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.


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.


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.


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.


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


A Market Valuation Gauge That Works

Originally Published March 15, 2016 in Advisor Perspectives

In my previous article, I examined many popular metrics that all show that U.S. equities have been overvalued for over 20 years. The conventional explanation is that the overvaluation and its unusually long duration is a statistical outlier. But those aberrations were observed in only 15% of the data population (20 out of 134 years) and are unlikely to be statistical outliers. The root cause is not yet known. Until the anomaly is better understood, naively equating the lack of mean reversion with overvaluations will lead to misguided valuations and ill-advised investment strategies.

A decade ago, I began searching for a valuation indicator that is immune to possible mean-reversion malfunction. The challenge proved to be much more difficult than anticipated. I ultimately had to abandon my search and developed my own valuations gauge, the total return oscillator (TR-Osc) and present it here.

Oscillatory gauge

Mean reversion is the underpinning of all valuations metrics. The basic concept of valuations relies on the notion that value oscillates between an upper bound (overvalued) and an lower bound (undervalued) around a median (fair-valued). How do you calibrate a gauge that has an unbounded output or with a drifing median that confuses mean reversion? A functioning valuations gauge should resemble a pseudo sine-wave oscillator with quasi-periodicity.

Although the cyclically adjusted price-to-earnings ratio (CAPE) oscillated around a stable geometric mean of 14 from 1880 to 1994, its mean has risen to 26.2 since 1995 (Figure 1A) – a telltale sign of mean reversion malfunction. By contrast, my TR-Osc has been bounded by well-defined upper and lower demarcations for over a century. The mean of TR-Osc measured from 1875 to 1994 is almost identical to the value computed over the last 20 years (Figure 1B). After reaching either extreme, TR-Osc always reverts toward its long-term historical mean.

From 1880 to 1950, TR-Osc and CAPE were almost in sync. After 1955, the two indicators began to diverge. Although both the CAPE and TR-Osc detected the dot-com bubble in 2000 (red squares), only the TR-Osc warned us about the 1987 Black Monday crash (red circle). After the 2000 peak, CAPE stayed elevated and came down only once in mid-2009 to touch its historical mean at 14. The TR-Osc, however, dropped to its lower bound in January 2003 (green arrow) getting ready for the six-year bull market from 2003 to 2008. The TR-Osc did it again after the housing bubble when it dipped below the lower bound of 0% in February 2009, just in time to reenter the market at the start of a seven-year bull market from 2009 to present.

In 2008, the TR-Osc reached a minor summit (red triangle) while CAPE exhibited no peak at all. Both TR-Osc and CAPE indicate that the meltdown in global financial markets did not stem from an overvalued equity market. I will expand on this later when I discuss the real estate sector.


Common deficiencies in all contrarian indicators

There are two common deficiencies shared by all contrarian indicators including all traditional valuations models. First, their signals are often premature because the market can stay overvalued or undervalued for years. Greenspan's 1996 irrational exuberance speech alluded to an overvalued market but it was four years too early. From 1996 to the dot-com peak in 2000, the S&P500 surged 87% and the NASDAQ 288%.

The second deficiency of all contrarian indicators is that the market can reverse direction without hitting either extreme at all. The CAPE, for example, was not undervalued in 2002 or 2009. Value investors would have missed out on huge gains of 90% and 180% from the two spectacular bull markets in the 2000s.

The dual gauges of the TR-Osc: scalar and vector

Before I explain how the TR-Osc overcomes these two deficiencies, let me first describe the TR-Osc. The TR-Osc captures what investors in the aggregate earn by investing in the S&P 500. That is the sum of two components – the first from price changes and the second from dividend yields. Price return is the trailing five-year compound annual growth rate (CAGR). Dividend yield is the annual return from the dividends investors received. The look-back period doesn't necessarily have to be five years. All rolling periods from 2 to 20 years can do the job. In addition, both real (inflation-adjusted) and nominal TR-Osc's work equally well because inflation usually does not change much over a five-year period.

