What Is Market Seasonality?

Every market has a calendar. Not in the sense of holidays and earnings dates — though those matter too — but in a deeper sense: certain months have, over long stretches of history, tended to be better or worse for certain assets than others.

This is seasonality: the tendency for an asset's price to move in a recurring pattern tied to the calendar, repeating with enough consistency across years that it shows up in the data — even though no single year is guaranteed to follow it.

A simple example

Take the S&P 500. Over the last 15 years, September has been one of its weakest months on average, while November has been one of its strongest. This isn't a one-off — it's shown up often enough, across enough years, that it's become one of the most discussed patterns in market commentary.

Why might this happen? There's no single proven cause, but commonly cited contributors include: fund managers rebalancing portfolios after summer, tax-related selling ahead of fiscal year-ends, year-end "Santa Claus rally" buying into the holidays, and simple herd behavior — if enough people believe a pattern exists and act on it, the pattern can become partly self-reinforcing.

What seasonality is not

This is the part that matters most, and where a lot of casual seasonality content goes wrong.

• It's not a prediction. A "70% win rate" for a given month means 7 of the last 10 years were positive — not that this year has a 70% chance of being positive. The other 3 years happened too, sometimes sharply negative.
• It's not a trading signal on its own. Buying an asset purely because "it's historically a good month" ignores everything else happening in the market — valuations, macro conditions, company-specific news.
• It can break down. Patterns that held for 10-15 years can weaken or reverse as markets structurally change — new participants, new regulations, new macro regimes.

How seasonality is measured

The core calculation is simple, even if the underlying data isn't always easy to get right:

1. Take an asset's monthly closing prices going back as many years as possible.
2. Calculate the percentage return for each individual month (January 2015, January 2016, January 2017, etc.).
3. Group all the "Januaries" together, all the "Februaries" together, and so on.
4. Calculate the average return and the win rate — the percentage of years that month was positive — for each calendar month.

The result is a 12-month profile showing which months have historically been stronger or weaker for that specific asset. More years of data generally means a more reliable average, though even 15 years gives you only 15 observations per month — a relatively small sample, which is why we always show the observation count alongside any seasonal statistic.

Why it varies by asset

Seasonality isn't one-size-fits-all. A broad market index, a tech stock, a gold ETF, and Bitcoin can each have very different seasonal profiles, often shaped by the underlying dynamics of that sector:

• Retail stocks often see strength heading into the holiday shopping season.
• Energy stocks can show patterns tied to seasonal demand for heating and driving.
• Tech and growth stocks often show an "earnings season" rhythm tied to quarterly reporting.
• Gold has historically shown strength around certain cultural and economic calendar events globally.

This is why looking at an individual asset's own history — rather than assuming "the market" has one universal seasonal pattern — matters.