stock index fund in our dual momentum portfolio. Dual momentum is adaptable. If there is a falloff in performance of our actively managed funds, the relative strength component of dual momentum can automatically move us to our lower-cost index fund. This is why we can confidently use actively managed funds within a dual momentum portfolio framework.[6]

Performance Results

Below is the same performance we saw above but with the addition of a dual momentum portfolio made up of all three sustainability funds, the Vanguard 500 index fund for the reason given above, and the Vanguard Total Bond fund (VBMFX) as a refuge for when absolute momentum takes us out of equities. The operating logic behind this model that we call ESG Momentum (ESGM) is the same as for our Global Equities Momentum (GEM) model and is fully disclosed in my book, Dual Momentum Investing: An Innovative Strategy for Higher Returns with Lower Risk. 

		PARNX	AMANX	VFINX	VBMFX	ESGM
Ann Return		12.63	9.71	11.35	6.48	17.60
Std Dev		21.71	12.02	15.32	3.90	13.93
Sharpe		0.39	0.48	0.47	0.69	0.92
Max DD		-47.98	-34.7	-50.97	-5.86	-22.73
Utilization		36%	13%	28%	23%	100%

Results are hypothetical, are NOT an indicator of future results and do NOT represent returns that any investor actually attained. This is not a recommendation to buy or sell any security. Please see our Disclaimer page for more information.

We see that the Sharpe ratio of our ESGM model portfolio was more than twice as high as the average Sharpe ratio of the four equity funds, and the ESGM maximum drawdown was less than half as large.[7] By being in bonds at the right time, ESGM was able to bypass the full severity of bear market drawdowns.

ESGM was in our three sustainability funds 78% of the time that it was in equities. So our mission was accomplished of being mostly in investments that contribute to advancements in social, environmental, and governance practices, while simultaneously giving us exceptional risk-adjusted returns by using dual momentum.

A link to the ESGM model's monthly and annual results is now on the Performance page of our website. It will be updated monthly along with the rest of our dual momentum models. Those wanting news and additional information on sustainability can visit GreenBiz, Social Funds, and US SIF.

     
[1] The two social responsibility ETFs, KLD and DSI, began in 2005 and 2006 respectively.
[2] PAX World Balanced began in August 1971 and CSIF Balanced Portfolio began in October 1982, but both funds have large bond allocations. 
[3] See Asness et al. (2013), "Quality Minus Junk.".
[4] For example, see Jeng et al. (2003) "Estimating the Returns to Insider Trading: A Performance Evaluation Perspective" or  Cohen, Malloy, and Pomorski (2012), "Decoding Insider Information".
[5] We could have used Vanguard's Admiral shares with an expense ratio of .05 or a low-cost S&P 500  ETF, but we wanted to be consistent with the retail shares we used for our socially responsible funds.
[6] There are many more ESG oriented funds, both index and actively managed, that one can choose.  The three used here were selected based on their longevity.
[7] If one uses the oldest no-load SRI bond fund, Parnassus Fixed Income (PRFIX), in place of Vanguard Total Bond from the inception of PRFIX in October 1992, the metrics improve to 17.31% for annual return, 13.97 for standard deviation, and 0.89 for Sharpe ratio. Maximum drawdown remains at -22.73%.  

When doing financial modeling, one of the first things to look at is if your empirical work makes sense. In other words, are there valid economic reasons why a model should work?  This can help you avoid drawing erroneous conclusions based on creative data mining.[1]

Next, you should look for robustness. This can take several forms. One of the most common robustness tests is to see how well a model does when it is applied to somewhat different markets. Even though equities have historically offered the highest risk premium, it is desirable to see a model do well when it is also applied to other financial markets.

Another robustness test is to see if a model is consistent over time. You do not want to see success based on spurious short periods of good fortune. Similarly, you would like to see a model hold up well over a range of parameter values. Getting lucky can be good in some things, but not in financial research. 

Relative and absolute momentum have held up well according to all of the above criteria. But now that momentum is attracting more attention, it is important to remain vigilant and to keep robustness in mind. What makes this especially true is the natural tendency to come up with modifications and "enhancements" that can add complexity to a once simple model.

An interesting new paper by Dietvorst, Simmons, and Massey (2015) called “Overcoming Algorithm Aversion: People Will Use Algorithms if They Can (Even Slightly) Modify Them,” shows that people are considerably more likely to adopt a model if they can modify it. Giving people the freedom to modify a model makes them feel more satisfied with the forecasting process, more tolerant of errors, and more likely to believe that the model is superior. Everyone likes to feel that they have some involvement with and control over a model. and that they may have made it better. Data mined “enhancements” may fit the existing data well but not hold up on new data or over longer periods of time.

I have seen dozens of variations and "enhancements" to momentum, and I will undoubtedly see many more in the days ahead. One variation that attracted considerable attention a few years ago was by Novy-Marx (2012) who found that the first six months of the look back period for individual stocks gave higher profits than more recent six months. This became known as the “echo effect.” However, it never made much sense to me. So I tested the echo effect on stock indices, stock sectors, and assets other than stocks. I was not surprised when incorporating the echo effect gave worse results than the normal way of calculating momentum.

A subsequent study by Goyal and Wahal (2013) showed that the echo effect was invalid in 37 markets outside the U.S. Goyal and Wahal also demonstrated that the echo effect was largely driven by short-term  reversals stemming from the second to the last month. Over reaction to news leading to short term mean reversion of individual stocks does make sense. Prior to that time, only the last month was routinely skipped when calculating momentum for stocks.[2] Based on this finding, the latest research papers skip the prior two months instead of just the last month when calculating individual stock momentum. [3]

While robustness tests are very important, the best validation of a trading model is to see how it performs on additional out-of-sample data. The statistician W. Edwards Deming once said, “In God we trust; everyone else bring data.”

When I first developed the dual momentum based Global Equities Momentum (GEM) model, my back test went to January 1974. This is because the Barclays Capital bond index data I was using began in January 1973. I am now able to access Ibbotson bond index data, which has a much longer history. My GEM constraint has now changed to the MSCI stock index data going back to January 1970.

Having this additional bond data, I have another three years of out-of-sample performance for GEM. My  new back test includes the 1973-74 bear market and shows dual momentum sidestepping the carnage of another severe bear market.


