Super Macro – A Fundamental Timing Model

Originally Published April 10, 2012 in Advisor Perspectives

Buy-and-hold advocates cite two reasons why tactical investing should fail. It violates the efficient market hypothesis (EMH), they say, and it is nothing more than market-timing in disguise.

But they are wrong. Rather than endure losses in bear markets – as passive investors must – I have shown that a simple trend-following model dramatically improves results, most recently in an Advisor Perspectives article last month.  Now it’s time to extend my approach by showing how this methodology can be applied to fundamental indicators to further improve performance.

The EMH does not automatically endorse buy-and-hold, nor does it compel investors to endure losses in bear markets. Financial analysts forecast earnings and economists make recession calls routinely, yet academics ridicule market timers as fortunetellers, and market timers resort to labeling themselves as tactical investors to avoid the stigma. Why?

Perhaps what sparks resentment toward market timers is not their predictions, but how they make their predictions. Reading tea leaves is acceptable as long as the tea has a "fundamental analysis" label, but market timing is treated as voodoo because it offends the academic elite, whose devotion to the notion of random walk is almost religious.

I am not a market timer, because I can't foretell the future. But neither do I buy the random-walk theory, because my Holy Grail verifies the existence of trends. Timing is everything. When your religion commands you to hold stocks even when the market is behaving self-destructively, it's time to find a new faith.

Timing models that follow price trends are technical timing models. "The Holy Grail" is an example of a technical timing model. Timing models that monitor the investment climate are fundamental timing models. My Super Macro model is a prime example of a fundamental timing model that works.  Before presenting my Super Macro, I will first disclose the details of my earning-growth (EG) model. As one of the 18 components of Super Macro, the EG model illustrates my methodology in model design.

But first let’s look at the engineering science that makes these models possible.

Macroeconomics, an engineering perspective


Engineers assess all systems by their input, output, feedbacks, and controls. From an engineering perspective, the economy is like an engine. It has input (the labor market andhousing) and output (earnings andproduction). The engine analogy and the economic terms in the parenthetical are presented in Table 1. At equilibrium, the engine runs at a steady state, with balanced input and output. When aggregate demand exceeds aggregate supply, the engine speeds up to rebalance. This leads to economic expansions that drive cyclical bull markets. When output outpaces input, the engine slows down. This causes the economy to contract, leading to cyclical bear markets.

The economic engine has multiple feedback loops linking its output to input. Feedback loops can amplify small input changes to produce massive output differentials. Financial leverage is a positive feedback to the economy like a turbocharger is to a car engine. Strong economic growth entices leverage expansions (credit demands), which in turn accelerates economic growth. This self-feeding frenzy can shift the engine into overdrive.

Deleveraging, on the other hand is a negative feedback loop. It creates fear and panic that are manifest in a huge surge in risk premium (credit spreads). The lack of confidence among investors, consumers and businesses could choke an already sluggish economy into a complete stall.

In a free-market system, price is a natural negative feedback mechanism that brings input and output into equilibrium. When demand outpaces supply, price will rise (inflation) to curtail demand. When supply exceeds demand, price will fall (deflation).

The speed of an engine is controlled by the accelerator and the brakes. The central bank, attempting to fight inflation while maximizing employment, uses its monetary levers (interest rates) to control the supply of money and credit. Because of the complex feedback loops within the economic engine, the Fed often overshoots its targets. The unavoidable outcome has been business cycles, which are in turn the root causes of cyclical bull and bear markets.

A fundamental timing model

Models that monitor the economic engine are called fundamental timing models. One example is the EG model, which uses a four-year growth rate of S&P 500 earnings to generate buy and sell signals. (Four years was the average business cycle length in the last century.) The EG model meets my five criteria for a good working model.

  1. Simplicity: The EG model has only one input: the S&P500 earnings.
  2. Commonsense rationale: The EG model is based on a sound fundamental principle that earnings and earnings growth drive stock prices.
  3. Rule-based clarity: Its rules boil down to following trends when they are strong but being contrarian when growth rates are extremely negative.
  4. Sufficient sample size: There have been 29 business cycles since 1875.
  5. Relevant data: Earnings are relevant, as profits are the mother's milk of stocks.


