QTAA Update: Global Tactical 5/10

Below we have updated our 2006 white paper.  While you can download the full 70+ page paper here, I’ve also chopped it up into a series of more digestible posts for the blog.  



Given the ability of this very simplistic market-timing rule to add value to various asset classes, it is instructive to examine how the returns would look in the context of an investor’s portfolio.   Here we introduce a version of the timing model we refer to as “Global Tactical Asset Allocation” or “GTAA”.   GTAA consists of five global asset classes:  US stocks, foreign stocks, bonds, real estate and commodities.  The returns for a buy and hold allocation are referenced as “Buy & Hold” or “B&H” and are equally weighted across the five asset classes.  The timing model also uses equal weightings and treats each asset class independently – it is either long the asset class or in cash with its 20% allocation of the funds.  Figure 12 illustrates the percentage of months in which various numbers of asset classes were held.  It is evident that the system keeps the investor 60%-100% invested the vast majority of the time (approximately ~80% of the time the portfolio is at least 60% invested).  On average, the investor is 70% invested.

Figure 12: Percent of the Time Invested, 1973-2012




Figures 13 and 13b below present the results for the buying and holding of the five asset classes equal-weighted versus the timing portfolio.  The buy and hold returns are quite respectable on a stand-alone basis and present evidence of the benefits of diversification. 


Figure 13: Buy & Hold vs. Timing Model, 1973-2012, log scale





Figure 13b: Buy & Hold vs. Timing Model, 1973-2012, non-log scale





However, the additional advantages conferred by timing are striking.  Timing results in a reduction of volatility to single-digit levels, as well as a single-digit maximum drawdown.  Drawdown is reduced from 46% to less than 10%, and the investor would have only experienced one down year of less than -1% since inception in 1973.  Figure 19 details the yearly returns, and post-2005 is highlighted as the out-of-sample period. 


Figure 14: Yearly Returns for Buy & Hold vs. Timing Model, 1973-2012


It is possible that Siegel (or others) have optimized the moving average by looking back over the period tested.  As a check against optimization, and to show that using the 10-month SMA is not a unique solution, Figure 15 presents the stability of using various moving averages lengths ranging from 3 to 12 months.  Calculation periods will perform differently in the future as cyclical and secular forces drive the return series, but all of the parameters below seem to work similarly for a long-term trend-following application. 

Figure 15: Parameter Stability of Various Moving Average Lengths, Timing Model 1973-2012



While it is instructive to examine the model in various asset classes, the true test of a model is how it performs out of sample in real time.  Since the paper was originally published in 2006 with results up to 2005, returns after 2005 should be seen as out of sample.  Figure 16 illustrates the returns for B&H and timing portfolios. 



Figure 16: Summary Annualized Returns for B&H vs. Timing Model, 2006-2012




The model performed exactly as one would expect it to from historical data.  Namely, even though it only outperformed in three out of seven years, it beat buy and hold by over two percentage points per year, with much less volatility and most importantly to many investors, lower drawdowns.

QTAA Paper Update: Manage Your Risk 4/10

Below we have updated our 2006 white paper.  While you can download the full 70+ page paper here, I’ve also chopped it up into a series of more digestible posts for the blog.  



There are a few criteria that are necessary for a model to be simple enough for investors to follow, and mechanical enough to remove emotion and subjective decision-making. 


They are:

1.  Simple, purely mechanical logic.

2.  The same model and parameters for every asset class.

3.  Price-based only.


Moving-average-based trading systems are the simplest and most popular trend-following systems (see for example Taylor and Allen (1992) or Lui and Mole (1998)).  For those unfamiliar with moving averages, they are a way to reduce noise.  The example below shows the S&P 500 with a 10-month simple moving average (SMA).

