figure-2a-secular-market-cycles-since-1900

Secular Market Cycles – Fact or Illusion?

Originally Published October 3, 2016 in Advisor Perspectives

Conventional wisdom dictates that equity markets adhere to long-term secular cycles and that investors should adjust their allocations based on whether valuation metrics, such as the Shiller CAPE, are relatively high or low. But what if the notion of secular market cycles is misguided because, for example, the sample size of past cycles is insufficient to attach any statistical significance?

I have been a student of the stock market for over 40 years. I rarely heard the term "secular market cycles" before the 1970s. In 1991, Angus Maddison used the term "long waves" to describe economic activities in 16 advanced capitalist countries since 1820. The term "secular cycles" has gained popularity since 2000 when Robert Shiller published the first edition of Irrational Exuberance. Figure 1 is taken from page 8 of his book, which shows the now famous Shiller CAPE (cyclically adjusted price-earnings ratio). Shiller's chart featured four major tops from 1881 to 2000. The last peak was spot-on in nailing the dot-com bubble.

Many believed that Shiller had deciphered the incoherent S&P 500 chart into a comprehensible rhythmic waveform with CAPE.

figure-1-reprint-from-robert-shiller-irrational-exuberance

Since then, the notion of "secular market cycles" has been increasingly accepted as an undisputable fact in both academic research and investment circles. Experts are busy giving meaning to such cyclical patterns. They rationalize causal connections between secular market cycles and socioeconomic shifts and attribute those cycles to structural factors such as technological advances, demographic waves, inflation trends, political reforms or wars.

The problem arises when analysts advise clients to deploy different investment strategies depending on whether the current phase is a secular bull or bear market. In order to know which strategy is appropriate, investors must first identify where they are in a secular market cycle. Unfortunately, the same experts who can explain past cycles are in total disarray regarding the current cycle. Since 2010, analysts have been debating if the secular bear market that started in 2000 is still in place or if a new secular bull market has already begun. During their six-year debate, the S&P 500 has melted up over 200%.

A deep dive into the secular market debate

Are we currently in a secular bull or secular bear market? You can find experts with a wide dispersion of opinions –– the bearish camp, the bullish camp and those on the agnostic fence.

Leading the bearish camp are many renowned analysts who believe that the secular bear market started in 2000 continues today. Members include Ed Easterling, Michael Alexander, John Hussman,Jeremy Grantham, John Mauldin, Russell Napier, Joseph Calhoun, Van Tharp and Martin Pring (who might have turned bullish recently). Many of them justify their bearish stances with only one or two secular cycles of data supported by anecdotal evidence. Easterling and Alexander extended the database to over a century but could only increase the number of cycles to four or eight.

Such sample sizes are too small for any meaningful statistical analysis.

In the bullish camp, there is a contingent of prominent experts who believe that a new secular bull market began sometime after 2009. Doug Short, Jill Mislinski, Guggenheim Partners and others presented calendar tables that depicted the periods of their secular cycles in the last century. Others include Chris Puplava, Liz-Ann Sounders, Craig Johnson, Jeffrey Saut, Barry Ritholtz, Ralph Acamporaand Tim Hayes. Institutional members include Fidelity, INVESCO and Bank of America Merrill Lynch. Most of the analysts in this camp turned bullish in the period from 2012 to 2014, after the March 2009 price had been firmly established as the bottom of the preceding secular bear market.

Members sitting on the agnostic fence are harder to find. It takes honesty, humility and, above all, guts to admit publically that you don't have the answer. Doug Ramsey turned from bearish to neutral in 2014.Alex Planes hedged his mildly bearish stance by acknowledging that no one could be certain about the exact cycle phase except in hindsight.

Easterling and Alexander are the only two researchers in all three camps who applied rule-based models on more than a century of data to define secular market cycles. The transparency of their methodologies allows peer reviews. Their work and findings are summarized below.

Ed Easterling's secular market cycle model

Ed Easterling of Crestmont Research is a recognized authority on the subject of secular market cycles and has written extensively on the subject. According to Easterling, secular bull markets start at the troughs of below-average price-to-earnings ratios (P/Es) or the Crestmont P/Es and secular bear markets start at the peaks of above-average P/Es. Based on these "rules," Easterling tabulated a secular cycle calendar from 1901 to 2015 with four secular bull and five secular bear markets. The performances of secular bull versus bear markets is tabulated in Table 1.