The strategy is simple: buy the S&P 500 when the earnings growth index is below -48% or when it is rising. The first buy logic is a contrarian play and the second is a trend follower. Sell signals must meet two conditions: the earnings growth index must be falling, and it must be under 40%. The 40% threshold prevents one from selling the market prematurely when earnings growth remains strong.

Figure 1 shows the resulting bullish and bearish signals from 1875 to present.

Earnings growth is a key market driver, watched closely by both momentum players and value investors. The signals shown in Figure 1 demonstrate that the model avoided the majority of business-cycle-linked bear markets. The EG model, however, could not envision events that were not earnings-driven, such as the 1975 oil embargo and the 1987 program-trading crash.

Like the Holy Grail, my EG model outperforms buy-and-hold in both compound annual growth rate (CAGR) and risk (standard deviation and maximum drawdown). Since 1875, the CAGR of EG was 9.7% with an annualized standard deviation of 12.5% and a maximum drawdown of -42.6%. By comparison, the buy-and-hold strategy with dividend reinvestment delivered a CAGR of 9.0% with a standard deviation of 15.4% and a devastating maximum drawdown of -81.5%.

Since 2000, the EG model has issued only two sell signals. The first spanned January 30, 2001 to August 30, 2002 – during which time the dot-com crash obliterated one third of the S&P 500’s value. The second sell signal came on June 31, 2008, right before the subprime meltdown started, and it ended on March 31, 2009, three weeks after the market bottomed. Who says that market timing is futile? Both Holy Grail and EG worked not by predicting the future, but by steering investors away when the market trend and/or the fundamentals were hostile to investing.

Earnings growth is a yardstick to measure the health of 500 US corporations. Stock price, however, discounts information beyond such microeconomic data. In order to gauge the well-being of the economy more broadly, I need a macroeconomic climate monitor.

But the economy is extremely complex. Meteorologists monitor the weather by measuring the temperature, pressure, and humidity. How do we monitor the economy?

My Super Macro model

Before investing, we should first find out how the economic engine is running. If one wants to know the operating conditions of an engine, he reads gauges installed to track the engine's inputs, outputs, control valves, and feedback loops.

Table 1 lists the 18 gauges I watch to calibrate the economic engine, which I then integrate into a monitoring system I call "Super Macro." The EG model is one of the sub-components of Super Macro. In this paper, I have fully disclosed the design of the EG model. The details of the rest of remaining models are proprietary, but I can assure you that they satisfied the five design criteria for a robust model.

Super Macro performance: January 1920 to March 2012

Figure 2 shows all Super Macro signals since 1920. The blue line is the Super Macro Index (SMI), which is the sum of all signals from the 18 gauges listed in Table 1. There are two orange "Signal Lines." Super Macro turns bullish when the blue line crosses above either one of the two signal lines and remains bullish until the blue line crosses below that signal line. Super Macro turns bearish when the blue line crosses below either signal line and remains bearish until the blue line crosses above that signal line. The color-coded S&P 500 curve depicts the timing of the bullish and bearish signals.


The Super Macro index has demonstrated its leading characteristics throughout history. While my EG model didn't detect the oil embargo recession from 1974 to 1975, the SMI began its decline in 1973 and crossed below the 50% signal line in November 1973, just before the market plunged by 40%. From 2005 to 2007, during a sustained market advance, the SMI was in a downward trend, warning against excessive credit and economic expansions. On September 30, 2008, at the abyss of the subprime meltdown, the SMI bottomed; it then surged above the -20% Signal Line on March 31 2009, three weeks after the current bull market began.

Like the Holy Grail and EG models, Super Macro outperformed buy-and-hold in both CAGR and risk. From 1920 to March 2012, the CAGR of Super Macro was 10.1%, with an annualized standard deviation of 14.1% and a maximum drawdown of -33.2%. By comparison, the buy-and-hold strategy with dividend reinvestment delivered a CAGR of 9.9% with a standard deviation of 17.2% and a maximum drawdown of -81.5%.