Figure 6 – S&P 500 vs. 10-Month Simple Moving Average, 1990-2012



The most often cited long-term measure of trend in the technical analysis community is the 200-day simple moving average.  In his 2008 book Stocks for the Long Run 5/E: The Definitive Guide to Financial Market Returns & Long-Term Investment Strategies, Jeremy Siegel investigates the use of the 200-day SMA in timing the Dow Jones Industrial Average (DJIA) from 1886 to 2006.  His test bought the DJIA when it closed at least 1 percent above the 200-day moving average, and sold the DJIA and invested in Treasury bills when it closed at least 1 percent below the 200-day moving average. 

 He concludes that market timing improves the absolute and risk-adjusted returns over buying and holding the DJIA. Likewise, when all transaction costs are included (taxes, bid-ask spreads, commissions), the risk-adjusted returns are still higher when employing market timing, though timing falls short on an absolute return measure.

When applied to the NASDAQ Composite Index since 1972, the market timing system thoroughly outperforms buy-and hold, both on an absolute and risk-adjusted basis. Siegel finds that the timing model outperforms buy and hold by over 4% per year from 1972-2006 even when accounting for all costs, and with 25% less volatility.  Unfortunately, Siegel does not report drawdown figures, which would have further demonstrated the superiority of the timing model.  (Note: Siegel’s system is twice as active as the system presented in this paper, thus increasing the transaction costs).  Sigel is updating the book with a 2013 edition, and we look forward to see the results including the 2006-2012 period.

It is possible that Siegel already optimized the moving average by looking back over the period in which it is then tested.  To alleviate fears of data mining, the approach will be examined across various parameters and other markets to test for validity.

The system is as follows:


Buy when monthly price > 10-month SMA.


Sell and move to cash when monthly price < 10-month SMA.


1.  All entry and exit prices are on the day of the signal at the close.  The model is only updated once a month on the last day of the month.  Price fluctuations during the rest of the month are ignored.

2.  All data series are total return series including dividends, updated monthly.

3.  Cash returns are estimated with 90-day Treasury bills, and margin rates (for leveraged models to be discussed later) are estimated with the broker call rate.

4.  Taxes, commissions, and slippage are excluded (see the Practical Considerations section later in the paper).


S&P 500 FROM 1901 – 2012


To demonstrate the logic and characteristics of the timing system, we test the S&P 500 back to 1901.  Total return series is provided by Global Financial Data and results pre-1971 are constructed by GFD.  Data from 1901-1971 uses the Standard and Poor’s Composite Price Index and dividend yields supplied by the Cowles Commission and from S&P.

Figure 7 presents the annualized returns for the S&P 500 and the timing method for the past 100+ years.  A cursory glance at the results reveals that the timing solution improved compounded returns while reducing risk, all while being invested in the market approximately 70% of the time and making less than one round-trip trade per year.  (Volatility is measured as the annualized standard deviation of monthly returns.)

Figure 7: S&P 500 Total Returns vs. Timing Total Returns (1901-2012)



The timing system achieves these superior results while underperforming the index in roughly half of all years since 1901.  One of the reasons for the overall outperformance is the lower volatility of the timing system.  It is an established fact that high volatility diminishes compound returns.  This principle can be illustrated by comparing average returns with compounded returns (the returns an investor would actually realize.)  The average return for the S&P 500 since 1901 was 11.26%, while timing the S&P 500 returned 11.22%.  However, the compounded returns for the two are 9.32% and 10.18%, respectively.  Notice that the buy and hold crowd takes a hit of nearly 200 basis points from the effects of volatility, while timing suffers a smaller decline of around 100 basis points.  Ed Easterling has a good discussion of these “volatility gremlins” in John Mauldin’s 2006 book, Just One Thing: Twelve of the World’s Best Investors Reveal the One Strategy You Can’t Overlook.

Figure 8 shows the superiority of the timing model over the past century, largely avoiding the significant bear markets of the 1930s and 2000s.  Figure 8b shows that timing would not have left the investor completely unscathed from the late 1920s early 1930s bear market, but it would have reduced the drawdown from a catastrophic 83.66% to a more manageable 42.24%. 