GEM is more attractive than it was previously on both an absolute basis and relative to common benchmarks. Here is summary performance information from January 1971 through July 2015. 60/40 is 60% S&P 500 and 40% Barclays Capital U.S. Aggregate Bonds (prior to January 1976, Ibbotson U.S. Government Intermediate Bonds). Monthly returns (updated each month) can be found on the Performance page of our website.

	GEM	S&P500	60/40
Ann Rtn	18.2	11.9	10.2
Std Dev	12.5	15.2	9.8
Sharpe	0.91	0.38	0.44
Max DD	-17.8	-50.9	-32.5

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 GEM Performance and Disclaimer pages for more information.

In our next article, we will look at longer out-of-sample performance using the world’s longest back tests. Fortunately for us, these were done to further validate simple relative and absolute momentum.


[1] For example, between 1978 and 2008, U.S. stocks had an annual return of 13.9% when a U.S. model was on the cover of the annual Sports Illustratedswimsuit issue versus 7.2% when a non-U.S. model was on the cover. 
[2] Short term mean reversion is not an issue with stock indices or other asset classes, so the last two months do not need to be excluded from their momentum look back period.
[3] See Geczy and Samonov (2015). Discovery of  two month mean reversion is an example of the Fleming effect in which different but related research can lead to serendipitous results.
 

I have always looked favorably upon do-it-yourself investing (DIY). It was a prominent feature of my own book. So I’ve been looking forward to DIY Financial Advisor: A Simple Solution to Build and Protect Your Wealth by Wes Gray, Jack Vogel, and David Foulke (GVF), the managing members of Alpha Architect.

GVF took on an ambitious project in that they cover a broad range of subjects from the theoretical ideas of models versus experts, proper investment evaluation, and behavioral finance to the practical applications of value and momentum investing, market timing, and asset allocation.

Part 1 of the book is called “Why You Can Beat the Experts.” Beginning with the Preface, GVF says it is best for investors to maintain direct control over their own accounts. They point out the misalignment of incentives and objectives between owners of capital and investment managers. Even though more concentrated portfolios usually perform better over the long run, managers prefer to hold broader portfolios that have less of a chance of deviating from benchmarks in the short run. Outperforming benchmarks gives managers little or no reward, while under performing can get one fired.
 
In Chapter 1, GVF presents evidence that model-based decision making gives better results than discretionary decision making. Simple is better than complex, yet “experts” often favor complexity so they can charge higher fees.

GVF points out that the decision making process has three components: R&D, implementation, and assessment. Experts may be useful for model R&D and assessment, but are not usually necessary for implementation. Experts’ use of qualitative information and additional data is often of little or no value. Experts can also suffer from the illusion of skill and a failure to recognize randomness.
 
In Chapter 2, GVF shows with additional studies and meta-studies that models are consistently better than experts. Models are even better than experts using the same models with discretion. Why is this? Models, unlike experts, are not subject to behavioral biases such as overconfidence, anchoring, framing, and loss aversion.

In Chapter 3, GVF goes into more detail about behavioral biases and shows just how strong they can be.  Chapter 4 explains how experts tell stories in order to make some approaches, such as buy-and-hold or value investing, seem like the only ones that make sense. Investors buy into these stories because they want to feel that expert opinions and judgements matter – when, in fact, they often do not.  In the course of their discussion, GVF debunks a few other investment myths, such as quality enhances value and economic growth leads to higher stock returns.

After GVF establishes the fact that we can beat the experts, Part 2 of the book moves on to explain “How You Can Beat the Experts.” Chapter 5 shows that it is difficult to identify competent and trustworthy advisors. GVF then lays out their sensible FACTS framework for investment evaluation. FACTS stand for fees, access, complexity, taxes, and search. You want fees, complexity, and taxes to be low, while accessibility and search ability should be high. Some of this may be obvious, but not everyone may realize that complexity isn’t usually desirable.

 In Chapter 6, GVF shows that Markowitz mean-variance portfolio optimization (MVO) does not hold up well in the real world due to the instability of its inputs. Research shows that equal weighting of assets does a much better job than MVO or other optimization approaches. The remainder of chapter 6 is devoted to old paradigm asset allocation schemes, such as the Swensen, Bernstein, and Faber IVY 5 strategies. These aim at reducing portfolio volatility by diversifying with permanent allocations to different asset classes. Since equities have consistently provided the highest long-run returns, permanent multi-asset allocation schemes sacrifice some potential return in pursuit of volatility reduction.[1]  Someone who fully appreciates momentum investing might prefer instead to hold only the top performing asset classes instead of them all.

Recognizing the value of trend-following timing overlays, in chapter 7 GFV lays out their own risk management framework that they apply to the IVY 5 portfolio. Because all five asset classes are permanently represented in the IVY portfolio, a trend-following exit from one asset class puts that portion of the portfolio in Treasury bills, which can create a drag on portfolio performance.

GVF once used a 12-month moving average (MA) as a trend following filter. They show here that time series momentum (TMOM) beats out MA as a timing model in 4 out of 5 asset classes.[2]  Instead of just adopting TMOM themselves, GVF now combines 12-month absolute momentum (TMOM) with the12-month MA on a 50/50 basis to create complicated approach that they call robust asset allocation (ROBUST). GVF says “…the evidence suggests that combining the two technical rules seems to be the strongest performer,” but results indicate TMOM and ROBUST are at least equal in their performance and that TMOM may actually be better. Table 7.8 shows the winner (marked by an X) between MA, TMOM, and ROBUST, according to GVF.

GVF declares a tie in two cases where there is only a 1 or 2 percentage difference in Sharpe and Sortino ratios, but they say that ROBUST is the winner in two other cases (SPX and REIT) where there is the same small difference in these ratios. The only case among the 5 assets where there is a clear cut winner is bonds (LTR), where TMOM comes out ahead.

GVF presents longer out-of-sample results for the U.S., German, and Japanese stock markets. The only clear winner there is also TMOM, when it is applied to the Japanese market. So both in-sample and out-of-sample, TMOM shows equal or better performance to the ROBUST method before transaction costs. After transaction costs, the case is stronger for TMOM over ROBUST.[3]  It is also simpler than ROBUST.
 
