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’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 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 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 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. 

August 10, 2015

Bring Data

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 Illustrated swimsuit 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.

July 20, 2015

Back to Fundamentals

After winning two consecutive national championships, the Green Bay Packers lost a game due to sloppy play. Coach Lombardi called a meeting the very next day to get his team back to fundamentals. When all the players were assembled, Lombardi held a football high up in the air and declared, “Gentlemen, this is a football!” From the back of the room, running back Paul Hornung shouted back, “Coach, can you slow down?”

Sometimes we all need to be reminded of fundamentals. The fundamental goal of investing should be to receive the most gain with the least pain. The question then becomes, how do we achieve this?

Asset Returns

The first step is to select the best investment assets. The following chart shows the annualized real returns of U.S. stocks, bonds, and Treasury bills since the years 2000, 1965, and 1900.

Source: Credit Suisse Global Investment Returns Yearbook 2015 

Over the past 100+ years, stocks have provided more than three times the real return of bonds despite the unusually strong bond market of the past 35 years. A much higher long-run return from stocks makes sense, since stocks are considerably riskier than bonds. They should therefore compensate investors with a higher risk premium.

The following chart by Wharton professor Jeremy Siegel shows the same dynamic over 200 years from the years 1802 to 2012.

Source: Stocks for the Long Run, 5th edition, Jeremy Siegel

Again we see that over the long-run, stocks have earned the highest return by a large margin. The annualized real return of U.S. stocks has been nearly twice as high as the annualized return of bonds since 1802.


Even though returns are maximized, the problem with holding only stocks in one's portfolio is their high volatility and negative skewness. These create considerable left tail/drawdown risk. There have been two bear market drawdowns in U.S. stocks greater than 50% during just the past 15 years.

Not only can large equity erosions create discomfort and uncertainty in the minds of investors, but they can  cause investors to react in ways that are counter to their own best interests. The yearly Dalbar studies show that investors underperformed the funds they were invested in by an average of 4% annually over the past 20 years. Poor timing by investors is often attributable to emotionally induced buying and selling.


In order to reduce the emotional stress and poor timing decisions triggered by high stock market volatility, investors have traditionally diversified their portfolio into assets other than stocks. The main alternative has historically been bonds. However, as we saw from the above charts, our long-run expected return decreases substantially as we add bonds or assets other to an all stock portfolio.

Yet this diminished return has not stopped investors from adopting so-called balanced portfolios, such as the common one that allocates 60% to stocks and 40% to bonds.  Even Harry Markowitz, the father of modern portfolio theory, split his personal investments equally between stocks and bonds.

Based on financial planning principles, some investors start off with a higher allocation to stocks in their early years and then switch to a greater allocation to bonds as they grow older. This may no longer be as prudent a strategy as it once was. The average life expectancy today of someone reaching the age of 65 is 19 years. The lengthening of retirement years and emphasis then on bond investing can aggravate the problem of portfolio under performance. Investors may need a way to keep growing their investment assets well beyond their retirement age.

Wealth Accumulation

What exactly does the old paradigm of a balanced stock and bond portfolio mean in terms of wealth accumulation over the long-run? To determine this, I looked at a rolling 40-year window comparing the performance of the S&P 500 to a portfolio invested 60% in the S&P 500 and 40% in10-year U.S. Treasuries from 1900 through 2014.

During that time, the average annual total return of the S&P 500 was 10.0%, compared with 8.3% for the 60/40 stock/bond portfolio.[1] Applying these average rates of return to a 40-year investment horizon, a $10,000 initial investment in the S&P 500 would have grown to $537,000 before expenses and taxes, while a $10,000 investment in the 60/40 stock/bond portfolio would have become only $273,500. Due to the power of compounding, an all-stock portfolio would have resulted in almost twice the accumulated wealth of a 60/40 balanced portfolio.

Many do not realize the impact over time of an extra 1-2 % per year in return and what a large difference it can have on one's accumulated wealth. (There might be far less money under active management now if investors were fully aware of this fact.)

High Costs of Diversification

In addition to lower expected risk premiums, there are substantially higher costs associated with diversification that many investors are not fully aware of. In their study “Fees Eat Diversification’s Lunch,” Jennings and Payne (2014) state that fees on diversifying assets are astonishingly high relative to their benefits. (On a real time basis, other assets have to compete with U.S. stock index funds having annual expense ratios of only 4 or 5 basis points.)

 In the 1970s, U.S. investors started to look seriously at the diversifying their stock holdings internationally, despite the fact that non-U.S. stocks since 1900 have returned on average 2% less per year than U.S. stocks. Jennings and Payne found that fees reduced the benefit of international diversification by one-third for small institutional investors. Fees almost completely eliminated any diversification benefit from investing in emerging market bonds, hedge funds, and private equity. In looking at 45 different asset classes, Jennings and Payne found that fees consumed over half the expected benefit in more than 60% of those markets.