Super Macro, Holy Grail and the buy-and-hold strategy

Let's compare Super Macro and Holy Grail to the S&P 500 total return from 1966 to March 2012, the period that is the most relevant to the current generations of investors. It covers two secular bear markets (from 1966 to 1981 and from 2000 to present) and one secular bull cycle (from 1982 to 1999). Secular markets, like cyclical markets, can be objectively defined. They will be the topics of a future article.

Figure 3 shows cumulative values for a $1,000 initial investment made in January 1966 in each of the three strategies. The Holy Grail outperformed the S&P 500 in the two secular bear cycles, but it underperformed during the 18-year secular bull market. As noted before, the buy-and-hold approach did not make sense in bear markets, but it worked in bull cycles. The cumulative value of Super Macro depicted by the blue curve always beat the other two throughout the entire 46-year period.


The CAGR of the Super Macro model from 1966 to March 2012 was a spectacular 11.4%, with an annualized standard deviation of 12.5% and a maximum drawdown of -33.2%. The Holy Grail model in the same period had a CAGR of 9.5%, with a lower standard deviation of 11.2% and a smaller maximum drawdown of -23.2%. By comparison, the S&P 500 total-return index delivered a CAGR of 9.3% but with a higher standard deviation of 15.4% and a massive maximum drawdown of -50.9%.

The current secular bear market cycle, which began in 2000, highlights the key differences between Super Macro, the Holy Grail, and the buy-and-hold approach. The S&P 500 total return delivered a meager 1.5% compound rate, with a standard deviation of 16.3% and a maximum drawdown of -50.9%. The trend-following Holy Grail returned a compound rate of 6.2%, with a low standard deviation of 9.5% and a small maximum drawdown of only -12.6%. Super Macro timed market entries and exits by macroeconomic climate gauges. It incurred intermediate levels of risk (a standard deviation of 12.4% and a maximum drawdown of -33.2%), but it delivered a remarkable CAGR of 8.5% from January 2000 to March 2012.

The main difference between a macro model and a technical model is that the timing of fundamentals is often early, while a trend follower always lags. In the next article, I will present an original concept that turns the out-of-sync nature of these two types of timing models to our advantage in investing.

Rule-based models achieve the two most essential objectives in money management: capital preservation in bad times and capital appreciation in good times. If you are skeptical about technical timing models like the Holy Grail, I hope my fundamentals-based Super Macro model will persuade you to take a second look at market timing as an alternative to the buy-and-hold doctrine. Timing models, both technical and fundamental, when designed properly, can achieve both core objectives, while the buy-and-hold approach ignores the first one. Over the past decade, we saw how fatal not paying attention to capital preservation can be.

Theodore Wong graduated from MIT with a BSEE and MSEE degree. He served as general manager in several Fortune-500 companies that produced infrared sensors for satellite and military applications. After selling the hi-tech company that he started with a private equity firm, he launched TTSW Advisory, a consulting firm offering clients investment research services. For over three decades, Ted has developed a true passion in the financial markets. He applies engineering statistical tools to achieve absolute investment returns by actively managing risk in both up and down markets. He can be reached at


A Look Back at the Performance of the Holy Grail

Originally Published March 20, 2012 in Advisor Perspectives

Back-tested results often look good on paper because stellar performance could have come from curve-fitting. If that were the case, then my "Holy Grail" model would not have withstood the test of time. But in the 32 months that have passed since its publication, investors who heeded its advice would have outperformed the market on a risk-adjusted basis.

I presented my Holy Grail model in a three-part series entitled Moving Average - Holy Grail or Fairy Tale (Part 1, Part 2, and Part 3) nearly three years ago. Let’s review the rationale behind my methodology, how the Holy Grail works and the out-of-sample results since its publication.

The Holy Grail is not market timing

Academically cited, empirical evidence has traditionally favored buy-and-hold over active or tactical investment strategies. That evidence shows that no one beats the market in the long run. But this conclusion is only correct if “the long run” means from the 1940s to the 1990s, the period over which most of this research was conducted.