GVF finishes up chapter 7 by giving a good reason, in addition to the usual behavioral finance ones, of why trend following works. This has to do with what GVF calls dynamic risk aversion. As prices drop, investors become more risk averse and do not want to step up to support prices. This accentuates trends as markets extend beyond their fair value. GVF does not mention it, but the same logic could apply to the upside. Investors may become less risk averse as markets rise, causing trends to overextend on the upside as well.

In Chapter 8, GVF discusses security selection using value and momentum. Their Alpha Architect website maintains an updated list of the top 100 value and momentum stocks based on the simple screening criteria that GFV presents here.

GVF’s value selection criterion is earnings before interest and taxes (EBIT) divided by total enterprise value (TEV). This is covered in greater detail in the earlier book Quantitative Value by Gray and Carlisle. GVF shows that the top decile of value stocks from 1927 through 2014 outperformed the market with respect to returns, but that value had much higher volatility, making the Sharpe ratios of value and the market the same. Both value and market also had high worst drawdowns of -91.7% and -84.6%, respectively.

A risk factor that GVF did not mention is the idea of a value trap. Some of the cheapest value stocks may be depressed because they deserve to be, based on very poor fundamentals. These stocks may remain permanently depressed or become even more depressed because they are on the verge of bankruptcy. For this reason, it may be better to invest in more than just the very cheapest value stocks.

Moving on to momentum, GVF gives behavioral explanations of why momentum works. They show that the spread between high and low momentum stocks is close to five times the spread between value and growth stocks (18.4% versus 3.7%) from 1927 through 2014. The top decile of stocks sorted by 12-month (skipping the last month) momentum and rebalanced monthly outperformed the market by 5.4% annually, after deducting 2.4% for annual transaction costs.

There is some controversy, however, about how high transaction costs might really be when momentum is applied to individual stocks. Gerstein Fisher (2015) estimates that a monthly rebalanced, long-only momentum portfolio can have an annual turnover of around 300%. Lesmond et al. (2004), whom GVF cites, report that bid/ask spreads are much higher for momentum stocks. They find strong evidence that trading costs for momentum stocks are at least 1.5% per trade using conservative assumptions and a battery of trading cost estimates. If you multiply the 300% turnover by at least 1.5% per trade, you get annual trading costs of at least 4.5% annually, rather than 2.4%. Perhaps transaction costs now are less than 4.5%. But they should still be substantial, since it is unlikely that the high bid/ask spreads of momentum stocks has changed all that much.

In Chapter 9, GVF gets to the heart of DIY investing by suggesting ways for investors to implement a DIY approach other than by buying and holding a balanced stock/bond portfolio. The first way is with a basic IVY 5 portfolio of generic ETFs filtered with a trend-following 12-month moving average. The five asset classes used are U.S. and international stocks, intermediate bonds, REITs, and commodities. GVF says, “Yahoo Finance charting allows you to run a monthly 12-month MA test for each asset class and with a yearly rebalance across assets you would be in DIY heaven.”  Unfortunately, this is not really possible with Yahoo finance, since it does not provide for monthly charts. Even if it did, Yahoo finance uses only price changes and not total returns. You could, however, use TMOM instead of an MA approach by inputting the appropriate ETFs into stockcharts.com and selecting a one year SharpChart that you could bookmark and look at monthly.[4]

GVF next presents enhancements to this strategy by applying both an MA and a TMOM timing filter (which equals their ROBUST method) and by substituting individual U.S. and international value and momentum stocks in place of generic U.S. and international stock ETFs. GVF has a risk-conscious allocation scheme that varies the percentages allocated to equities for balanced, moderate, and aggressive investors to 40%, 60%, or 80%, respectively. However, since timing filters are meant to remove equities entirely from one’s portfolio during bear markets, the balanced and even the moderate allocations may be too conservative for most investors. Equities have provided the highest returns historically, and substantial allocations elsewhere may greatly diminish one’s accumulated wealth over a lifetime of investing.

It is not easy for most DIY investors to calculate TMOM and MA values. So to remedy this situation, GVF provides a free service on their website that applies the ROBUST filter to each asset class for balanced, moderate, and aggressive portfolios. Therefore, no one needs to pay for an asset allocation or timing overlay. They are available free, along with the top 100 value and momentum stocks, to anyone who signs up on the Alpha Architect website.

GVF recommends concentrated portfolios in order to maximize the benefits of value and momentum investing. Modern portfolio principles tell us that a well-diversified portfolio at least 30 stocks can eliminate most idiosyncratic risk. But this may not be true for portfolios of stocks having high bankruptcy risk. Concentrated portfolios of 30-50 of the very cheapest value stocks may still be too risky.

The same issue applies to momentum stocks. Looking at the top momentum stocks according to the Alpha Architect screener, 18 of the top 25 and 31 of the top 50 stocks are in biotech/medical technology. High intra-industry covariance means that you would need to hold significantly more than the top 50 momentum stocks in order to have a well-diversified portfolio.

Another drawback to an individual stock momentum portfolio with only the strongest momentum stocks is that there is a good chance some of them may be takeover candidates or may have already been taken over. Takeover stocks usually do not perform as well in the future as stocks that showed less momentum but were subject to takeovers.

For all the above reasons, investors may want to hold at least 100 momentum and 100 value stocks if they are selected using simple screens such as the ones presented here. How likely is it that a DIY investor would be willing to manage 100 value and 100 momentum stocks, or even 50 of each?
 
Periodic timing model signals would add to that complexity, since you would, at times, need to exit and re-enter your entire stock portfolio. If, on the other hand, you do not use a timing model overlay, then you should be prepared for some very large drawdowns and multi-year periods of significant benchmark under performance.[5]

Another potential problem with holding individual stocks is the issue of scalability. The identity of the top value and momentum stocks is readily available now through the use of screeners, such the one that Alpha Architect provides. Increasing amounts of capital buying the same limited number of stocks may mean diminished future returns.
 
GVF finishes up chapter 9 with their “Ultimate DIY Solution.”  Here, instead of a generic REIT allocation, they recommend a monthly rebalanced portfolio of the top one-third REITs ranked by momentum (excluding those below the 40th percentile in market capitalization).  In place of a generic allocation to commodities, GVF recommends a portfolio of commodity contracts selected on the basis of momentum and term structure.