table-1-secular-cycles-per-easterling

In Figure 2A, the S&P 500 is in green to depict Easterling's secular bull markets and in red for his bear markets. Figure 2B is an overlay of the Shiller CAPE and is the same as Figure 1 above but is extended to 2015. According to Easterling, the current secular bear market that began in 2000 shows no sign of ending soon. His basic premise is that secular bull markets in the past didn't begin until either the Shiller CAPE or the Crestmont P/E bottomed at below-average levels.

figure-2a-secular-market-cycles-since-1900

The mean of the Shiller CAPE from 1881 to mid-2016 is 16.7. The CAPE dipped down to 13.3 in March 2009, but that was not "below-average" enough for Easterling. He noted that in all four previous bear market bottoms in the 1920s, 1930s, 1940s and the 1980s, the CAPE dropped to at least 10 and, most of the time, close to 5.

Michael Alexander's secular market cycle model

Michael Alexander wrote a ground-breaking book in 2000 entitled: Stock Cycles: Why Stocks Won't Beat Money Markets Over the Next Twenty Years. Alexander developed a database of over 200 years, much longer than other researchers. As a result, he was able to show more supporting evidence that linked his secular cycles to economic fundamentals. Alexander argued that there were two alternate types of secular cycles – monetary cycles followed by real cycles. In monetary cycles, falling inflation produced secular bull markets and rising inflation, secular bear markets. In real cycles, strong or consistent earnings growth fueled secular bull markets, and weak or inconsistent earnings drove secular bear markets. The secular bear market that began in 2000 and continues to the present day is a weak and inconsistent earnings phase of a real cycle.

Alexander developed a new metric called the P/R ratio (price-to-resource ratio) to detect secular market turning points. His metric is grounded on sound fundamentals and the derivation of P/R was detailed in the Appendix in his book. His P/R ratio resembles Easterling's P/E and he uses a similar rule narrative – secular bull markets start after P/R ratios have bottomed, and secular bear markets start after P/R ratios have peaked.

Table 2 summarizes Alexander's original findings, which ended in 2000. I updated his table through 2015, which is consistent with his bearish market stance posted in a recent blog.

table-2-secular-cycles-per-alexander

The common thesis Easterling and Alexander share

Both Easterling and Alexander applied quantitative metrics to define secular cycles. From their statements, we can find a common thesis in their bearish arguments.

In April 2013, Easterling affirmed that "the current secular bear will continue at least for another five to ten years until the CAPE reaches 10 or lower."

In July 2015, Easterling reaffirmed that "Crestmont Research identifies – without hesitation or doubt – the current cycle as the continuation of a secular bear market...we have a strong conviction that the prospect of a secular bull is far away...this secular bear, however, started at dramatically higher levels due to the late 1990s bubble... the reality is that the level of stock market valuation (i.e., P/E) is not low enough to provide the lift to returns that drives secular bull markets....the current P/E is at or above the typical starting level for a secular bear market."

In August 2013, Alexander wrote, "I sold my last position last month when the S&P 500 was in the low 1600's. The P/R graph shows that the market has reached roughly the same position relative to past secular bear markets as it had in 2007...The bet I am making is that there will be another downturn as there was in the past and this downturn will send the S&P 500 down to 1250."

In June 2015, Alexander published a blog entitled "10,000 point decline in the Dow in the cards over the next three years." Based on the declines from P/R peaks to P/R troughs in previous secular bear markets, he projected a secular bear bottom for the Dow Jones Industrial Average to be around 8000 and the S&P 500 around 900 by 2018.

The self-assurance expressed in the statements by Easterling and Alexander is admirable. But their doomsday forecasts are misplaced. When we become too personally or professionally invested in a supposition, we fall into an overconfidence trap.

Philip Tetlock in his book Superforecasting: The Art and Science of Prediction identifies key traits that separate good forecasters from bad. Hedgehogs are lousy forecasters because they are overconfident on their immutable grand theories and stubbornly cling to their confirmation biases despite contradictory evidence. Foxes, on the other hand are much better forecasters primarily because they are skeptical about grand theories, diffident in their beliefs and ready to adjust their convictions based on actual events. Foxes are true Bayesians.

The key to successful forecasts is to keep an open mind. In his book Sapiens: A Brief History of Humankind, Yuval Noah Harari argues that a new mindset in the 16th century based on the Latin word ignoramus – the willingness to admit ignorance -- was the catalyst that set in motion the Scientific Revolution that continues today. As Mark Twain said, “It ain’t what you don’t know that gets you into trouble. It’s what you know for sure that just ain’t so.”