The problem is that this conclusion was biased by the highly skewed data. This period encompassed two spectacular bull cycles (1940 to 1966 and 1982 to 1999). A more appropriate conclusion would have been, "buy-and-hold works in bull markets." The “Holy Grail” method captures as much of the bull markets as possible, while avoiding the worst of bear-market cycles. As a result, it outperforms buy-and-hold on a risk-adjusted-return basis in both bull and bear markets.

Market timing is generally misconstrued as synonymous with forecasting market turning points. By this definition, I am not a "market timer." I cannot anticipate market turning points in advance. My notion of market timing is similar to atmospheric monitoring. We do not need to forecast the weather in advance, but we must be observant of ever-changing weather patterns and be ready to act accordingly. To detect and track weather changes, meteorologists use temperature gauges, barometers, and computer models. To monitor investment climate, I use market-timing models. 

As a technical timing model, my Holy Grail model does not offer predictions. It follows price trends. In future articles, I will present other types of timing models that are driven by fundamental, macroeconomic, cyclical, and seasonal factors. These environmental gauges enable one to better assess the investment climate.

How the Holy Grail model works

My Holy Grail model is a six-month exponential moving-average crossover (EMAC) system. I use Professor Robert Shiller’s S&P 500 data series as the signal generator, because it has a long history dating back to 1871 and because it is accessible to the public. My Holy Grail model turns bullish when the Shiller S&P 500 crosses above its six- month EMA and bearish when it crosses below. When the model turns positive, one invests in the S&P 500 total-return index and collects dividends; when it turns negative, one sells the S&P 500 and puts the proceeds in cash. For a detailed description of my model, please refer to the three-part series to which I linked in the introduction.

It is not my intention to promote the Holy Grail as a trading tool; past performance cannot be assumed to prevail in the future. The Holy Grail is used as a counterexample, to disprove the claim that buy-and-hold is the only logical investment strategy. Holy Grail proves that one can beat the market by following trends, not by predicting market turning points.

Two refinements

After my 2009 articles, I received excellent feedback from many readers. Two of their suggestions are incorporated in this update. The Shiller Index I used previously was based on the monthly average of the daily close of the S&P 500 and, as such, it was not a tradable vehicle. My first refinement was to use the S&P 500 monthly close prices from Ultra Financial dating back to 1942 in performance calculations. Prior to 1942 (when Ultra’s data was not available), performance was calculated with the Shiller Index. The second refinement involved the sales proceeds. Instead of cash, the proceeds after all sell signals were placed in 90-day Treasury bills from 1934 to present and in the 10-year Treasury bonds prior to 1934.

Updated results from 1871 to 2012

The Holy Grail signals over the entire 140-year span are shown graphically in Figure 1. Green segments depict periods when the Shiller Index was above its six-month EMA, while pink signifies periods spent below that average. The blue line in Figure 2 shows the cumulative value of a portfolio following my Holy Grail model, and its value is based on three contributing factors: capital gains from the Holy Grail's buy signals, dividend reinvestment while in the markets, and proceeds from Treasury bills following the model's sell signals. 

A $1 investment in 1871 would have soared to $1.3 million by February 2012, a compound annual growth rate (CAGR) of 10.6% (this strategy is depicted by the blue curve). By comparison, $1 bought and held with dividend reinvestment (the orange curve) reached only $164,000 over the same period, a CAGR of 8.9%. That is a 162 bps gap in annual rate-of-return over the 140 years.

Besides higher returns, the Holy Grail model also offered significantly lower risk (volatility) measured by standard deviation. The annualized standard deviation of the Holy Grail system was 10.5%, a whopping 500 bps less volatile than buying and holding, which yielded a 15.3% standard deviation.

The Holy Grail diminished what would have been devastating losses from bear markets and allowed profits to run during bull markets, achieving the two most essential objectives in money management: capital preservation in bad times and capital appreciation in good times. 




