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2008issue C021-4

Two-layer equity regimes from seasonality and price history

A two-layer market-regime checklist for a single index position. Layer one crosses calendar month with the presidential-year cycle. Layer two asks whether trailing-return or moving-average displacement buckets change the next-horizon distribution.

  • Seasonal analysis groups index changes by calendar month and by place in the four-year presidential-year cycle to describe typical intra-year regimes.
  • Seasonality analysis tests whether month-of-year averages still differ after they are crossed with presidential-year type.
  • In the archive sample, trailing 12-month and 36-month S&P 500 change, and six-month moving-average displacement, were only weakly related to later index change.
  • Market-regime classification places one index position in a weeks-to-months context by combining calendar labels with rate-of-change buckets or moving-average displacement.
Entries in this reading3 entries
Market regime classification52 readingsCurrent trackSeasonality analysis65 readingsSeasonal analysis67 readings

A two-layer checklist for one index position

This archive note teaches a two-layer market-regime checklist for a single index position. Layer one is calendar month crossed with the presidential-year cycle. Layer two asks whether trailing-return or moving-average displacement buckets change the next-horizon distribution.

Seasonal analysis groups index changes by calendar month and by place in a multi-year political cycle to describe typical intra-year regimes. Seasonality analysis tests whether month-of-year averages still differ after they are crossed with another regime label, such as presidential-year type. Market-regime classification assigns the tape to a context bucket built from calendar labels, trailing-return percentiles, or displacement from a moving average.

Layer one: month and the presidential-year cycle

The presidential-year cycle is a four-year calendar split into post-election, midterm, pre-election, and election years. In monthly averages from 1945 through July 2007, pre-election years averaged 1.40% per month, election years 0.73%, and post-election and midterm years each 0.36%.

September's average monthly change was negative in every presidential-year type, while pre-election January, April, and December averaged 4.24%, 3.24%, and 3.20%.

Editorial interpretation: treat this calendar cross as the first context check for one index position on a weeks-to-months horizon. It describes typical intra-year regimes. It does not turn a month label into a stand-alone forecast.

Layer two: trailing change and moving-average displacement

A rate-of-change bucket is a historical percentile range of trailing index change used to classify the current trend regime. Across 724 monthly observations from January 1946 through May 2007, trailing 12-month S&P 500 change correlated -0.073 with the next 12-month change. In that sample, the lowest trailing-return decile (declines of 12.7% or more; 73 observations) was followed by a 12.2% average next-12-month change, versus an 8.4% full-sample average.

A linear regression of next-year S&P 500 change on last-year change had slope -0.072, intercept 9.3%, and R-squared 0.5%.

Across 701 observations from January 1948 through May 2007, 36-month S&P 500 change correlated -0.098 with the next 12-month change. The lowest 36-month quartile (9.00% or less; 175 observations) was followed by a 10.33% average next-12-month change, while the highest quartile (above 45.65%) was followed by 7.36%, versus a 9.13% sample average.

Moving-average displacement is the percentage gap between the latest close and a six-month arithmetic average of closes. From June 1945 through May 2007 (725 observations), the S&P 500 percentage gap versus its six-month arithmetic average correlated 0.018 with the next 12-month change. In that displacement sample, the lowest quartile (1.32% or more below the average) and the highest quartile (4.88% to 14.24% above) were followed by 10.11% and 11.19% next-12-month averages, versus 8.70% overall.

The same six-month displacement measure correlated 0.004 with the next one-month change (724 observations), and the highest quartile averaged 1.01% versus a 0.67% all-month mean.

What the two layers do together

Editorial interpretation: in this archive sample the price-only layer is only weakly related to later S&P 500 change. The calendar layer still sorts months by presidential-year type. Used together, seasonal analysis, seasonality analysis, and market-regime classification put one index position into a weeks-to-months context instead of treating the trade as an isolated price reading.

Educational research material, not investment advice. Historical source context does not establish present-day performance.
22 of 41 in the Seasonality analysis track
20081-1 pp.Next on Seasonality analysisSeasonal futures as a regime filter, not a calendar rulePhysical commodity futures such as grains and energies display a seasonal-tendency tied to harvest or product-demand cycles, and similar intra-year maps are also described in Treasury, currency, and stock-index futures.
All readings on this track · 41 readings
  1. 1989Evaluating venue volume as a speculation-breadth signal
  2. 1991A thirty-name price-weighted average as a seasonal regime classroom
  3. 1991Ranked half-year rate changes as an equity signal filter
  4. 1991Demographic wave as a market-regime overlay
  5. 1994Seasonal range regimes as a futures context overlay
  6. 1996Evaluating presidential party terms as equity regimes
  7. 1996A dominant cycle is a baseline, not a reprint
  8. 1996Seasonality and presidential election cycle regimes
  9. 1997Stacking calendar regimes around election years
  10. 1997Calendar seasonality as a testable trading procedure
  11. 1997Lunar phase delay as a testable seasonal regime
  12. 1998Seasonal system construction without curve-fitting
  13. 1999Crowd life cycle as a market regime map
  14. 2001Regime-dependent cycle timing after four-year and seasonal lows
  15. 2002A 2002 case study in regime-first seasonal selection
  16. 2002Seasonal windows and dominant-cycle rules
  17. 2002Fifty-four-year wholesale cycle as an inflation-deflation regime map
  18. 2004The championship conference rule as a yearly regime case study
  19. 2004Election-year seasonality as trade regime context
  20. 2006Two-ten inversion as an intermarket regime filter
  21. 2006Midterm-to-presidential seasonal holding window
  22. 2008Two-layer equity regimes from seasonality and price history
  23. 2008Seasonal futures as a regime filter, not a calendar rule
  24. 2008Retesting seasonal rules when regimes change
  25. 2010Corn and wheat staggered calendars as dollar-neutral seasonal spreads
  26. 2011Name the S&P 500 trend regime before using weekly and monthly seasonality
  27. 2012Seasonal windows that wait for confirmation
  28. 2013Pair-sleeve rotation as a two-state sector regime-switch
  29. 2013Lunar phase as a seasonal overlay on implied volatility
  30. 2013Soybean seasonal highs in a five-year carryover regime
  31. 2014Year-end tax-loss selling as a seasonal regime
  32. 2017Calendar-window overlays that mute mechanical signals without rewriting the system
  33. 2017A four-year cycle and volume case study of a secular bear
  34. 2017Treat the valuation climate as climate and implied-volatility extremes as weather
  35. 2019Seasonal depth versus tracking for futures position sizing
  36. 2019Stacking cycle forecasts with seasonal regimes
  37. 2019Hit-rate gates for seasonal regime evaluation
  38. 2019July to October as a seasonal window, not a reason to own the name
  39. 2020A recession-regime checklist from valuation stretch and the yield curve
  40. 2020Treat a seasonal idea as a stay-or-sit holding procedure
  41. 2020A single position as a sleeve on a seasonal regime map
By fieldSeasonality and regimes131 readingsBy year200878 readings
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