December 17, 2015

Why Does Dual Momentum Outperform?

Those who have read my momentum research papers, book, and this blog should know that simple dual momentum has handily 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 outperformed its benchmark over the long run, although the amount of outperformance has varied 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 some explanations why momentum has worked well. The reasons 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 shown the ability to provide higher returns and reduced risk exposure.

The behavioral explanation for momentum centers on initial investor underreaction of prices to new information. This is followed later by overreaction. Underreaction comes from anchoring, conservatism, and the slow diffusion of information. Overreaction is due to herding (the bandwagon effect), representativeness (assuming continuation of the present), and overconfidence. Price gains attract more buying which leads to further price gains. The same is true with 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 a powerful primordial instinct built into our brain chemistry and DNA. It is therefore unlikely to disappear. Representativeness and overconfidence are also evident when there are strong momentum-based trends.

Furthermore, investors' loss aversion may decrease as they see prices rise and they become overconfident. Their loss aversion may similarly increase as prices fall and they become more fearful. Studies have shown that investors are about 2 times more likely to avoid losses than they are willing to seek gains. These natural psychological responses are also unlikely to change in the future.

One can 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 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 investor under or overreaction with stocks and found evidence that supported the overreaction explanation as the source of momentum profits, especially when idiosyncratic volatility was high.

Many economic trends, not just stock prices, get overextended and then mean revert. The business cycle trends and mean reverts. Since the late 1980s, researchers have known that stock prices are long-term mean reverting [2]. Mean reversion supports the premise that stocks overreact and become overextended, which leads to their mean reversion. We can 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 differences between dual, relative, and absolute momentum. Here is a chart of our GEM model and its relative and absolute momentum components 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 use 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. Relative momentum profits in this case are also aided by neglect as investors fail to capture more profit from non-U.S. stocks due to strong home country bias.

Even though relative momentum gives us substantially increased profits, it does nothing to alleviate downside risk. Relative momentum volatility and maximum drawdown are comparable to the underlying indices themselves.

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 much 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 can mean revert.

The second way overreaction comes into play is when we avoid it 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. And it did this while maintaining the same stability as absolute momentum. We should keep in mind that stock market overreaction, as the driving force behind dual momentum, is not likely to disappear.

Distribution of Returns

Looking at things a little differently, the following histogram shows the distribution of  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.



S&P 500
S&P 500
Jan 71-Dec 72
Oct 74-Nov 80
Jan 73-Sep 74
Aug 82-Aug 87
Dec 80-Jul 82
Dec 87-Aug 00
Sep 87-Nov 87
Oct 02-Oct 07
Sep 00-Sep 02
Mar 09-Nov15
Nov 07-Feb 09
Average Return
Average Return

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 and had to reenter stocks a month or several months later [3].  Relative momentum also overcame lost profits when trend-following absolute momentum kept GEM out of stocks as new bull markets were just getting started. With only absolute momentum, the bull market average return would have been 214.9% instead of the 289.9% return that came from using both relative and absolute momentum.

What really stands out though are the average profits that GEM earned in bear market environments when stocks lost an average of 37%. Without only relative momentum, the bear market average return would have been -33.1% instead of the 3.6% positive return that came form using both relative and absolute momentum. Absolute momentum, by side stepping bear market losses, is what accounted for much of GEM’s outperformance.

Large losses need much larger gains to recover from those losses. For example, a 50% loss requires a 100% gain to get back to breakeven. By avoiding large losses in the first place, GEM has not been 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.

But increased profits through relative strength and loss avoidance through absolute momentum are only half the story. Avoiding losses also contributes to our peace of mind. It 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 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 10 instances of absolute momentum causing GEM to exit stocks and reenter them within the next 3 months, foregoing an average 3.1% difference in return.  

November 21, 2015

Bring More Data

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 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 goes back to only the early 1990s.) This is the amount of data that most sector rotation programs use to backtest their strategies.
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 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:

S&P 500
Average Annual Return
Standard Deviation
Sharpe Ratio
Maximum Drawdown

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. 