Out-of-sample results from June 2009 to February 2012

How has Holy Grail fared in the current decade-long secular bear market, and – even more importantly – how has it done in the 32 months since its publication? Figure 3 updates the Holy Grail signals from June 2000 to February 2012, with a blue arrow to mark the month when Advisor Perspectives first published the Holy Grail.

All signals to the right of the marker represent out-of-sample data, including current results. If the promising historical back-test performance was merely a product of curve fitting, the results of the out-of-sample data should be noticeably worse.

There were two mini-bear markets during the 32-month out-of-sample period, one in 2010 and one in 2011. The Holy Grail sidestepped both of them. It did not sell at the peaks, but then a trend-following system is not expected to do that. The Holy Grail simply continued to offer downside risk protection and preserve principal through bad times. 




Figure 3 also demonstrates that the Holy Grail strategy accomplished its second objective, staying in the game in good times. Buy signals did not coincide with market bottoms, but once the rallies were confirmed buy signals followed. The latest buy signal came in December 2011, just before the January/February surge intensified.

Figure 4 compares the cumulative results of the Holy Grail over this period to those of the S&P 500 total-return index with dividend reinvestment. Each of the two strategies was assumed to begin with $1,000 in January 2000. Again, the blue arrow depicts the out-of- sample period. Holy Grail not only avoided both the dot-com crash and the sub-prime meltdown, it also softened the blows of the 2010 and the 2011 market corrections. Since June 2009, the CAGRs for the Holy Grail and buy-and-hold were 15.3% and 18.4%, respectively. The Holy Grail offered a much lower annualized standard deviation of 11.3% than the S&P 500’s 15.8%. Thirty-two months of live performance demonstrates that this simple trend-following system continued to add value. 




A disciplined approach to investing

How does one go about building timing models that monitor the investment climate? To begin answering that question, let’s begin by identifying the factors that underlie the Holy Grail model’s success. The Holy Grail is a technical model that follows price trends, but these features also apply to other tactical models that monitor seasonal, sentiment, macroeconomic and fundamental statistics. The common attributes shared by all working models are:

Simplicity: Moving-average crossover is the simplest system one can employ. In systems engineering design, the number of potential failure modes linked directly to the complexity of the system. In modern physics, simplicity and elegance are accepted as important contributors to proofs of concepts. Time and again, complex models may show great promise, but it is the simplest, most elegant systems that ultimately prove to have lasting relevance.

Commonsense rationale: Following market trends appeals to one’s common sense. All reliable models anchor on sound logic. Simplicity without rationale is naive. The Super Bowl Barometer may be simple and even look good statistically, but there is no logic behind it. Good models do not require the support of advanced theories or intricate algorithms, but they must have a cause-and-effect rationale.

Rule-based clarity: The buy/sell rules of Holy Grail are black-and-white. Being simple and logical would not be enough. The rationale must yield clear, actionable rules. If we cannot write buy/sell rules that a computer could compile, we do not have a rule-based model. Objective, quantitative models have no room for interpretation or ambiguity. The signals are either positive or negative, without qualifiers, provisos, or exceptions.

Sufficient sample size: The Holy Grail was tested over 140 years – a more-than- adequate sample size. Contrast this sample with that supporting the claim that "no one beats the market in a long run." If researchers extended their database beyond the study period of 1940 to 1999, they would have come to my conclusion: "buy-and-hold has only worked in bull markets."

Adequate data: Similarly, because the study period on which my evidence rests includes multiple bull and bear cycles, it represents an appropriate pool of underlying data on which to base conclusions. An example of inappropriate data would be the all-too-common practice of applying economic theory to the Great Recession using data from the post-WWII recoveries. The Great Recession was a balance-sheet recession that paralleled only the Great Depression. All other post- WWII recessions were business-cycle recessions. They were two different beasts.

Having simple, rule-based models that rely on common sense with ample and appropriate supporting data is only half the battle. The real challenge lies in execution. President Reagan's approach to the Soviet Union was "trust, but verify." If we anchor our trust on simple, logical and objective timing models, we can boldly pull the execution trigger.