A common thesis behind the bearish stances of both Easterling and Alexander is that the current levels of their metrics – Easterling's P/E and Alexander's P/R – are still too high relative to the starts of all previous secular bull markets in the past century. This logic compels them to reject any possibility of a new secular bull market. I will challenge their logic a bit later. But I would like to clarify two common misconceptions first.

Misconceptions about the term "secular cycles"

Cycle advocates claim that the existence of secular cycles is self-evident as proven by the large performance gap between secular bull and bear markets shown in Tables 1 and 2. Large differences in the returns exist, but they don't necessarily prove the existence of secular cycles. Secular bull markets are defined as the periods from troughs (either in price, P/E or P/R ratio) to peaks, and secular bear markets, from peaks to troughs. By such definitions, returns in bull markets must be higher than those in bear markets. The self-evidence argument is a circular logic and can be illustrated with a simple analogy. The temperatures from June to August are relatively high not because of the summer season. Rather, the summer season is defined from June to August because the temperatures in those months are relatively high. Claiming that secular bull markets create wealth and secular bear markets destroy wealth is as trivial as saying that June, July and August are hot because of summer and the winter months are cold because of winter. Sequential high and low returns do not prove the existence of secular cycles because those patterns are used to define secular cycles in the first place.

The term "cycles" in engineering and sciences refers to events with regular periodicity or at a uniform frequency. The term "cycle" used by stock market researchers refers to contiguous pairs of up and down markets. Investment analysts claimed that the stock market exhibits cycles at an interval of 17 to 18 years. One analyst even calculated an average cycle as precisely 17.6 years.

The notion of "average" is only meaningful when the sample distribution has a central tendency, i.e., not flat, multi-modal or skewed. When the spreads on the "half cycle" are so widespread (from 3 to 25 years shown in columns 3 and 7 in Tables 1 and 2, respectively) and the sample sizes are so small (4 and 8 "full cycles" shown in Tables 1 and 2), the term "average" may not even be mathematically definable. The stock market does exhibit pseudo sine-wave oscillatory patterns because investors’ sentiment fluctuates between greed and fear emotional extremes. Such extremes are captured by my TR-Osc and several other models to be presented in my future articles. But there's no evidence of any periodicity. The term "cycles" is highly misleading and grossly misused and claims such as 17.6 years cycle length are absurd.

Why are secular cycles so illusive?

Let's return to the question – why is there no consensus among analysts on the current secular cycle phase? Is that because, when standing in the middle of a cycle, one cannot see the future direction of the market? It's understandable that if price turning points are used to define cycles, a cycle in progress cannot be identified until a higher high or a lower low has been clearly established.

But the lack of consensus is not limited to the cycle currently in progress. Experts couldn't even agree with the benefit of hindsight on past secular cycles. For example, none of the secular chronology published by Easterling, Alexander, Short, Guggenheim, Ramsey, Hussman, Maddison and Fidelitylooks exactly alike. For those analysts who used anecdotal evidence, descriptive arguments and only a few decades of supporting data to define their cycles, different hindsights should not be a total surprise. But one would expect the two cycle calendars from Easterling and Alexander to be similar because both researchers apply similar quantitative metrics and objective rules on over a century of market data to determine their secular cycles. How different are their secular calendars?

Compare Easterling's secular calendar shown in Tables 1 to Alexander's in Table 2. From 1900 to 2015, Easterling counted five bear markets and four bull markets, while Alexander identified only four bear markets and three bull markets. That's a whopping 30% discrepancy. An average investor has only 30 to 40 years to build his or her retirement nest egg, missing or adding one full secular cycle with an "average cycle of 17.6 years" could mean a world of difference.

Flawed assumptions common to both valuations and secular cycle models

Secular cycle metrics used by Easterling and Alexander share many common attributes with the traditional valuations gauges such as the Shiller CAPE, the Tobin-Q, the Buffet market-cap-to-GNP ratio, price-to-earnings, price-to-dividend and price-to-book ratio. I previously argued that their uniformly high readings in the past 20 years indicated two common flaws. Many experts have begun to question whether the two-decade long elevated CAPE readings really reflect high market valuations or if they are signs of possible calibration malfunction. Many "fixes" have been proposed to adjust the high levels back down (see Philosophical Economics, Jeremy Siegel and James Montier). When a gauge needs fixing, it means that users have lost confidence in its accuracy. The same critiques I made to challenge the validity of many of the valuations models also apply to the bearish secular market thesis of Easterling and Alexander.