Because the monthly correlation between GEM and DMSR is only 0.59, sector rotation may still have a  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. Future drawdowns may be higher and returns lower than expected.

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:

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 have  been 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 geographically diversified 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 before transaction costs, which would be much higher for stocks. Country equity indices outperform global equity sectors, which is consistent with our updated findings. 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 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, avoid the enormously high transaction costs associated with rebalancing momentum-based stock portfolios, and are much less susceptible to scalability issues. They are still subject to  though to high short-term volatility and significant periods of benchmark underperformance.

October 18, 2015

Multi-Factor Investing

Multi-factor investing that combines value, momentum, quality (profitability), or low volatility factors is today’s hot new investment approach. There has been an explosion of multi-factor ETFs recently with 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.

But multi-factor funds can have their aome quirks and issues. If the larEditge variety of factors is a “factor zoo,” then multi-factor approaches are a “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 problem areas: tractability, scalability, and volatility. Regarding  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 see this happen without losing patience and giving up on their factor portfolios. 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.

But only two out of the twelve large cap U.S. equities single factor ETFs include only 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% of the stocks that are in their investable universes. This makes them, to a great extent, closet index funds with relatively high fees.

Their large sizes ($8.3 billion, $23.5 billion, and $34.6 billion) 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 momentum. One of the largest momentum funds, 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. AQR manages considerably more than this in the momentum arena through their hedge fund and multi-factor funds. Large amounts of investment capital may make it difficult for single factor funds to focus only on the small number of stocks that appear in their top factor deciles.

The third problem for single factor portfolios is increased volatility and large bear market drawdowns that can accompany value, momentum, and small cap factors. Trend following filters, such as absolute momentum, can help reduce downside exposure in long-term bear markets, but they do little to reduce 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 reduced some by using intelligently constructed multi-factor portfolios. Multiple factors reduce tracking error, since it is unlikely that several factors will substantially underperform 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, you can sometimes reduce the volatility and bear market drawdowns associated with value and momentum factors by combining these factors with less volatile ones, such as quality and low volatility.

An issue associated with multi-factor funds, however, is their average annual expense ratio of 41 basis points for what are often enhanced index funds. Until 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 last month when Goldman Sachs entered the ETF business with an offering called Goldman Sachs Active Beta U.S. Large Cap Equity (GSLC). It uses the value, momentum, quality, and low volatility factors. Here is a description of how they determine these:

•      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 before the rebalancing 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 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 that allocates 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.

The fund came into existence because some of Goldman’s largest clients wanted to invest using an ETF wrapper to reduce their tax consequences. Because of this sponsorship, the fund was set up with an annual expense ratio of only 9 basis points.[2] 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 does not have a trend following filter like absolute momentum to help it avoid severe bear market drawdown. GSLC is also unable to enjoy international diversification during those times when international stocks show greater relative strength than U.S. stocks. Its future performance also may not be as good as expected, since, like most other factor ETFs, GSLC relies on data mining to determine its factor parameters.
Multi Factor Funds

Symbol Factors Assets Stocks Exp Ratio
4 Factor

Goldman Sachs Active Beta U.S. Large Cap  GSLC Value, Mom, Quality, LoVolty $78 m 432 0.09
ETFS Diversified Factor U.S. Large Cap SBUS Value, Mom, Size, LowVolty $17 m 492 0.40
iShares Factor Select MSCI USA LRGF Value, 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   


Value, Mom, Quality

$23 m


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, Profits $79 m 772 0.35
AQR Large Cap Multi-Style (non-ETF) QCELX Value, Mom, Profits $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


Value, Quality

$47 m


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]  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.