Figure 8: S&P 500 Total Returns vs. Timing Total Returns (1901-2012)


Figure 8b: S&P 500 Drawdowns vs. Timing Drawdowns (1901-2012)



Figure 9 is charted on a non-log scale to detail the differences in the two equity curves. Examining the most recent 22 years, a few features of the timing model stand out. First, a trend-following model can underperform buy and hold during a roaring bull market similar to the U.S. equity markets in the 1990s.  On the flip side, the timing model can avoid lengthy and protracted bear markets.  Consequently, the value added by timing is evident only over the course of entire business cycles. 

For example, the timing model exits a long position in October of 2000, thus avoiding two of the three consecutive years of losses, and its 16.52% drawdown is much shallower than the 44.73% setback suffered by buy-and-hold investors.  The timing model again exited the S&P 500 on December 31, 2007 and avoided the entire bear market of 2008-2009 and the 50.95% drawdown.


Figure 9: S&P 500 Total Returns vs. Timing Total Returns (1990-2012)




A glance at Figure 10 presents the ten worst years for the S&P 500 for the past century, and the corresponding returns for the timing system.  It is immediately obvious that the two do not move in lockstep.  In fact, the correlation between negative years for the S&P 500 and the timing model is approximately -0.38, while the correlation for positive years is approximately 0.83.  This reflects the ability of the timing model to stay long in up markets while exiting the long position during down markets.

Figure 10: S&P 500 Ten Worst Years vs. Timing, 1900-2012




Figure 11 gives a good pictorial description of the results of the trend-following system applied to the S&P 500.  The timing system has fewer occurrences of both large gains and large losses, with correspondingly higher occurrences of small gains and losses.  Essentially, the system is a model that signals when an investor should be long a riskier asset class with potential upside, and when to be out and sitting in cash.  It is this move to a lower-volatility asset class (T-bills) that drops the overall risk and drawdown of the portfolio.  Most importantly, it avoids the far left tail of big negative losses.

Figure 11: Yearly Return Distribution, S&P 500 and Timing 1900-2012




Appendix B breaks down the returns down by decade for the S&P 500 and the timing model.  While the timing model outperforms in about half of all decades on an absolute basis, it improves risk-adjusted returns in about two-thirds of all decades and improves drawdown in all but two decades.  Another interesting observation is the wide variance in Sharpe ratios per decade for buy and hold, ranging from -0.23 to 1.44.  The past decade has seen compound returns of -0.94% per year for buy and hold while the 1950s saw returns of 19% per year. 

QTAA Paper Update: Go Global 3/10

Below we have updated our 2006 white paper.  While you can download the full 70+ page paper here, I’ve also chopped it up into a series of more digestible posts for the blog.  


Modern portfolio theory holds that there is a tradeoff for investing in assets – you get paid to assume risk.  Figure 3 shows the five asset classes that we will examine in this paper and their returns since 1973 (later in the paper we expand the study to include more asset classes.)

Unless otherwise noted all data series are total return series including dividends and income, and from Global Financial Data:


US Large Cap, S&P 500

Foreign Developed, MSCI EAFE

US 10-Year Government Bonds

Commodities, Goldman Sachs Commodity Index

Real Estate Investment Trusts, NAREIT Index


While the indexes traveled different routes from start to finish, most of the asset classes finished with similar returns over the time period.  The exception was bonds, which trailed the other asset classes, an outcome that is to be expected due to their lower volatility and risk.  The fact that bonds were even close in absolute performance to the other equity-like asset classes reflects the greater than twenty year bull market that took yields from double-digit levels to near 2% today.