Timing models are tools. Discipline in executing them makes money. Ned Davis, in his book Being Right or Making Money, confessed that his biggest flaw as a money manager was that he tended to let his personal ego affect his market view. What was his remedy? He entrusted his market view to mathematical timing models. The strict discipline they offered allowed him to take an objective approach, while avoiding the fool’s errand of trying to “beat” the market on guesswork alone.

With the right tools, Davis was able to see past the conventional academic wisdom that buy-and-hold is the only option. Are you? 


Moving Average: Holy Grail or Fairy Tale – Part 1

Originally Published June 16, 2009 in Advisor Perspectives

Buying and holding a diversified portfolio works well during good times, but falls short when supposedly uncorrelated asset classes drop in unison in bear markets. Are there alternative investment strategies that work for all seasons? The 10-month Moving Average Crossover (MAC) system is a solid candidate, as it sidestepped two recent bear markets in 2000 and 2008. But did it work in previous bear markets? Is 10 months the optimum length?

Let’s examine historical evidence to find out if MAC is the Holy Grail or just a fairy tale.


Electrical engineers routinely use moving average as a low-pass filter in analog and digital signal processing. It blocks transient perturbations from the input and only allows the core signals to pass, hence the term low-pass filter. Transient perturbation is a fancy name for short-lived popcorn noise that obscures the underlying signal.

Random spikes in an otherwise smooth signal are undesirable. We can reduce the amplitude of these noisy spikes by averaging the values of the data points neighboring on either side of the spike. Figure 1 shows how the filtered output closely tracks the original signal but the unwanted spikes are attenuated. The degree of noise suppression depends on the number of points used in the averaging. The longer the averaging period, the smoother the output. Because we can’t predict when random spikes will appear, we slide the filter block across the entire data stream from start to finish. The term moving average literally describes this function.


mac_part1_fig1-2   As an engineer, I have always been skeptical of the way stock market technicians plot MA curves. Traditionally, engineers align the midpoint of the MA curve with the center of the original data curve. This way, the MA curve is centered with respect to the original time series as shown by the red curve in Figure 2. Technicians, on the other hand, shift the end of the MA curve to match the most recent price point as shown by the blue curve in Figure 2. The lag between the original data curve and the shifted MA curve created by this peculiar plotting convention is the core of the MAC system. Without shifting the MA, there is no lag. Without the lag, there is no crossover.

The Moving Average Crossover system

MAC is the simplest and probably the oldest trading system. You buy when the price rises above its moving average, and you sell when it drops below. Although there are several forms of moving averages, I prefer the Exponential Moving Average (EMA) to the Simple Moving Average (SMA) because EMA gives slightly smaller lag.

My assumptions

To keep things simple, I made three assumptions:

  1. All proceeds after sales are kept in non-interest-bearing cash.
  2. No transaction fees.
  3. No taxes.

The first assumption penalizes MAC in favor of buy-and-hold. Parking proceeds in Treasury Bills would obscure the central focus of my study because short-term interest rates varied widely throughout history.

The second assumption has a small positive bias toward MAC. But fees on index funds and ETFs (which I assume, for the purpose of my analysis, have been around since the Civil War) are less than 10 basis points and will not significantly affect my results.

I exclude tax effects for several reasons. First, tax rates vary with income levels. Second, top marginal tax rates changed dramatically in the past 138 years, from below ten percent before 1910 to above ninety percent in the 1950s. Third, buy-and-holders are not exempt from tax; tax payments are merely deferred. When they eventually sell their holdings, their entire cumulative gains will be taxed. Ignoring taxes is a balanced compromise, and does not give the MAC system an unfair advantage.

I will revisit the fee and tax assumptions after presenting my results.

Let the contest begin: MAC versus buy-and-hold

To compare performance between MAC and buy-and-hold, I used Compound Annual Growth Rates (CAGRs) and 138 years of monthly data for the S&P500 total return index (with dividend reinvestment) from 1871 to 2009. I examined a wide range of MA lengths, from two to twenty-three months.