Recognizing that DIY investors will never go about constructing all these different portfolios, GVF suggests that DIY investors hold all seven assets (U.S. value and momentum stocks, international value and momentum stocks, bonds, REITs, and commodities) in the form of exchange-traded products (ETFs or ETNs) and then apply their risk-management overlay to these funds each month. Here is a table of suggested investment vehicles:


There are currently no domestic or international momentum ETFs that hold fewer than 50 stocks, although some may be forthcoming. There are also no ETFs that hold fewer than 50 REITs or that use momentum to select REITs.

There is a just one commodity ETF or ETN with a focus on term-structure and momentum. It is the United States Commodity Index (USCI). However, USCI is required to maintain positions in six different commodity sectors at all times. This means some sectors may not always have positions that are in accordance with a favorable term-structure. USCI does use 12-month momentum to help select its long-only positions. According to Miffre and Rallis (2006), 12-month momentum applied to commodities is profitable, but mostly on the short side. However, Geczy and Samonov (2015) report that 12-month momentum (skipping two months) works in reverse for commodities. In other words, positive 12-month momentum is a negative factor. USCI is down 43% since its high in April 2011.

In their final chapter, GVF mentions some of the reasons investors might not want to do-it-themselves. These include being able to blame someone else if things don’t go well, inertia to change, unwillingness to let go of current relationships, bias in favor of experts over models, overconfidence in one’s own abilities, models not conforming to our preferences and beliefs, and the desire to be a hero. These psychological excuses are certainly worth thinking about. Perhaps then they will be less likely to influence us against adopting a sensible, model-based investment approach.

If I had been GVF, I might have said more about why investors who adopt DIY investing might abandon or modify it into something that is no longer purely model-based.  DIY investing should be simple, but it is not always easy. To be successful, DIY investing requires a good understanding of the principles underlying one’s model and the requisite discipline to stick with it under varying market conditions.
Here are my main conclusions about the second half of the book:

•    The IVY 5 permanent portfolio scheme does not take advantage of relative strength momentum.
•    The ROBUST timing model trades more, is more complicated, and is no better than TMOM.
•    Value trap risk and intra-industry co-variance risk may make value and momentum portfolios with 50 or fewer stock too volatile, while larger value and momentum portfolios may be impractical for DIY investors.
•    Both value and momentum are subject to high worst drawdowns. The use of a timing model to reduce drawdown may be difficult with large portfolios of individual stocks.
•    Turnover is very high and transaction costs may be substantial for momentum portfolios of individual stocks    
•    The most practical approach is to apply a timing overlay model to asset class ETFs.  Investors can easily do this with TMOM and SharpCharts. Those who prefer to use ROBUST can access it for free on the Alpha Architect website.
 
The reason I can recommend this book is the good job GVF does in explaining models versus experts and behavioral biases. We can never be reminded too often of these important matters.


[1] A newer paradigm uses trend following methods to diversify among different assets on a temporal rather than a permanent basis.  This means investors can focus on equities for as long as equities remain strong and diversify into other assets when they are strongest. 
[2] GVF uses the term time-series momentum (TMOM) to mean absolute momentum. Both relative and absolute momentum are based on time series (asset returns), and absolute momentum is a better term to differentiate it from relative momentum. However, we will use time-series momentum (TMOM) here to avoid confusion.
[3]GVF estimates that the MA signal has a 20% higher turnover than TMOM, while our calculations show 30% more trades. MA and ROBUST should therefore have greater transaction costs and more whipsaws than TMOM.
[4] Alternatively, you could track a portfolio on ETF Screen or Morningstar that shows performance over the last year. 
[5] Timing models have a much more beneficial effect on momentum portfolios than on value portfolios. 

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 eleven of the sixteen 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 disguising multi-signal factor  biases[1].

Factor Investing Issues

With factor investing in general there are three additional 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 (IVE), iShares Russell 1000 Value (IWD), and Vanguard Value (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 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. There is some controversy regarding the impact of size on risk adjusted returns [2]. Small cap stocks, while giving higher returns, may add little on a risk-adjusted basis because of their high volatility. When combined with value or with value and momentum, which is what  six of these multi-factor funds do, small cap may be undesirable, since it may aggravate already  high portfolio volatility and potential bear market exposure. Small cap stocks are also more costly and difficult to trade..

It is also surprising that the strongest anomaly by far, price momentum, is included in only twelve of the sixteen U.S. multi-factor funds. Considerable research has shown that momentum is the most powerful factor for generating positive risk-adjusted returns.

The final issue associated with multi-factor funds is their average annual expense ratio of 41 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 (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.[3] This is the same expense ratio as the biggest and most popular ETF in the world, the SPDR S&P 500 ETF Trust (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. Even though our testing has shown that value and individual stock momentum do not  add  any value to any of our dual momentum models, GSLC might be worth considering as a portfolio addition if it continues to have a low expense ratio, high liquidity, and good relative performance.
.

	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, Quality, Mom, Size	$5 m	135	0.35
Global X Scientific U.S.	SCIU	Value, Size, Low Volty, Mom	$2 m	489	0.35
3 Factor Lattice U.S. Equity Strategy	ROUS	Value, Mom, Quality	$23 m	201	0.35
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	Value, Mom, 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, Mom, Quality	$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.

[1] See Novy-Marx (2015) for details on multi-signal bias issues.
[2] See Israel and Moskowitz (2012), for example, who say that the alpha from sizearestatisticallyweak, and AlphaArchitect (2014), who explores the empirical side of small cap stock performance.
[3]  GSLC has an annual fee waiver of 15 basis points until September 14, 2016, after which time it has the option to raise its expense ratio up to 0.24. Whether or not fees are raised often depends on asset growth.

Several months ago we posted an article called “Bring Data” where we showed the importance of having abundant data for system development and validation. This was further reinforced to us recently when someone actually brought us additional U.S. stock sector data. Previously, we only had Morningstar sector data that went back to 1992, which we used to construct our Dual Momentum Sector Rotation (DMSR) model. (S&P sector data also goes back to only the early 1990s.) DMSR was shown in my book as one example of other ways you might use dual momentum.

When we were given equivalent Thompson Reuters U.S. stock sector data back to 1973, we immediately extended our DMSR back test to include this additional data. After incorporating the new data, DMSR still looked considerably more attractive than buying and holding the S&P 500 index. But one could argue that the performance of our dual momentum models using broad-based equity indexes, such as Global Equities Momentum (GEM), now look better than DMSR. Here are the comparative performance figures from January 1974 through October 2015:

	GEM	DMSR	S&P 500
Average Annual Return	17.36	15.86	12.21
Standard Deviation	12.32	14.55	15.43
Sharpe Ratio	0.89	0.67	0.42
Maximum Drawdown	-17.84	-33.96	-50.95

 
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 website's Performance and Disclaimer pages for more information.

