Stock market cycles

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Stock market cycles are the long-term price patterns of stock markets and are often associated with general business cycles.[1] They are key to technical analysis where the approach to investing is based on cycles or repeating price patterns.

The efficacy of the predictive nature of these cycles is controversial and some of these cycles have been quantitatively examined for statistical significance.

Well known cycles include:[2]

Investment advisor Mark Hulbert has tracked the long-term performance of Norman Fosback’s a Seasonality Timing System that combines month-end and holiday-based buy/sell rules. According to Hulbert, this system has been able to outperform the market with significantly less risk.[7] According to Stan Weinstein there are four stages in a major cycle of stocks, stock sectors or the stock market as a whole. These four stages are (1) consolidation or base building (2) upward advancement (3) culmination (4) decline.[8]

Short term and longer term cycles[edit]

Cyclical cycles generally last 4 years, with bull and bear market phases lasting 1–3 years, while Secular cycles last about 30 years with bull and bear market phases lasting 10–20 years. It is generally accepted[citation needed] that in early 2011 the US stock market is in a cyclical bull phase as it has been moving up for a number of years. It is also generally accepted[citation needed] that it is in a secular bear phase as it has been stagnant since the stock market peak in 2000. The longer term Kondratiev cycles are two Secular cycles in length and last roughly 60 years. The end of the Kondratiev cycle is accompanied by economic troubles, such as the original Great Depression of the 1870s, the Great Depression of the 1930s and the current Great Recession.

Compound cycles[edit]

The presence of multiple cycles of different periods and magnitudes in conjunction with linear trends, can give rise to complex patterns, that are mathematically generated through Fourier analysis.

In order for an investor to more easily visualise a longer term cycle (or a trend), he sometimes will superimpose a shorter term cycle such as a moving average on top of it.

A common view of a stock market pattern is one that involves a specific time-frame (for example a 6-month chart with daily price intervals). In this kind of a chart one may create and observe any of the following trends or trend relationships:

  • A long-term trend, which may appear as linear
  • Intermediate term trends and their relationship to the long-term trend
  • Random price movements or consolidation (sometimes referred to as 'noise') and its relationship to one of the above

For example, if one looks at a longer time-frame (perhaps a 2-year chart with weekly price intervals), the current trend may appear as a part of a larger cycle (primary trend). Switching to a shorter time-frame (such as a 10-day chart using 60-minute price intervals), may reveal price movements that appear as shorter-term trends in contrast to the primary trend on the six-month, daily time period, chart.

Dynamic cycles[edit]

Real cyclic motions are not perfectly even; the period varies slightly from one cycle to the next because of changing physical environmental factors. This dynamic behavior is also valid for financial market cycles. It requires an awareness of the active dominant cycle parameter and requires the ability to verify and track the real current status and dynamic variations that facilitate projection of the next significant event. Cycles morph over time because of the nature of inner parameters of length and phase. Active Dominant Cycles in financial markets do not abruptly jump from one length (e.g., 50) to another (e.g., 120). Typically, one dominant cycle will remain active for a longer period and vary around the core parameters. The “genes” of the cycle in terms of length, phase, and amplitude are not fixed and will morph around the dominant mean parameters.

Steve Puetz calles this "Period variability": “Period variability – Many natural cycles exhibit considerable variation between repetitions. For instance, the sunspot cycle has an average period of ∼10.75-yr. However, over the past 300 years, individual cycles varied from 9-yr to 14-yr. Many other natural cycles exhibit similar variation around mean periods.”[9][10]

These periodic motions abound both in nature and the man-made world. Examples include a heartbeat or the cyclic movements of planets. Although many real motions are intrinsically repeated, few are perfectly periodic. For example, a walker’s stride frequency may vary, and a heart may beat slower or faster. Once an individual is in a dominant state, the heartbeat cycle will stabilize at an approximate rate of 85 bpm. However, the exact cycle will not stay static at 85 bpm but will vary +/- 10%. The variance is not considered a new heartbeat cycle.