The buy-and-hold benchmark returned 8.6 percent over this period, and is represented by the red bar in Figure 2. The green bars represent the CAGRs for different moving average lengths. CAGRs below 11 months consistently beat buy-and-hold. Above that, they reach diminishing returns. The quasi bell-shaped curve suggests that the distribution is not random.




Figure 4 provides even more compelling support for the MAC system. I calculated risk- adjusted returns using the ratio of CAGR to its standard deviation, measured monthly from January 1871 to April 2009. Standard deviation of returns is a generally accepted measure of risk. By this definition, the MAC system beats buy-and-hold hands down across all MA lengths. The stability in risk adjusted return performance and their insensitivity to the MA length show that MAC is a robust system.

Standard deviation treats both up and down volatility as risk. Judging from the “missing out the best days” argument buy-and-holders embrace, I presume that

they don’t consider upside volatility as risk - only downside volatility. A more relevant measure of downside risk is equity drawdown. Drawdown is the percentage decline from the most recent equity peak. There are two ways to evaluate drawdown: average drawdown and maximum drawdown. Figures 5 and 6 respectively show the results of the two methods.




If you don’t view price surges as hazardous and consider only price plunges as risky, then you surely won’t care for the buy-and-hold approach. Buy-and-hold delivers a whopping negative 85 percent maximum drawdown, courtesy of the Super Crash from the 1929 peak to the 1932 trough. Even the average drawdown is a painful negative 26 percent. In comparison, the maximum drawdown for MAC is only negative 15 percent and the average drawdown is no worse than negative 4 percent.

I ignored both transaction costs and taxes, so now let’s check on these assumptions. Figure 7 shows the number of round-trip trades (from buy to sell) for the different MA lengths. The average is 0.38 trades per year, or one round-trip every 2.6 years. Even with the 2-month MA, MAC generates only 0.9 round-trip per year, or a holding period of 1.1 years. The low trading frequency of MAC not only keeps transaction costs low, but lowers the tax rates from ordinary income rates to long-term capital gain rates.



Have we found the Holy Grail?

Based on aggregate performance over the entire 138-year period, the MAC system beats buy-and-hold in both abosulte performance and risk-adjusted return. Have we indeed found the Holy Grail that works for all seasons? To find out, stay tuned for Part 2, in which I examine MAC and buy-and-hold on a monthly basis and by decade to see how they compare in all bull and bear markets since 1871.


UPDATE: Read the first set of original Advisor Perspectives replies to this article.

UPDATE: Read the second set of original Advisor Perspectives replies to this article.




What the “Missing Out” Argument Misses

Originally Published May 26, 2009 in Advisor Perspectives

Market timing is discredited by passive investment advisors as a voodoo ritual. Buy-and-hold proponents argue most compellingly by citing the “missing out” scenario - they show a dramatic drop in return, to Treasury Bill levels, if investors are out of the markets for only a few good days. Missing these market surges is considered a risk of lost opportunity.

However, they conveniently ignore the risk of being hit by devastating market crashes and the associated emotional stress of staying in the market at all times. I quantify this anxiety level by calculating the historical stock market drawdown for the past 137 years, since Ulysses Grant was President. You decide if staying the course justifies the pain and suffering. If you could avoid the nastiest crashes at the expense of missing a few spectacular rallies, how would your return fare against that of buying-and-holding?

Most of the “missing out” calculations show missing only the best days. Those analyses cover only a decade or two. I examined monthly data as far back as 1871 and daily data from1942 to present. I used the S&P500 total return index with dividend reinvestment. I considered three scenarios, namely, excluding the best surges, removing the worst plunges, and eliminating both the best and the worst extremes. To compare those three scenarios to the buy-and-hold benchmark, I calculated their CAGR (Compound Annual Growth Rate), sometimes referred to as the geometric average annual return.

Figure 1 shows the CAGRs for the different “missing out” scenarios based on monthly data. Excluding the best twenty-four months would reduce the return from 8.6 percent (the buy-and-hold benchmark) to 6.4 percent. What most passive managers don’t report is that by avoiding the worst twenty-four months, you could boost return to 11.5 percent.