Easterling's P/E, Alexander's P/R and all of the valuations gauges cited above share two key operating assumptions. First, they rely on the absolute levels of the readings in their metrics to appraise future market outlook. Second, they assume that mean reversion will always bring the outliers back to the normal range. The first assumption – high absolute levels (relative to the historical means) translate to low future returns – will only hold when the time series has a stable mean (a single mean that is constant in time). The means of all those valuations gauges cited above have shifted upward significantly in the last two decades. With multiple means, the out-of-bound data wouldn't know which mean to revert to. The elevation anomaly observed in the CAPE also appeared in both the Crestmont P/E and the Alexander P/R, which led both researchers to hold their secular bear market stances for over a decade.

The second assumption is mean reversion, which is misunderstood to imply that any data that is temporarily out-of-bound will always self-correct and migrate towards the mean. They have mistakenmean regression for mean reversion. Mean regression is a law in probability that states that random outliers in a normal probability distribution have a tendency to move towards the mean driven by random statistical processes. Mean reversion, on the other hand, is the result of causation, not randomness. Mean reversion is a causal hypothesis (not a law) postulated to explain certain observed tendency towards the mean. Jeremy Siegel, Philosophical Economics, James Montier and others have proposed various causes to explain the elevation in the Shiller CAPE. If causations are involved, the elevations in the CAPE and other metrics are not random, and therefore mean regression has no jurisdiction. Past mean reversion episodes in the Crestmont P/E, the Alexander P/R and the Shiller CAPE are no guarantees for their future reappearances. Since there is no mathematical law to mandate mean reversion, these metrics could stay elevated or suppressed indefinitely. The means could also step up or down to different plateaus if a new cause merges and shifts the baselines of their previous means.

Concluding remarks

The Shiller CAPE, the Crestmont P/E and the Alexander P/R are all good metrics built on solid economic fundamentals. The problem arises when these metrics are wrongly applied to gauge market valuations or to define long-term market cycles. Over the years, these widely held but misconceived models have become the sacred cows in the theology of investments. Any challenge to the cardinal truth would be denounced by the high priests as a heresy. Observations that cannot be explained by the traditional doctrines are conveniently casted as one-off anomalies. The fact is that secular cycles and the other related valuations models are not infallible axioms based on first principles but are merely hypotheses yet to be validated. Perhaps it would take a heretic from outside the investment circles with no career risk to point out the obvious flaws in this “cardinal truth.” I argue that the elevated readings in various secular cycle and valuations metrics since 1995 are not anomalous aberrations but are empirical evidence against the orthodoxy.

In fact, the dispersion in opinions among all secular cycle advocates could be viewed as a nullification of the secular cycle hypothesis. Analysts used price data from 1800 to 2000 for the "in-sample training" of their models. These models are "trained" to interpret the past. It is therefore no surprise that they can depict past cycles. Market behaviors from 2009 to present, however, could be looked at as the "out-of-sample" test results of these models. The confusion among analysts on their post-2009 market stances could be considered as a form of inconsistency between the out-of-sample test outcome and their in-sample data mining. When the out-of-sample reality stirs up a controversy that lasts for six years, it raises the presumption of doubt whether the secular cycle notion is a good approximation of realty.

There are two mathematical explanations for why these models give contradicting out-of-sample market stances even though they were trained with the same in-sample data. Any model that is constructed with fewer than a dozen input samples is deemed to be unreliable. First, the smaller the sample size, the more susceptible the model is to curve-fitting. Second, small in-sample sizes mathematically guarantee out-of-sample predictions to have low confidence intervals, high margins of error or both. The 2008 sub-prime meltdown was a horrific example of insufficient and irrelevant in-sample data. All credit rating agencies used U.S. housing market data from the 1970s to the 1990s to model the default risk of mortgage-backed securities. During this training period, mortgage default rates were very low and the U.S. real estate market was booming. If these credit agencies were to incorporate U.S. housing data from 1890 to 1950 (both bull and bear housing markets) or housing data from Japan since the 1970s (bear housing market) in their Gaussian copula credit risk models, we may not have had the sub-prime crash.

Daniel Kahneman in Thinking, Fast and Slow described two distinct human mental faculties – a spontaneous pattern recognition ability followed by a reflective aptitude to rationalize. Our ancestors survived in the savannah jungles mainly with their first mental faculty – extracting camouflaged signals from noise swiftly to outwit both stronger predators and faster prey. Having survived the jungles, humans had more time to indulge in contemplation. It's our propensity for ex-post rationalizations that gave birth to culture, religion, philosophy and sciences. Today, however, living in an internet maze packed with terabytes of data, our innate pattern perceptive intuition and our natural rationalization tendency are often fooled by randomness.