The TR-Osc overcomes the two deficiencies by having two orthogonal triggers, a scalar marker and a vector sensor. The oscillatory and mean-reverting attributes of the TR-Osc allow overvaluation and undervaluation markers to be clearly defined (Figure 2). When the TR-Osc was near the upper bound (the 20% overvalued marker), the S&P 500 often peaked. When the TR-Osc was near the lower bound (the 0% undervalued marker), the market soon bottomed. But in 2008, the TR-Osc only reached 12% and the market was not overvalued. Investors had no warning from the valuation marker to avert the impending subprime meltdown. Valuation markers (scalar) alone are not enough. The TR-Osc needs a second trigger, a motion sensor (vector) that tracks the up or down direction of valuations.


Let me illustrate how the scalar and vector triggers work in concert and how buy/sell signals are executed. When the TR-Osc is rising (an up-vector) or drops below the lower bound at 0% (an undervalued marker), a bullish market stance is issued. When the TR-Osc is falling (a down vector) but stays above 0% (not undervalued), or when it exceeds the upper bound at 20% (an overvalued marker), a bearish alarm is sounded. The asymmetry in the buy/sell rules stems from prospect theory, which contends that losses have more emotional impact to people than an equivalent amount of gains.

When a bullish signal is issued, buy the S&P 500 (e.g. SPY). When a bearish alarm is sounded, sell the S&P 500. After exiting the stock market, park the proceeds in 10-year Treasury bonds. The return while holding the S&P 500 is the total return with dividends reinvested. The return while holding U.S. Treasury bonds is the geometric sum of both bond yields and bond price percentage changes caused by interest rate changes.

The performance data presented in this article assume that all buy and sell signals issued at the end of the month were executed at the close in the following month. When the TR-Osc signals were executed closer to the issuance dates, both return and risk performances were slightly better.

TR-Osc performance stats

Figure 3 shows two hypothetical cumulative returns from 1880 to 2015 – the TR-Osc with the buy/sell rules stated above and the S&P 500 total return. Over 135 years, the TR-Osc has a 190 basis point CAGR edge over the buy-and-hold benchmark with less than half of the drawdown risk.

The TR-Osc traded infrequently – less than one round trip a year on average. The TR-Osc is an insurance policy that protects investors against catastrophic market losses while preserving their long-term capital gain tax benefits.


Let's take a closer look at the TR-Osc signals in two more recent time windows. Since 1950, there have been 10 recessions. Figure 4A shows that the TR-Osc kept investors out of the market in all 10 of them. Figure 4B shows that the latest TR-Osc bearish call was issued in September 2015. The TR-Osc sidestepped the recent stock market turmoil and has kept investors' money safe in Treasury bonds.


Table 1 shows performance stats for various sets of bull and bear market cycles. TR-Osc beats the S&P500 total return in CAGR, maximum drawdown, and volatility. The consistency in outperforming the S&P500 in returns and in risk over different sets of full bull/bear cycles demonstrates the robustness of TR-Osc.


TR-Osc has universal applicability

Like the CAPE, the TR-Osc’s efficacy is not limited to the S&P 500. It can also measure valuations in overseas markets (developed and emerging), hard assets and currencies. For example, Figure 5 shows three alternative spaces – raw materials (Figure 5A), oil and gas (Figure 5B) and real estate (Figure 5C) (data source: Professor Kenneth French). This universal applicability of the TR-Osc also enables intermarket synergies. Recall in Figure 2 that the stock market was not overvalued in 2008 according to both the CAPE and the S&P 500 TR-Osc. Note that the real estate TR-Osc correctly detected the housing bubble (red square in Figure 5C). When the systemic risk spread to the stock market, the S&P 500 TR-Osc vector sensed the danger and turned bearish.


Figures 6A to 6C shows that the TR-Osc improves both the return and drawdown in two distinctively different spaces – precious metals (data source: Professor Kenneth French), the Canadian dollar and the Australian dollar (data source: FRED). Prices in precious metals fluctuate widely at rapid speeds while foreign currencies crawl in narrow ranges at a snaillike pace. It's remarkable that the TR-Osc works equally well across drastically different investment classes. How does the TR-Osc help a diverse group of characters with different personalities perform better?