Because the monthly correlation between GEM and DMSR is only 0.59, sector rotation can still have a useful but modest role to play in a diversified equities-oriented portfolio. But DMSR is not the best choice as a core portfolio holding. Sector rotation programs that use data no further back than the early 1990s to develop their models may be in for a rude awakening someday if future drawdowns are higher and returns are lower than they expect based on back testing with a limited amount of data.

Along the same lines, there are also momentum-based portfolios popping up on the internet all the time now, some even labeled as “dual momentum,” that are modeled on the basis of only 10 or 15 years of ETF data. Momentum may be robust enough that future results won’t suffer much because of this. But those who think they are constructing optimal models this way are just fooling themselves. Overfitting modest amounts of data is one of the most pernicious problems in the development of investment models. Those who do this may argue that the markets change over time, so the best model parameters from years ago may not be as relevant as today’s best parameters. This may be true. However, what is also true is that today’s parameter values are also likely to be sub-optimal when moving forward in time. The following chart from my book, Dual Momentum Investing, shows what I mean:

 Chart courtesy of Tony Cooper

The S&P 500 is highlighted in different colors for each 15 year period. You can see that the latest period, 1999-2013, looks different from the preceding period, 1984-1998. 1999-2013, in fact, looks more like the earlier 1969-1983 period. 1984-1998 is also different from its preceding period, 1969-1983 and similar to the earlier years 1954-1968. If you had used each 15-year period to develop your model, you would have had something unsuited for each of the next 15-year periods. You would likely be better off using all four periods to formulate a model rather than just the last 15-year period. The more data you use, the more likely you are to have a robust model that will hold up reasonably well in the future, even though it isn’t the best fit to any one particular period.

The 12-month look back parameter we use for our GEM and ESGM dual momentum models was found to work well in 1937 by Cowles & Jones. It has been used extensively in momentum research since then and has held up well out-of-sample. But there is a lot more history than that to help give us more confidence in momentum. Let's take a look at some of that now.

We focus on stocks as our core asset since they have historically offered the highest risk premium to investors. U.S. stocks, in particular, have given investors the best long-run returns. Other assets can create a drag on long-run portfolio performance. They also lose some importance as diversifiers once you use a trend following overlay like absolute momentum to help attenuate your downside risk exposure.

The longest back test on stock market momentum is by Geczy and Samonov (G&S). Their 2013 paper called “212 Years of Price Momentum: The World’s Longest Back Test 1801-2012” compared the top one-third to the bottom one-third of U.S. stocks sorted monthly by relative momentum. Over this entire sample period, the top equally weighted momentum stocks outperformed the bottom ones by 0.4% per month with a highly significant t-stat of 5.7. Prior to this study, momentum outperformance on U.S. stocks had been found significant back to 1926. G&S showed that stock momentum was also positive and statistically significant from 1801 to 1926.

G&S also found that stock market momentum was remarkably consistent. In only 2 of the 21 decades from 1801 through 2012 did long-only momentum under perform buy-and- hold, and these were by just -1.2% and -0.7% annually. In all the other 19 decades, momentum outperformed buy-and-hold by an average of 3.8% annually.

This year G&S came out with a new study called, “215 Years of Global Multi-Asset Momentum: 1800-2014: Equities, Sectors, Currencies, Bonds, Commodities, and Stocks.” Here G&S expanded their momentum study to cover six different asset classes, including bonds, stock sectors, and equity indices, which are the ones we use in our momentum models. [1] G&S demonstrated the outperformance of momentum inside and across all asset classes except commodities. Here is a chart from their paper showing the log cumulative equally weighted average of the 6 asset classes plus the cross asset momentum excess returns.
The strongest momentum effect is in country equity indices, which had a long-only monthly excess return over buy-and-hold of 0.52% with a highly significant t-stat of 11.7, compared to 0.29% with a t-stat of 6.4 for individual U.S. stocks and 0.24% with a t-stat of 15.5 for all assets. G&S also show that long-only absolute (time series) momentum outperformed buy-and-hold by 0.15% per month with a t-stat of 11.2.

For those who want to further their momentum education, I suggest you first read the seminal paper by Jegadeesh and Titman (1993) that started the modern momentum renaissance. Next, learn about absolute momentum from Moskowitz et al (2012) or Antonacci (2013). Then follow up with Geczy and Samonov (2015) to satisfy yourself as to the efficacy and robustness of momentum investing based on 215 years of empirical evidence.

[1] Equity indexes are equally as good as individual stocks (or better, according to G&S) in capturing the momentum effect. Indexes are much easier to use and avoid the enormously high transaction costs associated with rebalancing momentum-based stock portfolios.

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 its 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 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 justify 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 disappear. Representativeness and overconfidence are also evident when there are strong momentum-based trends.

Furthermore, investors' risk aversion may decrease as they see prices rise, and they become overconfident. Their risk aversion may similarly increase as prices fall and they become more fearful. These natural psychological responses are also unlikely to change in the future.

One can easily make a sound 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 events, 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 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 first overreact and become overextended, which leads to their mean reversion. We will make a case 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", that 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 for more information.

Relative momentum provided almost 300 basis points more annual average 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 came from price overreaction, since both indices pulled back sharply following their 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, 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 market selected by relative momentum. Trend following absolute momentum can help lock in these overreaction profits before the markets mean revert and they are lost.

The second way overreaction comes into play is when we avoid it on the downside 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/mean reversion, 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 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 under performance 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 our 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. 

In the words of Asness et al (2014), “No other factor…has nearly as long a track record, as much out-of-sample evidence (including across time, geography and even security type), or as strong and reliable a return premium as momentum.” They were speaking only of relative strength momentum. When you add in absolute momentum, which has shown the ability to further boost returns as well as decrease bear market risk exposure, you have to wonder why many more investors are not enthusiastic about momentum. This is especially surprising since the stock market has twice experienced 50% losses during the past 15 years and is back in a precarious position again.