These two human traits manifest in the behaviors of secular cycle advocates. They first visualize a handful of apparent cyclical patterns like those in the Shiller CAPE. They then draw causal connections that link these observations to fundamental causes without bothering to check for statistical significance. According to Angus Maddison (see p. 16 in the reference), long-wave patterns are not caused by any periodic structural forces proposed by secular theorists, but rather, the results of accidental systematic shocks and subsequent attempts to stabilize the aftermaths by monetary and fiscal policies. Secular patterns are the reflection of these random wave-like disturbances on an otherwise continuously rising economic growth curve.

If those perceived cyclical patterns are purely accidental and caused by unpredictable random shocks, it's entirely plausible that the imaginative secular cycles could be mirages misconceived by the overzealous cycle advocates. Their faithful followers could be searching for something nonexistent. The Crestmont P/E, the Alexander P/R and the Shiller CAPE might stay elevated indefinitely and never revert to or undershoot their historical means. Long-lasting elevations could place the misguided forecasters in a special class of perpetual permabears.

Market watchers love the secular market controversy because a protracted debate keeps them relevant. Unfortunately, investors only have 30-plus years to accumulate wealth. Should we entrust our hard earned money to a hypothesis that might take an average secular period of 17.6 years to pan out? If by then the hypothesis is proven wrong, investors would have wasted half of their investing life-cycle.

It's a common belief that the stock market exhibits both secular and cyclical waves. If the concept of secular markets is dubious and the nature of the in-progress secular phase is always unclear, we should shift our attention to the shorter version called cyclical markets. Does the notion of cyclical markets share the same flawed premises as their secular cousin? What drives cyclical markets? Can they be defined, identified and modeled objectively? Modeling cyclical markets and the efficacy of such models will be the topics of my next articles.

Theodore Wong graduated from MIT with a BSEE and MSEE degree and earned an MBA degree from Temple University. 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 almost four decades, Ted has developed a true passion for studying the financial markets. He applies engineering design principles and statistical tools to achieve absolute investment returns by actively managing risk in both up and down markets. He can be reached at ted@ttswadvisory.com.

figure-3-super-macro-holy-grail-and-the-sp500-total-return

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

table-1-super-macro-model

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.

figure-1-the-eg-model-1875-to-2012

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.

figure-2-super-macro-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.

figure-3-super-macro-holy-grail-and-the-sp500-total-return

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 ted@ttswadvisory.com.

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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. 

 

holygrailperformance_fig1holygrailperformance_fig2

 

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. 

 

holygrailperformance_fig3

 

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. 

 

holygrailperformance_fig4

 

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? 

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Moving Average: Holy Grail or Fairy Tale – Part 3

Originally Published July 28, 2009 in Advisor Perspectives

“If there was ever a good time to consider a new investment approach,” an advertisement for a mutual fund company proclaimed in a recent issue of a financial planning magazine, “it’s when the old ones have proven so fallible.” For a second, I thought that they had finally given up on the tired buy-and-hold (B/H) approach. It turned out they were just promoting a new kind of index fund.

Buy-and-hold remains deeply entrenched in the financial planning community, despite many of the flaws my previous articles have illustrated. Although many financial advisors suffer dearly from their B/H practices, they are reluctant to change their approach. Who dares to challenge investment sages like Bogle, Siegel, and Malkiel who emphatically support this long-standing investment principle? Academic research studies overwhelmingly endorse B/H. How can they all be wrong?

Perhaps the investment scholars and researchers are right to advocate B/H, but for the wrong reason, as I will explain later. But first, let me digress to respond to some of the feedback my previous articles received.

Let’s be clear

I am gratified to learn that many readers are able to replicate my results. My research is only credible if it passes peer reviews. A couple of readers, however, had difficulty reproducing my exact numbers. I use Professor Robert Shiller’s S&P500 Index primarily because his data is accessible to the public, so that my calculations can be checked. But Professor Shiller creates his monthly index by averaging daily closing prices. If you use actual daily or monthly closing prices, you will get different results.

Several analysts also inquired about the actual implementation of the Moving Average Crossover (MAC) system. For the record, it is not my intention to promote the MAC system as a trading tool. There are many trading systems used by active managers with proven track records. I use MAC as a demonstration to challenge the popular notion that no one can beat the markets in the long run. In science, it only takes one counterexample to invalidate a principle, no matter how well-established it might be.

Establishing a way to implement active investment management systems into a business practice exceeds the scope of my articles. For those planners new to the field of rule-based trading systems, my advice is to work with a reputable active investment firm or to use experienced consultants. It’s not a do-it-yourself project.

