Stock market cycles

Stock market cycles are the long-term price patterns of the stock market.

Description

There are many types of business cycles including those that impact the stock market.^[1]

In his book The Next Great Bubble Boom, Guna, a Harvard graduate and Fortune 100 consultant, outlines several cycles that have specific relevance to the stock market.^[2] Some of these cycles have been quantitatively examined for statistical significance.

The major cycles of the stock market include:

• The four-year presidential cycle in the US.
• Annual seasonality, also known as Sell in May or the Halloween indicator^[3]
• The "January effect"^[4]
• The lunar cycle^[5]
• The 17.6 Year Stock Market Cycle^[6]

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]

Longer term cycles: Cyclical, Secular and Kondratiev

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 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 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.

Theory

The four-year U.S. presidential cycle is attributed to politics and its impact on America's economic policies and market sentiment. Either or both of these factors could be the cause for the stock market's statistically improved performance during most of the third and fourth years of a president's four-year term.^[9]

The month-end seasonality cycle is attributed to the automatic purchases associated with retirement accounts.

The secular stock market cycles that last about 30 years move in lockstep with corresponding secular economic, social, and political cycles in the US.^[10]

Compound cycles

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.

Concept of Dynamic Cycles

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.”^[11][12]

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:^[13]

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.^[14]

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

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.^[15][16]

• 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

Technical indicators

The 'technical analysis' approach to investing is based on cycles or repeating price patterns. Some of the technical indicators used to measure and project patterns include oscillators, moving averages, and candlestick charts.

• Oscillators: These illustrate the price and volume cycles thereby allowing the investor/trader to identify relevant peaks and valleys within the trend itself.
• Moving Averages: There are many types of moving average indicators; for example, the exponential moving average shows a slightly different version of the price trends by smoothing out the short-term fluctuations.
• Candlestick charts: This is a specialized type of price chart that provides different information about price activity than the standard "mountain" style chart.

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]

Chicago Fed National Activity Index, Moving Average of 3 months (CFNAI-MA3)

When the CFNAI-MA3 index falls below (–0.7) historically indicating the economy has entered a recession.

THE CFNAI-MA3 TRACKS ECONOMIC EXPANSIONS AND CONTRACTIONS

The CFNAI is a coincident indicator of economic expansions and contractions. To highlight this fact, it is best to focus on the CFNAI-MA3.

In each of the seven recessions, the CFNAI-MA3 fell below -0.7

… near the onset of the recession.

… after the onset of a recession, when the index first crosses +0.2, the likelihood that the recession has ended according to the NBER business cycle measures is significant.

… we have found the crossing of the -0.7 threshold at least six months after a recession's trough to be a more reliable indicator of an increasing likelihood of an end of a recession.

… 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).

Federal Reserve Bank of Philadelphia

Aruoba-Diebold-Scotti Business Conditions Index (ADS Index)

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]

BofA Merrill Lynch Global Research, GLOBALcycle

The GLOBALcycle is a real-time indicator of economic activity. The indicator optimally extracts a common factor across data frequencies. Weekly asset-price information, as well as monthly and quarterly hard data feed the indicator. It is based on the ADS index developed at the Federal Reserve Bank of Philadelphia.

Federal Reserve Bank of New York

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. ^[21]

See also

• Wave
• Technical analysis
• Market timing
• Bottom (technical analysis)
• Market trends and Trend following
• Histoire des bourses de valeurs (French)

References

External links

• http://research.stlouisfed.org/fred2/graph/?id=EMRATIO
• http://www.philadelphiafed.org/research-and-data/real-time-center/survey-of-professional-forecasters/anxious-index/
• http://www.nber.org/cycles/
• https://www.wellsfargoadvisors.com/market-economy/economic-market-reports/stock-market-trend.htm

External links

• Secular Mean Reversion in the Stock Market