One reason for a lack of interest may be common misconceptions about momentum. This prompted Asness and his posse in 2014 to publish a paper called “Fact, Fiction, and Momentum Investing.”

We have seen others characterize momentum as a shorter-term moving average approach or something based on daily price changes. But momentum is a well-documented intermediate term anomaly effective over a 3 to 12 month look back period. For stocks, longer (3 to 5 years) and shorter periods (1 month or less) are mean reverting, which means they perform the opposite of momentum. Daily price changes are just noise with little or no serial correlation.

We think there is more to the lack of interest in momentum than just incorrect information. Some investors diligently search for reasons, real or imagined, to reject, criticize, ignore, or misrepresent  momentum. Why is this?

Here are some possible explanations gleaned from my discussions with others, online reviews of my book, and some internet articles about momentum.

Reason #1: I have done a lousy job explaining momentum.

This reason was also given by Cliff Asness who did his PhD dissertation on momentum. I wrote an entire book about it, but there is so much more to momentum than one can cover in any one book, article, or research study. To appreciate the efficacy of momentum you need to spend some time exploring the small mountain of momentum research done over the past 20 years. There have been hundreds of research studies by academics on relative strength (cross-sectional) and absolute (time-series) momentum. Momentum has shown consistent in- sample and out-of-sample profits and strong robustness with many different markets all the way back to the year 1801.  Results have been impressive enough to turn the academic world on its head and sway them away from their prior efficient market view of the world. Even Fama and French, two of the bastions of efficient market theory, called momentum the “premier anomaly.”

In my book and blog posts you will find references to many momentum research studies you can download for free. Wading through these may seem like a daunting task and, to quote Yogi Berra, “If people won’t come to the ballpark, how are you going to stop them?” But you can gain considerable insight from reading just the papers’ abstracts, conclusions, and exhibits. Momentum is not as complicated as you might think. In fact, one of the main attractions of momentum is its simplicity.

Reason #2: Momentum is too simple.

Some people wonder how an approach this simple can be so good. Momentum has even been called a trivial strategy, but its results are hardly trivial.

Simple is good, since it means less chance of overfitting the data. Synergy happens when you combine simple absolute momentum with simple relative momentum. Over the long run, relative momentum offsets the underperformance of absolute momentum in bull markets. Absolute momentum, unlike relative momentum, can side step severe bear market drawdowns. This complementary combination can boost profits while also reducing bear market risk exposure. Our last blog post, “Why Does Dual Momentum Outperform”, shows how relative and absolute momentum work together synergistically to create the whole of dual momentum that is greater than the sum of its parts.

We also opt for simplicity by using a simple stock and bond portfolio. Because of the drawdown reducing benefit of absolute momentum, dual momentum does not need to diversify into a large number of assets to reduce portfolio risk.

The post receiving the most views on my blog is the one about our dual momentum sector rotation model. People seem attracted to approaches with more “moving parts,” even though, as in this case, complex models are often no better than simpler ones with fewer parameters. Frederic Chopin called simplicity “the final achievement.”

I recently gave a presentation in Florida where the sponsoring chairperson said he initially dismissed my book because the approach in it looked too simple. After he back tested the strategy himself and saw how strong and robust it was, he became an enthusiastic supporter of dual momentum.

Simplicity is a great virtue, but it requires hard work to achieve it and education to appreciate it. And to make matters worse, complexity sells better.   - Edsger Dijkstra, 1972 Turing Award winner              

Reason #3: Momentum is hard to get your head around.

We all love a good story, and momentum is not a good “once upon a time” concept. Most of us understand the principle behind value investing. We buy what is “cheap”. The concept of value is widely accepted throughout the investment industry. Yet Israel and Moskowitz (2012), using book-to-market and other common value criteria, show that “the value premium is largely concentrated among only small stocks (microcaps) and is insignificant among the two largest quintiles.” Because of liquidity and cost issues, hardly anyone invests in microcaps. Other studies show that value works with large stocks but only during the month of January. The following table from Das and Rao (2012) illustrates this point.


Value investing is also subject to sustained tracking error (six straight years of under performance in the 1990s), larger drawdowns than the broad market, and value traps where cheap companies can become much cheaper. The energy and mining stocks are showing us that now. They appeared inexpensive over the past few years, but no one accounted for the dramatic drop in the future value of their assets.

Value investing is still quite popular because investors like the idea of buying what may be cheap. But investors forget (or never realized in the first place) that stocks may be cheap because their risks may be high.

With momentum investing, instead of buying what is cheap, we buy what has appreciated. Buying strength goes against human psychology. In fact, the disposition effect makes us want to sell recent winners rather than buy them. It goes against the maxim first uttered in the early 1800s, “cut short your losses and let your profits run on.” The behavioral heuristics of anchoring and conservatism can also create inertia keeping us from buying momentum winners.

Reason #4: You cannot trust back tested results.
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Like the Wizard of Oz witches, there can be good back tests and bad back tests. Practitioners often prefer complicated models. They may search for the best ex-ante assets, parameters, or filters without considering if their logic makes sense, how consistent their results are over time, and what the impact is of transaction costs. They tend to use limited amounts of data, over optimize their model parameters, and overfit the data.

This kind of back testing usually does not hold up well ex-post, and it is good to be skeptical of it.
Academic trained researchers, however, prefer simple (parsimonious) models, and they apply them to as much data as possible. They then look to verify promising models on out-of-sample data whenever it becomes available.

Trained researchers also give importance to robustness. They prefer models that hold up over a range of parameter values and when applied to other non-correlated markets.

Momentum has held up well based on these more stringent criteria. It has worked over a wide range of look back periods and across almost all asset classes. Here is a table from Geczy and Samanov (2015b) showing decade-by-decade long-only (the winner’s column) momentum performance of U.S. stocks from 1801 through 2010:

Only 2 out of the last 21 decades showed momentum underperformance versus buy-and-hold, and those performance differences were small. Here is a similar table from Geczy and Samonov (2015a) with long winners and short losers using different asset classes over the same 210 year period:
We see impressive consistency over the past 21 decades across all asset classes. The authors also confirm the efficacy of absolute momentum over the 210 year period. But momentum is not just a good fit to the data. There are some deep seated behaviorally-based reasons that can explain why momentum has performed well and has a good chance of continuing to perform well.

Reason #5: Momentum is not diversified.