Let’s revisit market history

Implementation considerations aside, let’s turn to a more detailed analysis of the MAC system, and see how it compared to B/H pre- and post- the Great Depression, as well as during each of the last 14 decades.

 

mac_part3_fig1

 

The effectiveness of the 6-month MAC system is illustrated graphically in Figure 1. I present the 138-year history into two plots of seventy years each for better visual clarity. All the buy and sell signals are superimposed on the S&P500 Index in two colors. The red segments (sell) depict periods when the Index was below its 6-month moving average; and the green (buy), above. The blue curve shows equity accumulation from three contributing factors: capital gains resulting from MAC transactions, reinvestment of dividends while in the markets, and capital preservation in cash while out of the markets. A $1 investment in 1871 would have soared to $332,000 in June 2009. By comparison, $1 invested under the B/H approach with dividends reinvested earned only $105,000 over the same period. Note that without reinvesting dividends a $1 investment in the S&P500 Index itself only returns $211 (from $4.44 in 1871 to $938 in June 2009).

A tale of two periods

 

mac_part3_fig2

 

As we have seen, the 1929 crash gives MAC an advantage over B/H as seen in Figure 1. Several readers wanted to know how MAC would have fared if we removed the one- time impact of the Great Depression. In Figure 2, I show two separate investments with $1 each at the beginning of the two seventy-year periods. From 1871 to 1940, the B/H strategy returned 100-fold and MAC beat it by a factor of five, primarily a result of side- stepping the Great Depression. From 1941 to June 2009, without the impact of the Great Depression, both systems gain 1,000-fold and tie at the end. However, B/H outperforms MAC for most of the seven decades. So you may say that B/H is indeed unbeatable, if bear markets like the Great Depression, the Oil Embargo of 1974, the 2000 Internet Bubble, and the 2008 Sub-prime Meltdown can all be ignored. B/H can be considered as a bull market Holy Grail.

Dissecting the decades 

Let’s examine market history in a different light. In Part 2, I compared MAC’s monthly and annual performance to that of B/H. MAC beat B/H on both counts. But by looking at monthly and even yearly perfromance you could miss the forest for the trees. Examining decadal performance, thought, one gains new insight from a longer-term perspective.

 

mac_part3_fig3

 

Figure 3 shows Compound Annual Growth Rate (CAGR) by decade. The 138 years cover fourteen decades. The upper graph compares B/H to MAC. The lower graph is more instructive, as it shows the net CAGRs (MAC minus B/H). Out of fourteen decades, B/H outperforms MAC in only six, and by small margins. Five of those six decades occurred after 1941. In those decades when MAC outperforms B/H, the margins are quite significant. Finally, for more than a century, the current decade is the only one that B/H has shown a loss, although the current decade is not over yet.

The Emperor has no clothes! 

I mentioned earlier that researchers are right to endorse B/H, but they do so for the wrong reason. They are right that B/H has an impeccable track record over six decades. But they are wrong to declare B/H as the best way to invest at all times. B/H underperfomed more than half of the time in 138 years. They are also wrong when they justify their argument with theories such as the Modern Portfolio Theory and the Efficient Market Hypothesis. The markets were quite efficient during all bear markets so why didn’t B/H work then? History and simple logic tell us why B/H didn’t work in many decades before the ’40s, why it has worked for so long after the ’40s, and why it has stopped working since 2000. You don’t need advanced economic theories to explain the obvious.

After World War II, the West, led by the United States, unleashed the power of the free market system. Capitalism fueled technological innovation, which in turn bolstered global economic expansion. As a result, the stock markets enjoyed the most powerful and the longest advance in human history. The unprecedented secular bull markets skyrocketed 1,000-fold and lasted six decades. All academic research sudies focused on this post-WWII era naturally concluded that no one can beat the markets. The efficient market theories had little to do with B/H’s success. The B/H strategy was the Holy Grail simply because of the secular bull markets.

Then came 2000. The markets tumbled and B/H faltered. Researchers who clung to six decades of flawless records with a seemingly sound theoretical underpinning were perplexed. Since bull markets always returned in the past, they waited, only to get hit again in 2008. They continue to hold, wait, and hope.

As an engineer surrounded by financial scholars and investment geniuses, I feel like the little boy watching the naked Emperor in the parade. I point out the obvious with no fear of embarrassing myself. President Clinton once said, “It’s the economy, stupid!” I holler, “It’s the bull markets, Professors!” The truth is that B/H works wonders during economic expansions, but it underperforms during economic slowdowns or contractions. If there were no bear markets, B/H would indeed be the Holy Grail!