Figure 3 – Asset Class Returns 1973-2012, Log Scale



With US assets set to produce uninspiring returns, it makes a lot of sense to look at global assets as well as real assets to protect a portfolio from rising inflation.  Figure 4 shows that, while these are some pretty nice returns for these asset classes historically, they are coupled with some large drawdowns.  With the exception of U.S. government bonds, which declined less than 20%, the other four asset classes had drawdowns around 50% to 70%.  If an investor were to include inflation or take the data back further, those drawdowns only get bigger.  Higher resolution daily data and longer look back periods can only increase the drawdown amount. A good rule of thumb is that risky asset classes have Sharpe ratios that cluster around 0.20, while a diversified portfolio is around 0.40. 

Figure 4 – Asset Class Maximum Drawdowns 1973-2012



To give the reader a visual perspective of drawdowns, Figure 5 shows the drawdowns for stocks for the past 108 years.  Drawdowns of 10%-20% are fairly frequent, with 30%-40% drawdowns less so.  The large 1920s bear market dominates the figure with a drawdown over 80%.

Figure 5 – S&P 500 Drawdowns, 1900-2012



The former manager of the Harvard endowment, Mohamed El-Erian stated inKiplinger’s in 2009, “Diversification alone is no longer sufficient to temper risk. In the past year, we saw virtually every asset class hammered. You need something more to manage risk well.”

This paper examines a very simple quantitative market-timing model that manages risk.  This trend-following model is examined on the U.S. stock market since 1900 before testing across four other markets.  The attempt is not to build an optimization model, but rather to build a simple trading model that works in the vast majority of markets.  The results suggest that a market timing solution is a risk-reduction technique that signals when an investor should exit a risky asset class in favor of risk-free investments.  Instead of offering a lengthy review of the momentum and trendfollowing literature here, the material is included in the Appendix. 

The approach is then examined in an allocation framework since 1973 where the empirical results are equity-like returns with bond-like volatility and drawdown.  Later in this update we also examine other extensions including alternate allocations, cash management strategies, and more asset classes.

Mutual Fund vs ETF Fees

The granddaddy of mutual fund vs. ETF fees, active and passive (courtesy of Morgan Stanley’s very excellent ETF Quarterly):

Screen Shot 2013-06-04 at 10.17.04 PM

QTAA Paper Update: The Current Challenge 2/10

Below we have updated our 2006 white paper.  While you can download the full 70+ page paper here, I’ve also chopped it up into a series of more digestible posts for the blog.  


While investors have benefited from strong equity markets in 2012 with the S&P 500 up approximately 16%, the new millennium has been challenging for most investors.

US stocks have returned a meager 1.65% per year from 2000 – 2012, and factoring in inflation, have returned -0.76% per year.  That is, if the investors had the ability to sit through two gut-wrenching bear markets with declines of over 45%, and according to recent DALBAR studies, many have not.  The average equity investor underperformed the S&P 500 by 7.85% in 2011, and underperformed the index by 4.32% over the past 20 years.  (Bond investors are equally as bad.)

One of the reasons for the subpar returns is simple – valuations started the 2000s at extreme levels.  The ten-year cyclically adjusted price-to-earnings ratio (CAPE) reached a level of 45 in December 1999, the highest level ever recorded in the US.  (We examine approximately 40 global stock markets and how to use global CAPEs in our paper “Global Value: Building Trading Models with the 10 Year CAPE”.)

Figure 1 – Ten-Year Cyclically Adjusted Price-To-Earnings Ratio (CAPE), 1881-2011




As you can see in the figure below, future returns are highly dependent on starting valuations.  The current reading as of the end of 2012 is 21.55, about 30% above the long-term average of around 16.5.  At the current levels of 20-25, future returns have been an uninspiring 6% nominal, and 3% real since 1881.  Not horrific, but not that exciting either.



Figure 2 – Ten-Year CAPE vs. Future Returns, 1881-2011




US government bonds on the other hand proved to be a wonderful place to invest during the past twelve years.  The compound return was 7.07% and a nice 4.5% after inflation.  The problem with these returns, however, is that they come at the expense of future returns as yields have declined to all time low levels in the US below 2%.

