Affirm Holdings Inc Seasonality
Historical data through November 13, 2026 · 1,467 trading sessions. Seasonal averages describe the past; they are not a forecast and not investment advice. Disclaimer
Across 6 years of monthly data (2021–2026), Affirm Holdings Inc's strongest month on average has been August (+25.7% average, higher in 5 of 6 years). Its weakest has been February (−13.6% average, higher in 1 of 6 years).
- Jan −1.0% Up 2/5
- Feb −13.6% Up 1/6
- Mar −10.6% Up 1/6
- Apr −2.5% Up 2/6
- May +7.8% Up 3/6
- Jun +4.1% Up 5/6
- Jul +6.5% Up 2/6
- Aug +25.7% Up 5/6
- Sep −4.3% Up 2/6
- Oct +6.3% Up 4/6
- Nov +20.2% Up 2/5
- Dec −3.3% Up 2/5
A month's return is the close on its last trading day compared with the previous month's last close. Averages are historical and not a forecast; any single year can differ sharply.
AFRM has 4 complete years of data, too few for reliable seasonal statistics. The charts show each year as it happened; treat any average as anecdotal until more history accumulates.
Updated This AFRM seasonality chart is built from 1,467 trading days of historical data, spanning from through — covering 6 years of market activity.
Sessions traded per month for AFRM. Months with more sessions carry more weight in every seasonal average below.
Year-by-year performance for Affirm Holdings Inc, so a seasonal pattern can be read against the trend it sits inside.
Intraday momentum. Each cell sums the daily open-to-close percent changes inside that month, leaving out the overnight gaps between one close and the next open. Compare it with the next heatmap to see whether a month's gains came during trading hours or overnight. Very old years can show 0.00 where the data source recorded a single price per day.
Held through the month. Each cell is the change from the first session open to the last session close — the return of entering on day one and exiting on the final bar.
Daily percent change, January 13, 2021 to November 13, 2026
Close is plotted by default. Add open, high, low or volume from the legend, and drag across the chart to zoom into any period.
Same date, every year
Each line is one calendar year of Affirm Holdings Inc, measured as its return since the previous year's final close (year to date) and drawn on a common January-to-December axis. The bold average line shows where AFRM has typically stood on each date across every complete year on file.
How the average is built. Returns are compounded from daily closing prices. Every year counts on every date: on weekends and market holidays a year keeps its last close, so the average is never built from a different mix of years on different days. Only complete calendar years enter the average, so a partial current year or a mid-year listing cannot distort it. This view surfaces date-anchored effects such as tax-loss selling into December, the January effect, quarter-end rebalancing and recurring earnings seasons.
Day-of-year seasonality for AFRM
The bold blue line is the multi-year average and orange is the current year; thinner coloured lines are the most recent years, and older years appear in grey when you choose All years. When the current-year line sits above the average, AFRM is ahead of its usual pace for the date. Hover any point to compare each year with the average on that day.
AFRM has 4 complete years of data, too few for reliable seasonal statistics. The charts show each year as it happened; treat any average as anecdotal until more history accumulates.
Weekday and week of year
Here each session is placed by its week number and weekday instead of its date. Weeks run Sunday to Saturday and week 1 is the week that contains January 1. Because calendar dates shift by a day or two each year, week numbers line up comparable sessions: week 14 ran from Monday, April 1 to Friday, April 5 in 2024, from March 31 to April 4 in 2025 and from March 30 to April 3 in 2026. Comparing "week 14, Monday" across years therefore compares the first Monday of the second quarter each time, which a fixed date such as April 1 cannot do.
Use this view for weekday tendencies (soft Mondays, strong Fridays) and to see how a pattern drifts week by week through the year. Each point is the year-to-date return at that week and weekday, averaged across complete years.
Year-to-date path by week number and weekday
Each line is one year building through the weeks; the bold line is the average. Where the yearly lines bunch together, the pattern repeated year after year; where they fan out, that stretch of the calendar was driven by events rather than the season. Labels read "week - weekday", for example "14 - Mon".
AFRM has 4 complete years of data, too few for reliable seasonal statistics. The charts show each year as it happened; treat any average as anecdotal until more history accumulates.
Trading day of the month
Sessions are numbered by their position inside each month: trading day 1 is the first session of the month, day 2 the second, whatever the calendar date. Most months have 19 to 23 sessions. Lining months up this way exposes flow-driven effects that calendar dates blur, such as pension and payroll inflows around the turn of the month, mid-month options expiry and fund rebalancing in the final sessions.
The chart runs from the first trading day of January to the last trading day of December, so each point is the year-to-date return after that session. On the axis, trading day N of a month is drawn at day N of that month.
Year-to-date path by trading day of the month for AFRM
Steps that recur at the same point of every month, a rise over the first few sessions or a dip before month-end, are the signature of flows rather than news. Compare the slope of the average around the start and end of each month with the middle weeks.
AFRM has 4 complete years of data, too few for reliable seasonal statistics. The charts show each year as it happened; treat any average as anecdotal until more history accumulates.
AFRM vs the market
AFRM seasonality compared with the S&P 500 (SPY)
Both lines are average year-to-date returns over the same complete years, so the comparison is like for like. The dashed line is the gap: when it rises, AFRM has typically outperformed the market over that stretch of the calendar; when it falls, it has typically lagged. A stock with a strong seasonal line can still trail the market if SPY rises even more in the same weeks.
Not enough overlapping complete years with SPY to compare yet.
Calendar date vs week and weekday vs trading day of the month
The averages from methods 1, 2 and 3 above, placed on one calendar axis (week slots and trading days are drawn on the dates they fell on in 2021). Lines that overlap mean the seasonal pattern holds however you count the sessions; a gap shows where an effect depends on the counting, typically around month ends and holidays.
Not enough complete years to compare the three methods yet.
How to read this page
What is AFRM seasonality?
Seasonality is the tendency of a security to behave differently at particular points of the calendar. This page measures it for Affirm Holdings Inc three ways (by calendar date, by week and weekday, and by trading day of the month) across 1,467 trading sessions from January 13, 2021 to November 13, 2026.
How are the averages calculated?
From daily closing prices, compounded rather than added, so a 10% gain followed by a 10% loss counts as the 1% loss it really is. Each year's year-to-date return is carried over weekends and holidays, and only complete calendar years enter the averages. Prices are adjusted for splits but not for dividends, so the figures are price returns.
What does "up in 23 of 45 years" mean?
It counts the years in which the stated period ended higher than it started. Together with the average and the median it shows how reliable a pattern has been: an average lifted by one or two exceptional years can still have a win rate near 50%.
Why do the three methods sometimes disagree?
They line the data up differently. A calendar date falls on a different weekday and a different trading day of the month every year, so an effect that is really about month-end flows shows clearly in method 3 and only faintly in method 1. Agreement across all three is the stronger signal.
How current is the data?
Prices are refreshed automatically after each trading session and checked for gaps, duplicates, bad prints and unadjusted splits before anything is published. The most recent session on file is November 13, 2026; every table, chart and statement on this page is rebuilt from that same data.
Can seasonality be traded on its own?
No. These are historical averages over a limited number of years, and any single year can differ completely. Treat a seasonal pattern as context for a decision you have other reasons to make, size positions for the risk, and see the Disclaimer.
Data last refreshed November 13, 2026. Seasonality figures are historical averages drawn from 1,467 trading sessions; they are not a forecast, and nothing on this page is investment advice. Please do your own research and see the Disclaimer.