Diversification in time

The Modern Portfolio Theory tells us not to put all your eggs in one basket. The B/H strategy calls for holding all your eggs in one continuous “basket” of time. That sounds like a risky proposition to me. Market timing is not witchcraft. It reduces risk through temporal diversification. There are times to hold, and there are times to fold.

Active investment management with market timing works not by forecasting the future, but by following major market trends. By way of example, let me illustrate how the 6- month MAC system described in Part 1 and Part 2 realizes temporal diversification. Figure 4 shows the difference between $1 investments in B/H and in MAC made in

January 2000. How have the two systems performed through the 2000 Internet Bubble and the 2008 Systemic Meltdown, to June 2009? I’ll let you be the judge. The MAC system doesn’t predict the markets, it follows the trends. It doesn’t sell at peaks or buy

 

mac_part3_fig4

 

at bottoms. But it’s effective in preserving wealth in bear markets and accumulating wealth in good times.

Now you know why the B/H strategy that worked so well in the past has proven so fallible since 2000. The question is whether you believe the secular bull of the past is likely to return after the current recession is over. If you think that the next decades will not match the good fortune of the post-WWII era, you should start looking for an alternative investment approach.

 

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

 

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Moving Average: Holy Grail or Fairy Tale – Part 2

Originally Published June 16, 2009 in Advisor Perspectives

Prominent Nobel laureates in economics often point to a large body of evidence that supports the Efficient Market Hypothesis (EMH), which states that no one can beat the markets over the long haul. Many renowned financial experts further declare that passive investing in a diversified index like the S&P500 is the only sensible way to manage money. I respect their opinions but I am unable to verify their claims. By examining the evidence, I show that the Moving Average Crossover (MAC) system offers a superior risk-return profile to a buy-and-hold strategy.

I tested the simplest form of active investing, the MAC system, against a buy-and-hold approach on the S&P500 total return index from January 1871 to April 2009. With no data mining or systems optimization, such that anyone analyzing the same S&P500 database would have made the same investment decisions, this basic trend-following system beats the markets.

“How dare you challenge the Canon of Finance with such heresy as ‘beating the markets?’” the experts are sure to respond.

I must have found the Holy Grail, or else the buy-and-hold logic is flawed!

Before I continue, let me recap my key findings in Part 1. I tested different moving average lengths from 2-months to 23-months. By comparing the results of the best of class (6-months) and the worst of class (23-months) to those of the buy-and-hold benchmark, I can make an objective assessment on the MAC system as a whole relative to the markets.

MAC performances that beat the buy-and-hold benchmarks are in green; those that don’t are in red. 

 

mac_part2_chart

 

CAGR is the Compound Annual Growth Rate. Terminal Equity Value is how much $1 invested in January 1871 would grow to at the end of April 2009. Risk-adjusted return is the average annualized monthly return divided by the standard deviation of annualized returns. Drawdown is the percentage decline in equity value from its recent peak.

Aggregate versus periodic performance

The table above compares aggregate performance over 138 years. But aggregate results are not the only information pertinent to investing. You want to know periodic performance as well. For example, how did the systems perform during bear markets? How often and how brutally did the markets turn against you when the systems told you to stay the course? What were the monthly, annual, and decadal performances?

Bear market risks

Let’s first find out which system protects us better from the wrath of bear markets. Three growth curves are shown in Figure 1. The red one is the buy-and-hold benchmark. The 6- and 23-month MACs are shown by the blue and the green curve, respectively. 

 

mac_part2_fig1

 

Each curve represents how an initial investment grows over time. A smooth and rising curve is preferred. All three investors invested $1 in the S&P500 total return index in January 1871. The buy-and-holder reinvested his dividends at all times. The two active investors reinvested dividends only when the S&P500 index was above its moving average but otherwise kept the proceeds in non-interest bearing cash.

Figure 1 not only shows which investment wins the race in wealth accumulation (6- month MAC), but also graphically illustrates how the three systems play out in historical bear markets. MACs won’t get you out at every market peak, but they would have preserved some – if not most – of your accumulated wealth. In contrast, passive advisors willingly handed over their clients’ hard-earned money to every hungry bear they encountered! Worse, by the time a passive investor realizes that a bear is eating his lunch, his strategy calls for him to do nothing to try to stanch his losses, lest he miss the market’s rebound. Don’t laugh! That’s the passive experts’ “Missing out” logic!

