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.

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