Stock Market Seasonality Statistics (2026)

Seasonality is the tendency for stock returns to cluster in particular calendar windows: certain months, quarters, and even days of the week that have, on average, run hotter or colder than the rest. These patterns come from the return record itself, not from a theory about why prices should move.

The important word is average. A pattern that shows up over 75 or 95 years is a base-rate tendency, not a forecast. September is the worst month on average, yet it has finished higher in plenty of individual years. Treat seasonality as context, not a trading signal.

Since 1950 the S&P 500's average monthly returns range from about +1.82% in November, the best month, to about -0.72% in September, the worst and the only month with a negative average (see the chart and table below). December (+1.49%) and April (+1.46%) round out the top three.

The pattern is lopsided toward the fourth quarter and early year: November, December, April, and July have historically been the standouts, while the summer-into-fall stretch (August and September) has been the soft patch. Ten of twelve months carry a positive average.

September is the market's problem child. Using data back to 1928, the S&P 500 has averaged roughly -1.1% in September and finished negative about 52 of 95 years, the only month that has closed lower more often than higher. Since 1950 the average is a milder but still negative -0.72%.

There is no clean explanation. Theories range from post-summer portfolio rebalancing and tax-loss positioning by funds to seasonal cash needs. Whatever the cause, the effect is real in the averages but far from guaranteed: September has produced strong gains in many individual years.

The late-year window is the market's sweet spot. November has averaged about +1.8% since 1950, December about +1.5%, and January about +1.1%, the run that gives 'Sell in May' its mirror image. Retail spending, year-end fund flows, and new-year optimism are the usual explanations offered.

This stretch also contains the Santa Claus rally and feeds the January Barometer, two of the most-followed seasonal signals. The clustering of strong months from November into early spring is why the 'Best Six Months' framing (November through April) has held up for decades.

The oldest seasonal saying tells investors to sell in May and return around Halloween. On average, it has some truth: since 1945 the S&P 500 has gained roughly 7% from November through April but only about 2% from May through October (see the chart and table below). Fidelity notes the winter half rose about 75% of years versus 66% for the summer half.

The Stock Trader's Almanac codified this as the 'Best Six Months' strategy, showing a +7.3% average DJIA gain in November-April versus +0.8% in May-October since 1950 (Almanac). But the edge is unstable: since 1990 the May-October stretch has actually averaged a small loss in some studies, and sitting out half the year risks missing big summer rallies.

The Santa Claus rally is a narrow seven-day window: the last five trading days of December plus the first two of January. Coined by Yale Hirsch in the 1972 Stock Trader's Almanac, it has averaged about +1.3% since 1950 and been positive roughly 76% of the time, well above the 0.3% and 58% you would expect from any random seven-day stretch (see the table below).

Its real fame is as a warning sign. The Almanac's rhyme, 'If Santa Claus should fail to call, bears may come to Broad and Wall,' captures the finding that years following a missing rally have tended to be weaker. Like all these signals, a small sample makes it more folklore than forecast.

'As January goes, so goes the year.' The January Barometer, another Hirsch creation, says the S&P 500's January direction predicts the full year. Since 1945, when January is positive the year has finished higher about 86% of the time, averaging roughly 16% in those years; Fidelity puts the all-year base rate at about 72% up (see the table below).

The catch is the downside. A negative January has still been followed by a positive year in about half the cases (14 of 29 since 1950), so the barometer works far better as a bull confirmation than a bear alarm. Some of its accuracy is simply that up years are common to begin with.

The January Effect is a separate, older anomaly from the January Barometer. It describes small-cap stocks tending to outperform in January, historically attributed to investors selling losers in December for tax reasons and buying back in the new year, plus year-end bonus inflows.

The effect was strongest in mid-20th-century data and has faded as it became widely known and as tax-loss harvesting spread through the year. Much of the historical January small-cap pop is now understood to have started in late December, blurring into the turn-of-the-year window.

Zoom into the week and old patterns appear. Over roughly 98 years, Monday has been the weakest day (about -0.07% average, lower more than half the time), while Wednesday has posted the highest average return (about +0.06%) and Friday the highest odds of a green close (about 54.6%) (see the table below).

These 'weekend effect' and 'Monday effect' anomalies were well documented in older data but are tiny, fractions of a percent, and have weakened or reversed in recent decades. After trading costs, they offer no practical edge for a long-term investor and are mostly of academic interest.

By quarter, the fourth is the champion: since 1930 Q4 has averaged about +3.1%, carried by strong Novembers and Decembers and the holiday rally (see the chart and table below). Q2 (about +2.3%) and Q1 (about +1.6%) sit in the middle.

The third quarter is the weakest, at roughly +1.1%, weighed down by September, the worst single month. The quarterly picture simply aggregates the monthly one: seasonal strength concentrates in late autumn and the softest stretch runs through late summer into early fall.

Seasonality also has a four-year rhythm tied to the US election calendar. In secondary compilations, the third year of a presidential term has historically been the strongest, with the S&P 500 up around 14-16% on average and positive in roughly 90% of third years since the 1930s, often explained by pre-election stimulus and policy clarity.

The second year has tended to be the weakest (an average near 3-4%), and the first and fourth years middling. The sample is small (fewer than 25 full cycles in the modern era), so the pattern is suggestive rather than dependable, and recent cycles have not always cooperated.

Two of the best-documented academic calendar anomalies are the turn-of-the-month and pre-holiday effects. Lakonishok and Smidt (1988) found that a four-day window (the last trading day of a month plus the first three of the next) captured essentially all of the DJIA's positive returns from 1897 to 1986 (SSRN).

Ariel (1987) similarly showed the market's gains historically concentrated in the first half of each month, and returns have tended to run positive on the trading day before major holidays. Proposed causes include pension-fund flows and payroll timing; like the other effects, they are real in the record but too small and inconsistent to trade after costs.

Seasonal edges tend to shrink once they are published and widely known, a hallmark of a semi-efficient market. The January small-cap effect and the Monday effect have both faded materially from their mid-century strength, and 'Sell in May' has been unreliable in recent decades, with several strong summers.

There is also a data-mining trap: test enough calendar windows and some will look significant by chance. The patterns that survive (broad autumn strength, September weakness, the Best Six Months tilt) are the ones grounded in real flows and repeated across long samples, but even those carry wide year-to-year variation.

The honest takeaway: seasonality is interesting context, not a timing strategy. The gaps between 'good' and 'bad' months are small next to the market's normal volatility, and trying to sit out weak stretches means paying taxes and transaction costs while risking missing the biggest up days, which do the heavy lifting for long-run returns.

For most investors the better move is to stay invested through the whole calendar and let the strong months compound rather than guess them. Use seasonality to set expectations (a red September is normal, not a crisis), not to jump in and out. A consistent, thesis-driven portfolio beats a calendar-driven one.