Seasonality: Ask for the Mechanism
Seasonality is any market pattern that repeats by calendar date rather than by price behavior. A few have physical or institutional causes, such as harvests, heating demand or tax-year flows. Most are found by scanning calendars, and an annual effect over twenty years is twenty observations.
How it works
Seasonality is any market pattern that repeats by the calendar rather than by price. The claim is that a particular month, weekday or run-up to a holiday behaves differently from the rest of the year. It is a statement about dates, not structure.
Two very different things travel under the same name. One has a cause you can point at, such as harvest cycles in agricultural commodities or heating-oil demand. The other was found by searching dates for anomalies.
The mechanical ones rest on somebody being obliged to act at a set time. Crops arrive when they arrive, and heating demand rises in winter. Tax-year-end flows and index rebalancing dates move money on a published schedule.
Why the sample is smaller than it looks
Slice the year finely enough and a pattern is certain to appear. Month, week of month, weekday, day of month, days before a holiday: each slicing multiplies the comparisons being run. Something clears any threshold by chance alone.
The point almost nobody states out loud is the observation count. An annual effect studied across twenty years is twenty observations. Calling it twenty years of data borrows credibility from a sample that does not exist.
Monthly effects give twelve readings a year, but they are not independent. The same regime and positioning run through consecutive months. Correlated observations inflate apparent significance without adding information.
In practice
Ask for the mechanism before accepting the statistic. Name the participant, the obligation and the date. If nobody can say who is forced to transact, treat the pattern as a search result.
Trading volume genuinely is seasonal, and that part is observable. Holiday weeks and mid-summer are thinner. Thin books mean wider spreads and worse fills, which is a use of seasonality needing no forecast.
Evaluation is slow in a way that defeats most people. An annual effect adds one observation per year, so abandoning it takes decades to justify on evidence.
A single unusual year can manufacture the whole average. One crisis or policy shock in the right month drags the mean across every other year.
A calendar says when to act and nothing about where you are wrong. A date has no invalidation level, so the stop must come from structure, from volatility, or from a loss accepted in advance.
The claims worth naming, and what they need
The famous seasonal claims are famous because they are memorable, not because they are settled. The January effect, “sell in May”, the Santa Claus rally and tax-year-end flows all circulate widely and are easy to state in a sentence. That memorability is why they survive without a mechanism attached.
Each one can be examined the same way. Ask what the proposed cause is, whether it is documented independently of the price pattern, and whether it still operates. Tax deadlines move, fund structures change, and a widely published effect gets front-run.
Some causes are durable because they are physical. Crops do not read research papers, and cold weather is not arbitraged away. Institutional causes are more fragile: a rule that creates a flow can be rewritten.
That is the whole test in one line. Name the cause, check that it is still in force, then decide whether the remaining edge survives the cost of trading it.
What seasonality is not
- Not a forecast. It describes an average across years, not the year you are in.
- Not a large sample. Twenty years of an annual effect is twenty observations.
- Not an entry signal. A date supplies timing and nothing about level or risk.
- Not a substitute for a mechanism. A statistic without a cause is a search result.
How this connects
Tax loss harvesting is the mechanism behind the best-known year-end claims. Investors sell losing positions before the tax year closes, and the wash sale rule stops them buying straight back, which is the documented cause usually offered for late-year selling pressure in beaten-down stocks.
Physical seasonality is usually priced in advance. A futures contract for a crop or a fuel embeds the expected season across its delivery months, so the known part of a seasonal pattern is usually in the price before anybody trades it.
A calendar rule is a form of market timing. Moving in and out on dates carries the same cost as any other timing method: being absent when the unexpected move arrives.
Participation also varies inside the day. Differences across trading sessions and after hours repeat every day rather than once a year, which gives them a far larger sample than any annual effect.
Expectancy is the test a seasonal has to pass after costs. A pattern that is right in most years can still lose money if its losing years are large, because expectancy weighs size as well as frequency.
Walk forward testing is the honest check, and it is painfully slow here, since each out-of-sample step waits for another year.
And mean reversion is the usual rival explanation. A strong month followed by a weak one may be an ordinary pullback after an extended move rather than anything about the date.
When it fails
- The cause has expired. Institutional seasonals die when the rule, deadline or fund structure that created them is changed. The record still shows the pattern; the market no longer has the reason.
- The pattern was never there. With enough calendar slices, overfitting produces effects that vanish the moment they meet data they were not fitted on.
- One year is carrying the average. Remove the largest contributing year and the remainder is often noise, so the spread of outcomes matters more than the mean.
- The market is in a range and nothing else is happening. In a flat year the calendar is the only visible story, so ordinary drift gets attributed to it.
- The move arrives as an opening gap you cannot participate in. Anticipated seasonals get priced overnight, leaving the date-based entry filled well after the move.
- Costs eat the edge. Seasonal trades are infrequent and the expected move modest, so a rule firing once a year has few chances to earn back each round trip.
The original data
The figures below come from this site’s own measurement files. From a scan of a corpus of 24,971 trading and investing videos: “seasonality” appears in the title of 1 video, at 65 views, from 1 channel.
Two ordinary indicators from the same file give the scale. “bollinger” appears in 268 videos, median 4,842 views, across 204 channels; “ichimoku” appears in 151 videos, median 10,245 views, across 99 channels.
Set any calendar claim against the base rate first. The S&P 500 closed up on 53.1% of trading days from 1950 to 2026 and finished higher over 59% of ten-day windows, so a seasonal window that usually rises in a rising market may be showing the drift rather than the date.
The question: It worked in 17 of 20 years. Trade it?
Read the corpus numbers as a demand signal, not a quality signal. Almost nobody publishes on seasonality because almost nobody searches for it. The base rate matters more: a date that rises most years in an index that rises most of the time has not yet shown anything, and a rule firing rarely has few chances to earn back each round trip.
Now take the hypothetical above. A pattern that worked in seventeen years out of twenty — hypothetical, not measured — is still twenty observations, three of them losses, with no mechanism stated. So write down the cause, the date it operates and the loss you will accept; if you cannot fill in the first field, do not take the trade.
Related
Backtesting is where seasonal claims are born, and its discipline of holding out data matters most when each year contributes one observation. Overfitting is the specific failure that calendar slicing invites, because the possible date filters are many and the independent observations are few. Commodities are where mechanical seasonality genuinely lives, since harvests, storage and weather-driven demand impose schedules no publication can arbitrage away.
I do not trust a seasonal chart that arrives without a reason attached. The useful ones are boring, where somebody can tell you exactly who is buying or selling and why they have no choice. Without such a person, the pattern is usually the calendar being sliced until something looks interesting. I watch the quiet weeks for costs and liquidity, not for direction.
— Michael Whitman
This page is educational, not financial advice. Test every idea on your own charts before risking money.