Relative strength momentum could not exist without diversification. Relative strength requires multiple assets to compare and choose from. You can think of momentum as vertical rather than horizontal diversification. It diversifies across time rather than across assets in order to better exploit market strength and avoid the performance drag that comes from always holding lower risk premium assets.

The U.S. stock market has the highest long-run risk premium, and that is why we use it as our core dual momentum asset. This gives us a logical reason rather than one based on data mining. We simply look for the highest risk premium, then manage that risk. We hold short term bonds when stocks are weak and it makes the most sense to be in fixed income.
Source: Jeremy Siegel, Stocks for the Long Run. McGraw-Hill

Why do we diversify in the first place? It is usually to reduce portfolio volatility, uncertainty, and downside risk exposure. Dual momentum does this automatically. By keeping that in mind, we may be able to better tolerate the higher short-term volatility that comes from holding a single asset portfolio. As Charlie Munger said at the 2004 Berkshire Hathaway annual meeting, “The idea of excessive diversification is madness… almost all good investments will involve relatively little diversification.”

Reason #6: Momentum conflicts with strongly-held prior beliefs.

Beliefs can have a powerful influence on how we perceive new information. This may have created confirmation bias that now causes them to reject momentum investing if they perceive momentum as being the opposite of value investing. Then there is the buy-and-hold Borg (“resistance is futile; you will be absorbed”) that rejects any approach trying to beat the market.
 
Dual momentum may experience extra prejudice since it includes trend-following absolute momentum that may seem like voodoo to those taught that you cannot successfully adapt to changing market conditions. Andrew Lo and other academics have now demonstrated that this is not always true.  Trend following has been shown to be effective all the way back to the 1700s. According to Greyserman and Kaminski (2014), equities with trend following showed a higher Sharpe ratio, reduced downside exposure, and gave nearly a 3% greater annual return than buy-and-hold from 1695 through 2013.

No investment approach is perfect, and dual momentum will at times underperform its benchmark when looking at incomplete market cycles [1].  Its absolute momentum component can be subject to whipsaws, especially during bull markets [2]. Career risk can be a significant factor among investment professionals who fear tracking error and deviations from investing norms.

Reason #7: Momentum outperformance may not last.

Stocks and bonds may return less in the days ahead. But then most other investment portfolios would also show lower returns. Momentum has outperformed from at least the 1800s.

Anything can happen in the investment world, and momentum could lose some of its luster. But if you want to play stump the critic, ask them why they think momentum outperformance is unlikely to continue. You may guess the sun will stop rising in the East some day, but past evidence is against it. I have not yet heard a credible reason on what would cause the sun or dual momentum to suddenly change direction.

Reason #8: Momentum may attract too much interest, and this will ruin it.

Anomalies can lose their effectiveness if too many people get on board. This may be particularly true for an approach like dual momentum that offers both higher expected returns and lower risk exposure.

But investors have known about the momentum anomaly for more than 20 years, and there has been no degradation in its out-of-sample performance, despite blog posts saying otherwise. Over the past 15 years, the Sharpe ratio of the top one-third value-weighted momentum deciles of U.S. stocks was 0.41, compared to 0.15 for the bottom one-third deciles [3].
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Research has shown that stock momentum has earned twice the annualized return premium as value since 1927. Despite this, momentum investing has not attracted anywhere near the amount of interest that value investing has. Value is used more than momentum in multi-factor funds. The word “value” is found over 130 times more often than the word “momentum” in the names of all U.S. mutual funds and ETFs.

Since most momentum research covers individual stocks, momentum is most commonly used with portfolios of individual stocks. But we use broad market indices with dual momentum, and these are much more scalable than individual stocks.

There is much less interest in absolute or dual momentum than in relative momentum because many still refuse to accept trend following. This gives our dual momentum approach even more scalability.
For abnormal profits to be arbitraged away, investors need to behave rationally, and momentum profits are mostly due to investor irrationality.

Reason #9: If momentum is so good, why hasn’t it attracted more interest, especially from institutional investors?

There are innumerable value, buy-and-hold, and other investors whose biases keep them from being receptive to momentum. This is especially true of institutional investors. Institutional constraints keep most institutional investors away from momentum in general. Career risk associated with tracking error, long-standing aversion to trend following, and confirmation bias are disincentives that keep institutional investors away from dual momentum. Without their participation, it is unlikely momentum will be over exploited. The reasons that keep investors from accepting and using dual momentum are the same reasons that should keep it from ever becoming too popular [4].

Summary

There are certainly risk factors and challenges associated with momentum investing. These include the possibilities of whipsaw, lags in stock market re-entries, and other forms of tracking error. Risk premiums may also change over time, but this is not a potential issue limited to just momentum. Momentum can also be challenging to use, since its signals may run counter to our emotional inclinations.

But the aversion some have to momentum has little to do with these risk factors. The behavioral biases that keep investors away from momentum investing are the same ones that cause momentum to work in the first place. These include conservatism, confirmation bias, and anchoring that prevent us from accepting something new or unfamiliar. Other influencing biases are herding and overconfidence by those who are strongly committed to other investment approaches [5].

It should come as no surprise that the same irrationality causing momentum to work in the first place also keeps investors from accepting and using it. Behavioral economists have long shown that people are consistently irrational. And that’s all people, not just my ex-wife. For those who can overcome their behavioral biases and prejudices, may the momentum force be with you.


[1] Dual momentum underperformed its benchmark in 1979-80 and 2009-11.
[2] Since 1971, our Global Equities Momentum model exited and reentered stocks 9 times within 3 months. During this same period, the popular 10-month moving average exited and reentered stocks 20 times.
[3] See Israel and Moskowitz (2012) for more evidence of continuing momentum outperformance.
[4] See Sheifer and Vishny (1997) for more on the limits of arbitrage.
[5] For an introduction to behavioral biases, see "Are You Trying Too Hard: The Case for Systematic Decision Making" by Alpha Architect.

Last month a millennial emailed me saying he liked my book. But he wondered if the outperformance of dual momentum would disappear if he used dollar cost averaging (DCA) because he would not be able to buy  cheaply during bear markets. This is because dual momentum reduces bear market drawdowns. I showed him logically that he would actually end up with more shares at a lower average cost by using dual momentum along with DCA. I promised to post a more complete analysis using actual monthly returns.

