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1997issue C091-5

When market cycles are absent more than present

Tradable cycles are estimated to occupy only about 15% to 30% of market time. Spectral tools are useful only after they show that a coherent oscillation is on, and they should stay silent once it fades.

  • Tradable market cycles are estimated to be present only about 15% to 30% of the time, so a cycle method must detect presence rather than assume a standing wave.
  • Maximum-entropy spectrum analysis is presented as the computer method for resolving short, shifting cycle lengths from limited price data.
  • The dominant cycle is the currently strongest repeating interval, and it is useful only while that interval remains coherent.
  • Spectral analysis maps cycle lengths and strengths so the first job is to judge whether oscillation is present at all.
Entries in this reading3 entries
Spectral analysis24 readingsCurrent trackMaximum entropy spectrum analysis30 readingsDominant cycle detection119 readings

Most of the time, there is no tradable cycle

Tradable market cycles are estimated to be present only about 15% to 30% of the time. A cycle method must first detect presence rather than assume a standing wave.

One cited technician described about 23% of price motion as oscillatory and only semi-predictable, which matches the claim that useful cycles are intermittent.

A cycle has a period, but not every swing is one

A classic market cycle is defined as a smooth rise from a low to a high and a smooth return over the same interval, with that interval called the period or cycle length. Simple spacing of successive lows can mark a cycle.

Seasonal agricultural and winter real-estate patterns are treated as a special 12-month case of a cycle, distinct from less regularly timed business cycles. Business-cycle amplitude is framed as a bounded swing from roughly +3% growth to about -1% recession, without requiring an exact repeating period.

How the spectral tools are used here

Maximum-entropy spectrum analysis is presented as the computer method for resolving short, shifting market cycles from limited data. It is a high-resolution estimate of which cycle lengths are strongest in a short price sample, designed to stay useful when those lengths keep shifting.

The dominant cycle is the currently strongest repeating interval in price, identified so a trader can act only while that interval remains coherent.

Spectral analysis is the conversion of a price series into a map of cycle lengths and strengths, used here to judge whether oscillation is present at all.

Short-term coherence is not a long-term law

Market prices are analogized to a two-dimensional drunkard's walk that can show short-term coherence even while remaining random over a longer span. The same contrast is drawn with a river: short-term meanders can look cyclic, yet overlaying many meanders is indistinguishable from a purely random path.

The practical task is therefore to recognize when a short-term cycle is present and to stop treating it as predictive once it fades.

Educational research material, not investment advice. Historical source context does not establish present-day performance.
16 of 28 in the Maximum entropy spectrum analysis track
19971-5 pp.Next on Maximum entropy spectrum analysisA spectral estimator that retunes indicators to the measured cycleA measured cycle period is treated as an independent construction parameter so indicator lengths can be retuned to current conditions instead of remaining on fixed settings.
All readings on this track · 28 readings
  1. 1984Constructing maximum-entropy spectra for dominant-cycle forecasts
  2. 1984How to construct a maximum-entropy cycle model
  3. 1984Constructing a maximum-entropy forecast from a chosen lookback
  4. 1985Constructing period-locked half-cycle and full-cycle averages
  5. 1986Why Fourier windows limit dominant-cycle resolution
  6. 1987Assembling short-lookback maximum-entropy cycle forecasts
  7. 1988Why a fitted dominant cycle is not a forecast
  8. 1989Evaluating commodity cycle personalities with spectral histograms
  9. 1989Evaluating next-session cycle forecasts with stops
  10. 1989Constructing cycle-aged volatility trailing stops
  11. 1990A channel signal-to-noise gate for dominant-cycle forecasts
  12. 1990Year-over-year dominant cycle personality audit
  13. 1991Cyclic entry from a locked dominant-cycle phase
  14. 1992Stationarity states on synchronized futures spectral contours
  15. 1997Hidden horizon assumptions in dominant-cycle readings
  16. 1997When market cycles are absent more than present
  17. 1997A spectral estimator that retunes indicators to the measured cycle
  18. 2000Constructing a Hilbert dominant cycle and a maximum-entropy refinement
  19. 2000Switch trend and cycle indicators after a half-cycle dwell test
  20. 2000Constructing a dominant-cycle squelch trend filter
  21. 2000Phasor displays for dominant-cycle construction
  22. 2002Low-lag trendline from elliptic and dominant-cycle notches
  23. 2004Spectral peaks are mode diagnostics, not forecasts
  24. 2004Compressive last-stage oscillator construction
  25. 2013Constructing trend failure curves from qualified-trend transitions
  26. 2014Lookback range, a two-lag smoother, and next-bar fills
  27. 2014Constructing a MESA stochastic with roofing and SuperSmoother filters
  28. 2016Constructing spectral heatmaps for dominant market cycles
By fieldCycles and spectral analysis77 readingsBy year199756 readings
All 30 readings tagged Maximum entropy spectrum analysis
Also on Maximum entropy spectrum analysis5 readings
1984How to construct a maximum-entropy cycle modelSpectral analysis1987Assembling short-lookback maximum-entropy cycle forecastsSpectral analysis1989Evaluating commodity cycle personalities with spectral histogramsSpectral analysis1990Year-over-year dominant cycle personality auditSpectral analysis1992Stationarity states on synchronized futures spectral contoursSpectral analysis