Future bond returns are fairly easy to forecast – it is simply the starting yield.  Your ten-year nominal return for buying US government bonds will be around 2% currently if held to maturity.

So, investors are presented with the following opportunity set (assuming 3% inflation going forward, and rounding to make it simple):


US stocks:  6% nominal, 3% real

US Bonds:  2% nominal, -1% real


That leaves a 60/40 investor with a 4.4% nominal return, or a 1.4% real return.  Not exactly exciting!

So where should investors look for outsized returns while managing their risk?  We examine the effects of expanding a traditional 60/40 allocation into a more global allocation in the coming pages.  We then overlay some simple risk management in hopes of protecting a portfolio against brutal bear markets.

QTAA Paper Update: Introduction 1/10

Below we have updated our 2006 white paper.  While you can download the full 70+ page paper here, I’ve also chopped it up into a series of more digestible posts for the blog.  

Updates included in the 2013 paper include:


1.  Results are extended to include the years 2009-2012.

2.  Additional asset classes are included (from 5 to 13, although still includes results from original 5 model).

3.  Alternative cash management strategies are included.

4.  Additional conservative and aggressive approaches are included.

5.  Alternative allocations are included.

6.  References translated into hyperlinks.




In this paper we update our 2006 white paper “A Quantitative Approach to Tactical Asset Allocation” with new data from the 2008-2012 period. How well did the purpose of the original paper – to present a simple quantitative method that improves the risk-adjusted returns across various asset classes – hold up since publication?  Overall, we find that the models have performed well in real-time, achieving equity like returns with bond like volatility and drawdowns. We also examine the effects of departures from the original system including adding more asset classes, introducing various portfolio allocations, and implementing alternative cash management strategies. 



 Much has happened in the world since the original publication of this white paper in 2006.  However, change has always been the constant, and indeed has anything new really been seen in our world of investing?  Bubbles, defaults, government interventions, bear markets, downgrades, quantitative easing, fortunes made and lost – they’ve all happened before. (For a lengthy examination of bubbles, see our paper “Learning to Love Investment Bubbles”.)

Since publication of the original paper we have seen a devastating bear market in 2008 – 2009.  The normal benefits of diversification disappeared as many non-correlated asset classes experienced large declines simultaneously.  Commodities, REITs, and foreign stock indices all suffered drawdowns over 50%.  (Drawdown is the peak-to-trough decline an investor would experience in an investment, and we calculate it here on a monthly basis.)  The classic barometer of stocks, the S&P 500 Index, declined 36.77% in 2008 alone. 

The fantastic book Triumph of the Optimists: 101 Years of Global Investment Returns (and 2012 update here), illustrates that many global asset classes in the twentieth century produced spectacular gains in wealth for individuals who bought and held those assets for generation-long holding periods, but the assets also went through regular and painful drawdowns like 2008. All of the G-7 countries have experienced at least one period where stocks lost 75% of their value.  The unfortunate mathematics of a 75% decline require an investor to realize a 300% gain just to get back to even – the equivalent of compounding at 10% for 15 years!

For some long term perspective, below are some long term charts based on the data from Morningstar / Dimson Marsh Staunton.  Below are the best, middle, and worst case scenarios for the main asset classes of sixteen countries from 1900-2011.  All are real return series on a log graph (except the last one).

First, here are the best cases for returns on your cash.  This chart goes to show that leaving cash under your mattress is a slow bleed for a portfolio.  Germany is excluded after the first series as it dominates the worst case scenarios (in this case hyperinflation).

Chart 1 – Cash Real Returns, 1900-2011

Best Case:  -2.30% per year

Middle:  -4.10%

Worst Case:  -100%




 Next up is real returns for short term government bills.