A Practical Solution

Is there anything investors can do to reduce their downside exposure during equity bear markets without giving up half their accumulated wealth in the process? Our dual momentum based Global Equities Momentum (GEM) methodology diversifies one’s portfolio in a more intelligent way.[2] GEM’s core holding is the S&P 500 in order to capture the highest long-run risk premium. GEM switches between U.S. and international stocks according to relative strength price momentum, which can improve the expected return from holding stocks. The GEM model also switches between stocks and bonds in accordance with trend-following absolute (time-series) momentum. When equities have been going up according to the rules of absolute momentum, GEM stays fully invested in stocks. When the trend of the stock market turns negative, GEM switches into low-duration aggregate bonds. Bear markets in stocks often foreshadow economic recessions with falling or flat interest rates. These are often the best times to hold bonds. Dual momentum is an adaptive approach that diversifies in a temporal way, which makes the most sense.

Here is the performance of GEM compared with the S&P 500 index and a portfolio allocated 60% to the S&P 500 and 40% to10-year Treasuries from January 1974 through June 2015. Positions are rebalanced monthly.

Performance of GEM

  GEMS&P 500 60/40
Ann Rtn 17.73 12.33 10.76
Std Dev 12.36 15.43   9.74
Sharpe   0.89   0.41   0.50
Max DD-17.84-50.95-30.54

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.

GEM has a considerably lower maximum drawdown than the 60/40 stock and bond portfolio. In addition to providing greater downside protection than afforded by the 60/40 portfolio, GEM returns have been significantly higher than the returns of the S&P 500 portfolio. Large losses in the S&P 500 need to be recouped before stocks can again show a net profit. For example, it takes a 100% gain to get back to even after a 50% loss. By sidestepping severe bear market losses, GEM can earn higher overall profits. 

GEM remains in stocks when the trend of the stock market is positive in order to capture all it can of the high risk premium associated with stocks. GEM retreats to the safety of bonds during the 30% of the time when stocks are weak and bonds are often their strongest.

Possible Concerns

Why would anyone want to adopt a permanent stock/bond portfolio with its fixed income drag on performance when a simple dual momentum approach like GEM has shown considerably less downside exposure and substantially higher expected return than either an all-stock or a balanced stock/bond portfolio? 
The first reason for some is that the future may not be like the past. However, dual momentum is a simple model with several hundred years of out-of-sample performance to support it. The GEM look back parameter used by Cowles and Jones in 1937, has held up well back to the early 19th century and up to the present time. There are also good reasons, as described in my book, why the momentum effect should continue to persist.

The next concern may be occasional re-entry lags when a new bull market begins after dual momentum has protected your portfolio from the preceding bear market. There may also be occasional whipsaw trades at other times that can cause dual momentum to temporarily lag behind the stock market. Over the past 40 years, GEM underperformed the stock market in 1979-80 and 2009-10. No strategy can outperform all the time.

Career risk associated with tracking error, long-standing aversion to market timing, and confirmation bias may keep institutional investors from ever using dual momentum.[3] As an encouraging note for the rest of us, this attitude should help keep momentum from being over exploited.

Since bonds make up 20% of GEM's profits, there may be some concern that bonds may not perform as well in the future as they have over the past. GEM uses aggregate bonds with around only a 5.3 year average duration, which gives them relatively low sensitivity to interest rate changes. GEM uses bonds when there are bear markets in stocks. These often precede recessions which often lead to falling rather than rising interest rates.

Finally, the trend-following component of GEM is slow moving so as to minimize whipsaws. This means that GEM is still subject to the volatility associated with short-term stock market fluctuations. Very conservative investors can always allocate a modest portion of their portfolio permanently to bonds in order to attenuate this volatility.

Volatility Attenuated Dual Momentum

Here is what would have happened if we had allocated 75% of our investment portfolio to GEM and 25% permanently to aggregate bonds from January 1974 through June 2015.

Annual Rtn
Std Dev
Max DD

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.

The GEM/25 allocation now has the same volatility as the 60/40 portfolio, but GEM/25 has a substantially higher annual return and Sharpe ratio. The maximum drawdown of GEM/25 is only 39% as large as the maximum drawdown of the 60/40 portfolio.

We see that dual momentum in various forms meets our fundamental goal of investing – the most gain with the least pain.

[1] Both portfolios had the same 40-year minimum average return of 5.4%. On the basis of avoiding the lowest average portfolio return, the 60/40 portfolio was not any better than the S&P 500 portfolio over a 40-year time frame.
[2] The GEM model is fully disclosed in my book, Dual Momentum Investing. It takes less than 5 minutes per month to apply it.

[3] Even those who understand and appreciate momentum can be subject to long-standing biases that keep them from using momentum in a significant way.