DCA Benefits

Meanwhile, there was an internet article last week called “How Great Is Dollar Cost Averaging? You Don't Know the Half  of  It” by Eric D Nelson. The point of the article was that lackluster returns provide an opportunity to buy more shares at depressed prices.  Nelson gave an example of how the S&P 500 returned only 4.1% per year since 2000, but a DCA approach during these same years returned 8.5%. This is because you could buy more shares in the weak market years of 2001, 2002, 2008, and 2011. This turned volatility into a benefit.  Nelson concluded by saying DCA works well with stocks and not as well with portfolios containing both stocks and bonds.

Josh Brown (The Reformed Broker) in his post, "How to Make Volatility Your Bitch," used Nelson’s logic to make the point that a portfolio dropping in half twice during a 15 year period would outperform a portfolio having the same return but with zero drawdowns. This is due to the rebalancing profits of DCA. Our friend Jake (EconompicData) in his "Combining Momentum and Dollar Cost Averaging for Smoother Results," showed that regardless of volatility, a trend following moving average overlay applied to DCA could outperform DCA alone over the long run.

Relative Momentum

I will move this discussion forward and show how momentum and DCA reinforce and complement each other. Let us first look at a simple two asset relative strength momentum portfolio. We will use the S&P 500 and the MSCI All Country World ex-U.S. (MSCI World ex-U.S. before 1988) indices from January 1971 through January 2016. Every month we buy or hold whichever index had the higher total return over the past year.

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 Disclaimer page for more information.

An initial $1000 investment would have grown to $265,176 for our relative momentum portfolio versus $86,443 for just the S&P 500 and $98,055 for a benchmark made up of the S&P 500 and MSCI indices in the same 55% to 45% ratio as the amount of their use in the relative momentum model over those 45 years [1]. The compound annual growth rate of the relative momentum portfolio was 13.2% versus 10.7% for benchmark portfolio and 10.4% for the S&P 500. The average number of transactions per year with relative momentum was less than one, so relative momentum did not suffer from high trading costs or scalability issues, as can happen with momentum using individual stocks. There is an annual 250 basis point advantage here over the benchmark portfolio by using relative momentum.

There is a mountain of academic research supporting the use of relative momentum. Geczy and Samonov (2015) showed that relative momentum has outperformed buy-and-hold all the way back to the year 1800, and momentum using stock indices has outperformed individual stock momentum. This was before  transaction costs. After transaction costs, the contest wasn’t even close.

We should take note that the world is now more globally connected than it once was. Many large U.S. corporations derive a large part of their revenue from international operations. Portfolio diversification using h U.S. and non-U.S. stocks now has more to do with the strength or weakness of the U.S. dollar than it does with stock market returns.

Source: Sharpereturns.ca

Since it is counterproductive to be both long and short the U.S. dollar, it makes more sense to invest in only U.S. stocks or non-U.S. stocks depending on which is stronger, rather than in both together. Adaptive diversification based on market condition is the key to momentum investing.

DCA with Relative Momentum

Let us see now how relative momentum does when combined with DCA. We start with $1000 and this time add $100 every month. The ending value of a DCA relative strength portfolio after 45 years is $2,844,126 compared to $1,073,465 for a DCA benchmark portfolio without the use of momentum. The internal rate of return (IRR) for the DCA relative momentum portfolio is 13.4% versus 10.4% for the DCA benchmark portfolio that does not use momentum. We get a 300 basis point annual increase in return by adding relative momentum to our DCA benchmark portfolio.  


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 Disclaimer page for more information.

DCA with Dual Momentum

We will now extend our earlier logic where we said you cannot be both long and short the U.S. dollar, and we used relative momentum to select the better scenario. We can also say that the stock market cannot be going both up and down at the same time. Following the same logic as before, we will be in stocks when they are in an uptrend and move to the safety of bonds when stocks are in a downtrend. This is absolute momentum. When in stocks, we will use relative momentum to see whether we should be in U.S. or non-U.S. stocks. We will use DCA with this dual momentum and see what it does to our DCA profits. The dual momentum DCA benchmark will be 45% S&P 500, 25% MSCI All World ex-U.S., and 30% Barclays Capital U.S. Aggregate Bond (Ibbotson Intermediate Government Bond before 1976) index. This  is the allocation to each of these assets over the 45 year period using dual momentum.

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.
 
As with relative momentum DCA, the dual momentum DCA strategy has an initial $1000 investment with $100 per month additions. The dual momentum DCA strategy gives an ending value of $9,467,595. During this same period, the non-momentum DCA benchmark portfolio grew to $882,763. The IRR of the DCA portfolio with dual momentum was 17.0%, versus 9.8% for the DCA non-momentum benchmark portfolio. We get an increase in return of  over 700 basis points per year by adding dual momentum to DCA.We also get downside protection from being largely in bonds during bear markets in stocks.

When Bonds Are Good

The non-momentum DCA benchmark portfolio that is without bonds has a higher IRR than the non-momentum DCA  benchmark portfolio that uses bonds (10.4% versus 9.8%). The IRR of the dual momentum DCA portfolio that uses bonds adaptively to reduce bear market risk is substantially higher then the IRR of the relative momentum DCA portfolio that does not include bonds (17.09% versus 13.4%). But better returns is only half the story. The worst drawdown of the DCA dual momentum portfolio is less than half the worst drawdown of its non-momentum benchmark DCA portfolio.

Conclusions 

Rules-based approaches are a good thing. They can provide a disciplined framework to help us overcome self-defeating behavioral biases. DCA is a worthwhile approach since it can counteract our natural tendency to buy as we become greedy and sell when we become fearful. This is the opposite of what we should do, according to Warren Buffett.

Adding relative momentum to DCA more than doubles DCA’s ending capital after 45 years. Adding dual momentum instead of relative momentum to DCA gives more than a ten-fold increase in ending wealth, and it does so with considerably less downside risk exposure.

DCA and dual momentum complement each other well. DCA converts volatility into higher returns, while dual momentum converts differential returns into higher  returns and provides important downside portfolio protection. Both DCA and dual momentum make it easier for investors to accept the volatility of their investments. For those who have to accumulate investment capital over time, rather than go all in at once, DCA with dual momentum make a great combination.



[1] This is about the same allocation as the capitalization weighted MSCI All Country World Index (57% S&P500 and 43% MSCI) that includes both U.S. stocks and non-U.S. stocks.