Chart 2 –Short Term Government Bills Real Returns, 1900-2011

Best Case:  2.25% per year

Middle:  0.71%

Worst Case:  -3.63%

(Real Worst Case, Germany -100%)




Followed by the real returns for longer dated bonds:


Chart 3 –Long Term Government Bonds Real Returns, 1900-2011

Best Case:  3.04% per year

Middle:  1.40%

Worst Case:  -1.91%

(Real Worst Case, Germany -100%)




And finally, the real returns for equities.


Chart 4 –Stocks Real Returns, 1900-2011

Best Case: 7.43% per year

Middle:  4.60%

Worst Case:  2.00%

 (Real Worst Case, China, Russia -100%)





And the same chart presented non-log…

 Individuals invested in U.S. stocks in the late 1920s and early 1930s, German asset classes in the 1910s and 1940s, Russian stocks in 1927, Chinese stocks in 1949, U.S. real estate in the mid-1950s, Japanese stocks in the 1980s, emerging markets and commodities in the late 1990s, and nearly everything in 2008, would reason that holding these assets was a decidedly unwise course of action.  Most individuals do not have a sufficiently long time frame to recover from large drawdowns from risky asset classes.

However, also since the recent update of this paper in 2009, we have seen a strong recovery in many of the world markets.  While some markets are still down considerably from their peak values, here in the US stocks and bonds are trading near or at all-time highs including dividends. 

 Most importantly for any investor is that they have a plan and process for investing in any environment, regardless of how improbable or unfathomable that may be.  Are you prepared for all of the possible outcomes, such as declines of 50-100% in your asset class or portfolio?  Are you prepared for currency devaluations, but also massive rallies in stocks or bonds?  Can you fathom a world with interest rates at 0.1% as well as at 10%?

Travel & Macro Pessimism, Micro Optimism (Part III)

I will be in Las Vegas this Friday and Alaska later this month if any readers are local and want to meetup!

This is the third post I have done that focus on cool new ideas and startups I have never heard of.  It just goes to show the creativity and brilliance of all the entrepreneurs out there.  Part I here and Part II here.

Most descriptions are from Entrepreneur mag article 100 brilliant companies.

Yessay – website aimed at helping students write better college application essays.

PaperKarma- Scan unwanted paper mail with the app, which will contact the sender and unsubscribe you from the mailing list.

 Freight Farms upcycles shipping containers into stackable modular mini-farms, reducing the footprint required for growing crops and allowing for locally grown produce in urban areas.

The Full Monty by Front Yard Coop: A solar-powered, self-propelled coop that moves around the yard to provide birds with fresh foraging, while a fence keeps them safe from predators.

Wide Open Spaces- A deal site for hunting and fishing gear, also curates under-the-radar brands.

AgLocal scored $1 million in funding last year from Andreessen Horowitz for an online platform that allows local and family farms to sell responsibly raised meat directly to consumers.

NatureBox: A monthly subscription snack-food box full of minimally processed and nutritionist-approved goodies.

Speek is changing the free conference-call game: The platform uses links instead of phone numbers and PINs, while browser-based controls make it easy to manage file sharing and see who has joined and who is talking.

and my favorite:

Rent-a-Goat.com – Online directory lists herders around the world who rent out their goats for clearing away unwanted brush and weeds, as an eco-friendly alternative to machinery or chemicals.



A Tale of Two Bubbles

Lots of gyrations in Japan lately.  Below is a chart that lines up the US and Japanese 10 year PE ratios so their bubbles are synched.  This goes to show just how ginormous the 1989 bubble was in Japan – more than twice the biggest stock bubble in US history (2000).  This is also the main reason it took Japan 21 years to get back to a normal valuation while it only took the US 9 years. (defined as CAPE of 17)



My Mantra is Diversity

“My mantra is diversity. I clone my mentors. I copy everything they do, and then I innovate on top of it.” – Henry Markram

Great article in this month’s Wired Magazine.

Searching for Yield

Great chat with Priest and Mack…while we depart on a few methods of screening, the general approach is very similar:


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