To arrive at more reliable and robust information on the dominant cycle for financial markets, the following steps should be performed:[11]

  • Step 1: A cycle detection algorithm should have a dynamic filter for detrending, which is included for pre-processing. This ensures that the data is not affected by trending information.
  • Step 2: Subsequently, a cycles engine performs a spectral analysis based on an optimized Discrete Fourier Transform and then isolates those cycles that are repetitive and have the largest amplitudes. Research results have shown that an adapted Goertzel algorithm is most suitable when it comes to detecting cycles in financial time series.
  • Step 3: In a third step, the statistic reliability of each cycle is evaluated. The goal of this procedure is to exclude cycles that have been influenced by one-time random events (e.g. news).

One of the algorithms used for this is a more sophisticated Bartels Test. The test builds on detailed mathematics (statistics) which measures the stability of the amplitude and phase of each cycle. Bartels’ statistical test for periodicity, published at the Carnegie Institution of Washington in 1932, was embraced by the Foundation for the Study of Cycles decades ago as the best single test for a given cycle's projected reliability, robustness, and consequently, usefulness. The method provides a direct measurement of the likelihood that a given cycle is genuine. The higher the Bartels score is (above 70%, up to 100%), the higher the likelihood that the cycle is genuine and has not been influenced by one-time events.[12]

  • Step 4: An important final step in making sense of the cyclic information is to establish a measurement for the strength of a cycle. Once the third step is completed, cycles that are dominant (based on their amplitude) and genuine with reference to their driving force in the financial market are detected. But for trading purposes, this does not suffice. The price influence of a cycle per bar on the trading chart is the most crucial information.
  • Step 5: Sort the outcome according to the calculated cycle strength score.

After a cycles engine has completed all five steps, the cycle at the top of the list (with the highest cycle strength score) will give information on the dominant dynamic cycle in the analyzed market. In fact, the wavelength of this cycle is the dominant dynamic cycle, which is useful for trading financial markets.

Use of multiple screens[edit]

A stock market trader will often use several "screens" or charts on their computer with different time frames and price intervals in order to gain valuable information for making profitable buying and selling (trading) decisions.

Often expert traders will emphasize the use of multiple time frames for successful trading. For example, Alexander Elder suggests a Triple Screen approach.[13][14]

  • Longer-term screen: To identify the long-term trend and opportunities
  • Middle screen: To identify the best day(s) on which to locate a buy or sell opportunity
  • Finer screen: To identify the optimum intra-day price at which to buy or sell a given security


  • Conference Board - Consumer Confidence, Conference Board’s Present Situation Index - Major turns in the Conference Board’s Present Situation Index tend to precede corresponding turns in the unemployment rate—particularly at business cycle peaks (that is, going into recessions). Major upturns in the index also tend to foreshadow cyclical peaks in the unemployment rate, which often occur well after the end of a recession. Another useful feature of the index that can be gleaned from the charts is its ability to signal sustained downturns in payroll employment. Whenever the year-over-year change in this index has turned negative by more than 15 points, the economy has entered into a recession.[15] The most useful methods to predict business cycle use methods similar to the organization as Eurostat, OECD and Conference Board.[16]
  • Federal Reserve Bank of Chicago - Chicago Fed National Activity Index (CFNAI) Diffusion Index - The Chicago Fed National Activity Index (CFNAI) Diffusion Index is a macroeconomic model of Business Cycle Models. [When passing thru a value of -0.35, the] “CFNAI Diffusion Index signals the beginnings and ends of [ NBER ] recessions on average one month earlier than the CFNAI-MA3.” … the crossing of a -0.35 threshold by the CFNAI Diffusion Index signaled an increased likelihood of the beginning (from above) and end of a recession (from below)...[17][18]
  • Federal Reserve Bank of Philadelphia - Aruoba-Diebold-Scotti Business Conditions Index (ADS Index) - is published by the The Federal Reserve Bank of Philadelphia. The average value of the ADS index is zero. Progressively bigger positive values indicate progressively better-than-average conditions, whereas progressively more negative values indicate progressively worse-than-average conditions.[19][20]
  • Federal Reserve Bank of New York - Yield Curve - the slope of the yield curve is one of the most powerful predictors of future economic growth, inflation, and recessions.,[21][22][better source needed]
  • BofA Merrill Lynch - Global Wave - has indicators from around the world such as industrial confidence, consumer confidence, estimate revisions, producer prices, capacity utilization, earnings revisions, and credit spreads. When the Global Wave troughs, THEN the MSCI All Country World equity index is up 14% on average over the next 12 months.[23][24]
  • JP Morgan - Equities tend to do well in environments featuring rising growth rates as well as falling inflation.[25]