Market exposure risks

Full market exposure is risky – even during bull markets – because it increases the risk of drawdown. There is a material difference between actual loss and drawdown. Actual loss is painful but the healing process begins as soon as the investor realizes the loss. 

Drawdown, on the other hand, is like an open wound. It represents the pain of holding stocks when the markets turn against us. The pain continues to grow with every additional price decline. Exposure to uncertain and unfriendly markets is more harmful to investors’ mental health than actual loss is to their wallets.

Both the duration and the magnitude of drawdown for the two MAC systems are shown in Figure 2. The blue stripes are the 6-month MAC and the green are the 23-month. The average drawdowns for the two systems are 2 and 4 percent, respectively. Drawdowns of greater than ten percent were rare during the 138-year period. In comparison, the average drawdown of the buy-and-hold system was a painful 26 percent. 

 

mac_part2_fig2

 

Figure 2 shows that the MAC system would never expose investors to an unfriendly market for more than a few months at a time. On the contrary, buy-and-holders could be underwater for over ten or even twenty-five years before breakeven, as shown in Figure 5 in my “Missing out” article. The mental anguish of suffering in a hostile market environment for such a prolonged period of time is unimaginable.

Active investments offer much lower market exposure risks than the buy-and-hold approach, both in magnitude and in duration of drawdown. Which camp would you rather join? 

Annual performance tradeoffs

Markowitz’s Efficient Frontier is an instructive way to compare monthly performance because it shows risk-reward tradeoffs on a single diagram. Figure 3 shows annualized monthly returns (reward) versus standard deviations of annualized monthly returns (risk). To keep the graph legible, I show only the 6-month MAC (green squares) against the buy-and-hold (red squares) benchmark. 

 

mac_part2_fig3

 

The Efficient Frontier lies at the top-left portion of the graph where most green squares reside. This means that MAC’s annual returns are generally higher than those of buy- and-hold at the same level of risk. The undesirable portions of the graph (bottom and right) are mostly occupied by red squares. All except one of the extremely high volatility years are in red. If Markowitz favors investments at the Efficient Frontier, then he would surely prefer the MAC system to the buy-and-hold approach.

Doesn’t Modern Portfolio Theory call for absolute correlations between return and risk? Hence any investment offering high returns with low risks must be flawed. On the contrary, Modern Portfolio Theory does not postulate that high intrinsic risks are an inherent characteristic of high-return investments. Rather, it simply points out that rational investors would logically ask for additional risk premium to compensate for the extra risk they are taking. The performance of the MAC system is theoretically sound.

Based on the risk-and-return tradeoffs presented in Figure 3, no rational investor would subscribe to the buy-and-hold scheme as it offers no adequate risk premium to compensate for its enormous volatilities.

Monthly performance comparisons

The monthly performance comparisons between the MAC and the buy-and-hold method are best illustrated with a histogram. Again, I show only the 6-month MAC to keep the graph legible. The horizontal axis in Figure 4 shows different increments of monthly percentage change. The vertical axis tabulates the number of occurrences of each of these increments in 1,659 months. 

 

mac_part2_fig4

 

On the positive-return side of the distribution, green squares capture all the winning months of the markets, including the few unusually strong rallies of 10 to 30 percent. When the markets are bullish, the MAC system does not miss the best months.

On the negative-return side, there is a sizable gap between the two systems. The MAC system is able to elegantly sidestep the markets during most of the losing months. Proceeds from all these bad months are safely kept in cash as reflected by the single green square floating at the very top of the vertical axis. Many red triangles suffer worse than fifteen percent losses, while green squares rarely incur losses of more than five percent.

Figure 4 illustrates graphically how the 6-month MAC system beats the markets. There is no fairy tale if a system can consistently avoid the losers but stay with the winners 1,659 times over 138 years.

Holy Grail or fairy tale?

I am not trying to persuade anyone that the MAC system is the Holy Grail. Indeed, I discovered MAC’s limitations when evaluating its decadal performances, which I will discuss in Part 3. Stay tuned!

What I have tried to convey is that all claims should be treated as hypotheses until they are proven by objective evidence - even a claim as sacred as the eminent passive investment doctrine. Perhaps the generally accepted buy-and-hold investment principle is only a fairy tale! 

 

UPDATE: Read the original Advisor Perspectives replies to this article

 

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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.

Background

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.

 

mac_part1_fig3-4

 

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.

 

mac_part1_fig5-6

 

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.

 

mac_part1_fig7

 
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.

 

 

missingout_featuredimage

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.

 

missingout_fig1-2

 

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.
 
 
missingout_fig3-4
 
 
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.
 
 
missingout_fig5

 
 
“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