September 8, 2015

Book Review: DIY Financial Advisor: A Simple Solution to Build and Protect Your Wealth

I have always looked favorably upon do-it-yourself investing (DIY). It was a prominent feature of my own book. So I was 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 “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 more studies and meta-studies that models are 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 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 one 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 better job than MVO or other optimization approaches. The rest 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 provided the highest long-run returns, permanent multi-asset allocation schemes sacrifice some potential return in pursuit of volatility reduction.[1]  Someone who accepts momentum investing might prefer instead to hold only the top performing asset classes instead of all of them.

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 part of the portfolio into 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 adopting TMOM themselves, GVF combines 12-month absolute momentum (TMOM) with a 12-month MA on a 50/50 basis to create an 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 show that TMOM and ROBUST are at least equal in their performance, and TMOM may be better. Table 7.8 indicates 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. They say ROBUST is the winner in two other cases (SPX and REIT) where there is the same small difference in Sharpe and Sortino ratios. You cannot draw any meaningful conclusions when Sharpe ratios are so close. 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 also is TMOM when 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 even stronger for TMOM over ROBUST, since ROBUST trades more frequently than TMOM [3].

GVF finishes up chapter 7 by giving a good reason, besides the usual behavioral finance ones, 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 the 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 also overextend on the upside.

In Chapter 8, GVF discusses security selection using value and momentum. The 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 their earlier book Quantitative Value. GVF shows that the top decile of value stocks from 1927 through 2014 outperformed the market, but that value had much higher volatility, making the Sharpe ratios of value and the market about the same. Both value and market had 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 poor fundamentals. These stocks may remain depressed or become even more depressed if they are on the verge of bankruptcy. For this reason, it may be better to invest in other than the 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 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.

In Chapter 9, GVF gets to the heart of DIY investing by suggesting ways for investors to implement a DIY approach. The first 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 possible since Yahoo finance does not provide monthly charts. But you could use TMOM instead of an MA approach by inputting the appropriate ETFs into and selecting a one year SharpChart that you could bookmark and look at monthly.[4]

GVF next presents enhancements to their 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. But since timing filters are meant to remove equities entirely from one’s portfolio during bear markets, the balanced and moderate allocations may be too conservative for some investors. Equities have provided the highest returns historically, and large allocations in other assets should diminish one’s accumulated wealth over a lifetime of investing.

GVF provides a free service on their website that applies the ROBUST filter to each asset class for balanced, moderate, and aggressive portfolios. 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 to maximize the benefits of value and momentum investing. Modern portfolio principles tell us that a well-diversified portfolio of at least 30 stocks can eliminate most idiosyncratic risk. But this may not true for portfolios of stocks having higher bankruptcy risk. Concentrated portfolios of 30-50 of the cheapest value stocks may still be too risky.

The same 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 to have a well-diversified portfolio.

Another drawback to an individual stock momentum portfolio holding 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 non-takeover stocks that showed less momentum.

For 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?

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 you do not use a timing model overlay, you should be prepared for some 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 through the use of screeners, such the one that Alpha Architect provides. Increasing amounts of capital buying the same limited number of stocks may diminish 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 usually not go about constructing all these 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 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 50 or fewer stocks, although some may be forthcoming. There are also no ETFs that hold 50 or fewer 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). But USCI is required to maintain positions in six different commodity sectors at all times. This means some sectors may not always have positions 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. 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 do not 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 beliefs, and the desire to be a hero. These psychological excuses are worth thinking about. .

If I had been GVF, I might have said more about why investors who adopt DIY investing might abandon or change it into something that is no longer model-based.  DIY investing can 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 stocks too volatile, while larger value and momentum portfolios may be impractical for DIY investors.
•    Both value and momentum are subject to high drawdowns. The use of a timing model to reduce drawdown may be difficult with large portfolios of individual stocks.
•    Turnover is high, and transaction costs may offset all or most momentum profits for portfolios of individual stocks.
•    The most practical approach is to apply a timing overlay model to asset class ETFs.  Investors can 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 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) is the same as 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. We use (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.

[4] Alternatively, you could track a portfolio on Morningstar that shows performance over the past year. 
[5] Timing models have a more beneficial effect on momentum portfolios than on value portfolios.