The analytics of TR-Osc

You may say that TR-Osc is just a five-year rolling total return. But what breathes new life into an otherwise ordinary formula is the analytics behind the TR-Osc. The adaptability of buy and sell rules is the reason behind the TR-Osc's universal applicability. As indicated previously, the TR-Osc has two triggers: valuation markers (scalar) and valuation directional sensor (vector). How did I pick the values for these triggers? The vector is obvious – up is bullish and down is bearish – but how do I select the valuation markers?

In Figures 2, 5 and 6, the middle blue line is the mean. The upper blue lines are the overvaluation markers and the lower blue lines, undervaluation markers. The upper blue lines are M standard deviations above the mean and the lower blue lines, N standard deviations below the mean. Each time series has a unique personality. For example, the means of most currencies are near 0% while the mean of the S&P 500 is near 9%. More volatile investments like precious metals, oil and gas would have larger standard deviations than the serene currency space. The values of M and N are selected to match the personality of each underlying investment. The general range for both M and N is between 1 and 2.

A common flaw in the design of engineering or investments systems is over-fitting. I have developedfive criteria to minimize this bad practice. The five criteria are simplicity, sound rationale, rule-based clarity, sufficient sample size, and economic cycle stability. The TR-Osc not only meets all of these criteria but offers one additional merit – universal applicability. It works not only on the S&P 500, but on overseas markets and across a diverse set of alternative investments.

Theoretical support for TR-Osc

Traditional valuation metrics rely on fundamentals, which often experience paradigm shifts across secular cycles. Fundamental factors can be influenced by generational changes – technological advances, demographic waves, socioeconomic evolutions, structural shifts, political reforms or wars. Therefore the means in many of the traditional valuation metrics can drift when the prevailing fundamentals change.

The TR-Osc downplays the importance of the external fundamental factors and focuses primarily on the internal instinct of the investors. Investors' value perception has two behavioral anchors. The first anchor drives investors toward the greed/fear emotional extremes. For example, when the S&P 500 delivers a five-year compound annual return in excess of the 20%, euphoria tends to reach a steady state and investors become increasingly risk adverse. When their returns get stuck at 0% five years in a row, investors are in total despair and the market soon capitulates. Both greed and fear extremes can be quantified by the TR-Osc's over- and undervaluation markers.

The second behavioral anchor is the tendency of herding with the crowd. When neither greed nor fear is at extreme levels, investors have a behavioral bias toward crowd-herding. Once a trend is established in either up or down direction, more investors will jump onboard the momentum train and price momentum will solidify into sustainable trends. The collective movement of the masses is tracked by the TR-Osc's vector sensor.

Concluding remarks

Unlike fundamental factors which can be altered by paradigm shifts over long arcs of time, human behaviors which are hardwired into our brains have not changed for thousands of years. The efficient market hypothesis assumes that markets are made up of a large number of rational investors efficiently digesting all relevant information to maximize their wealth. Behavioral finance theory suggests that investors are often driven by the inherent cognitive psychology of people whose decisions are often irrational and their actions exhibit behavioral biases. Perhaps the aberration (the malfunctioned mean reversion) observed in many of the traditional valuations ratios suggests that investors are not 100% homo economicus beings after all. More often than not, investors behave irrationally when they are besieged by emotions.

The TR-Osc captures the essence of both traditional finance and behavioral economics by reading investors' value perception from both the rational and the emotional wirings of their brains. It elucidates many valuable but abstract concepts from both schools into quantitative, objective and actionable investment strategies. As long as humans continue to use their dual-process brains (see also Dr. Daniel Kahneman) in decision making, TR-Osc will likely endure as a calibrated valuation gauge until humans evolve into the next stage.

The TR-Osc asserts that the current stock market is not overvalued. Instead, since mid-2015, its vector has been reverting towards its stable historical mean.

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


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