See also[edit]


  1. ^ Channels & Cycles: A Tribute to J. M. Hurst, by Brian Millard, Traders Press (March 18, 1999)
  2. ^ The Next Great Bubble Boom: How to Profit from the Greatest Boom in History, 2005–2009, by Harry S. Dent, Jr., Free Press (September 2004
  3. ^ The Harvard Business Review, December 6, 2006
  4. ^{CDFB00A7-8DAA-49A9-A2EF-83502D872CDF} For everything a season?, By Mark Hulbert, MarketWatch, Oct. 28, 2005
  5. ^ STRATEGIES; Playing the January Effect, Whatever Its Cause, By MARK HULBERT, December 5, 1999
  6. ^ The 17.6 Year Stock Market Cycle, Connecting the Panics of 1929, 1987, 2000 and 2007 by Kerry Balenthiran, Harriman House, February 2013
  7. ^ Hulbert on Fosback seasonality system (Commenting on Barron's article, Trading the Calendar Can Pay Off Big, By Mark Hulbert, Thursday, November 3, 2005)
  8. ^ Weinstein S., Secrets for Profiting in Bull and Bear Markets, McGraw Hill, 1988, p. 31
  9. ^ Puetz, Stephen (May–June 2014). "Evidence of synchronous, decadal to billion year cycles in geological, genetic, and astronomical events". Chaos, Solitons & Fractals. 62–63: Page 55–75. doi:10.1016/j.chaos.2014.04.001. 
  10. ^ von Thienen, Lars (2010). "Dynamic Cycles Explained". whentotrade. 
  11. ^ von Thienen, Lars (April 27, 2014). Decoding The Hidden Market Rhythm - Part 1: Dynamic Cycles: A Dynamic Approach To Identify And Trade Cycles That Influence Financial Markets. CreateSpace Independent Publishing. ISBN 978-1499283495. 
  12. ^ Bartels, Robert (March 1982). "The Rank Version of von Neumann's Ratio Test for Randomness". Journal of the American Statistical Association. 77 (377): pp. 40–46. doi:10.1080/01621459.1982.10477764. 
  13. ^ Dr. Alexander Elder, Trading For A Living” (1993)
  14. ^ Triple Screen Trading System - Part 1 by Jason Van Bergen
  15. ^ Federal Reserve Bank of New York, Consumer Confidence: A Useful Indicator of ... the Labor Market? Jason Bram, Robert Rich, and Joshua Abel ... Conference Board’s Present Situation Index
  16. ^ MONITORING AND PREDICTION OF BUSINESS CYCLE Andrea Tkáčová 1 - Barbora Gontkovičová 2 - Emília Duľová Spišáková 3
  17. ^ Chicago Fed National Activity Index (CFNAI) Diffusion Index
  18. ^ The Chicago Fed National Activity Index (CFNAI) Diffusion Index and business cycles (see Figure 7)
  19. ^ Aruoba-Diebold-Scotti Business Conditions Index
  20. ^ Aruoba-Diebold-Scotti Business Conditions Index for the past year
  21. ^ Arturo Estrella & Frederic S. Mishkin, The Review of Economics & Statistics, Predicting U.S. Recessions: Financial Variables as Leading Indicators, 1998
  22. ^ Business cycle#Yield curve
  23. ^ Bank of America, Merrilly Lynch, RIC-Themes and Charts | 21 February 2017, page 12
  24. ^ Business Insider, Apr. 23, 2012, 2:08 PM | BofA: The Economic 'Global Wave' Just Turned Positive And That's Extremely Bullish For Stocks
  25. ^ JP Morgan | Abdullah Sheikh, Director of Research, Strategic Investment Advisory Group (SIAG). Regime change: Implications of macroeconomic shifts on asset class and portfolio performance | Research Summit 2011

External links[edit]