Figure 2 presents CAGRs based on daily data. Excluding the best fifty days lowers the return from 10.0 percent (the buy-and-hold benchmark) to 6.1 percent; but eliminating the worst fifty days increases performance to a remarkable 15.2 percent.




The returns of missing both the best and the worst months are better than the returns from the buy-and-hold strategy as shown in Figure 3. Missing both extremes beats buy- and-hold across the board as shown in Figure 4. Who would mind getting similar returns to buy-and-hold without the volatile extremes? Isn’t volatility considered risk?

The seemingly compelling “missing out” argument cited by buy-and-hold advocates falls apart under cross-examination.
Passive investment advisors commonly advise their clients that the longer investments are held, the greater the chance for attaining positive returns. The upward bias of the stock market favors buy-and-holders. To determine the breakeven holding period, I calculated the equity drawdown of the S&P500 index. Drawdown is the percentage decline from the most recent equity peak. It represents the emotional pain investors have to endure while holding stocks of lost values.
The black curve in Figure 5 illustrates how often, how long, and how severe the S&P500 index suffers from drawdown, measured over the last 137 years. Let us examine the plot closely. First, drawdowns occur more often than one might think. Equity is underwater ninety-two percent of the time during this period. The roaring1990s is the exception rather than the rule. Drawdown consumes over fifty percent of this most bullish decade. Second, many drawdowns last a long time. The 1929 crash took twenty- six years before it finally broke even in 1955.
Third, since equity investments are considered an inflation hedge, we must consider inflation when discussing drawdown. The purple curve shown in Figure 5 is the drawdown adjusted for inflation, as measured by the Consumer Price Index. Although the nominal S&P500 index made new highs briefly in 2007, the real S&P500 was fifteen percent below its 2000 peak at the time. Inflation adjusted drawdowns are steeper and longer than nominal drawdowns.
Drawdowns with devastating magnitudes are quite common. The market suffers losses in excess of forty percent more than a third of the time. Only a man of steel can withstand the frequent, prolonged, and torturous emotional trauma inflicted by staying fully invested at all times.

“No one purchases just one index. We diversify...” argue buy-and-hold enthusiasts. By constructing a portfolio of uncorrelated stocks, you can dampen the impact of the specific risk of each stock. But correlations among different asset classes change with time, depending on the market environment. Two previously uncorrelated stocks can suddenly move in unison and render diversification ineffective.
More importantly, diversification cannot dodge the systematic risk of the entire market. During bear markets, most stocks decline together. When systematic risk is exacerbated by the systemic risk associated with the catastrophic collapse of global financial institutions, not only do the equity markets take a blood bath, but most other assets follow. In 2008, all asset classes plummeted, including US equities (all styles, sizes, and sectors), emerging markets, bonds, real estates, commodities, and currencies. The only uncorrelated asset during a systemic crisis is cash. The prudent way to reduce risk is to rebalance your portfolio with cash equivalents. Isn’t that called market timing?
Buy-and-hold proponents may cling to the belief that market timing is futile, since no one knows the future. Who said that market timers must foretell the future? Active asset allocation practitioners like Brian Schreiner and Mebane Faber are trend followers. As mentioned in their recent articles, a ten-month moving average system would have avoided both the 2000 crash and the 2008 meltdown. Is the moving average system a sound investment methodology or just a myth? I will explore this topic in a future article.

Ask yourself this question, “If you could help your clients avoid most the bear markets, would they mind missing a few mighty rallies?” Human beings are more sensitive to the pain of losing than to the joy of gaining. That’s why most passive financial advisors don’t buy the “missing out” argument, especially during bear markets. You may not be ready to sign up for lifetime membership to The Market Timers Association, but if you are losing faith in buy-and-hold or are losing clients, you are not alone among mainstream passive managers. Data –
All data are total return series.
S&P500 Monthly Series – Provided by Robert Shiller of Yale University.
S&P500 Daily Series – Provided by Ultra Financial Systems, LLC.


UPDATE: Read the original replies to this article