June 28, 2026 · 15 min read
Do certain months consistently beat others in the stock market? We analyzed 75 years of S&P 500 data to separate seasonal signal from noise — and explain why knowing the patterns still will not make you rich.
Eight numbers that frame the entire seasonal investing debate.
Stock market seasonality refers to recurring patterns in market returns that correlate with specific times of the year — certain months, quarters, days of the week, or calendar events. These are not random coincidences: with S&P 500 data stretching back to 1950, the patterns have statistical significance.
But statistical significance is not the same as investability. A pattern can be real in historical data yet not reliable enough to profitably trade on, especially after transaction costs, taxes, and the risk of being wrong in any given year.
The core tension: these drivers are real, but because they are well-known, markets partially price them in. Every time a seasonal pattern becomes popular, traders front-run it, weakening the signal — a classic example of the efficient market hypothesis at work.
Here is the core data. These are average monthly total returns of the S&P 500, including dividends, over a 75-year span. The pattern is surprisingly consistent — November and April lead, September is the only negative month.
| Month | Avg Return | Visualization | Key Driver |
|---|---|---|---|
| January | +1.2% | January effect, new year inflows | |
| February | -0.1% | Post-January giveback, short month | |
| March | +1.0% | Quarter-end rebalancing, window dressing | |
| April | +1.5% | Tax refund investing, strong earnings | |
| May | +0.2% | Start of weaker seasonal period | |
| June | +0.1% | Summer doldrums begin | |
| July | +1.1% | Mid-year recovery, earnings season | |
| August | -0.2% | Vacation volume drop, late-summer weakness | |
| September | -0.5% | Worst month — fund fiscal years, budget debates | |
| October | +0.9% | Recovery month, historically volatile | |
| November | +1.6% | Best month — holiday rally, election years | |
| December | +1.5% | Santa Claus rally, window dressing |
The spread is meaningful: November averages +1.6% while September averages -0.5%. That is a 2.1 percentage point gap — but in any individual year, September can be the best month and November can be the worst. Averages mask enormous variation.
The most famous seasonal adage in investing has roots in the old British saying, “Sell in May and go away, do not come back till St Leger Day” — referring to a September horse race. The idea: exit stocks in May, return in November, and avoid the weaker summer months.
| Period | Avg 6-Month Return | Risk Profile | Strategy Says |
|---|---|---|---|
| May - October (Summer) | +2.1% | Higher drawdowns | Sell in May says: be out |
| November - April (Winter) | +7.1% | Lower volatility | Sell in May says: be in |
| Full Year Buy & Hold | +9.8% | Market exposure all year | History's winner after costs |
Yes, November-through-April returns are about 3.4x higher than May-through-October. But this does not mean selling in May is smart.
A buy-and-hold investor who stayed invested from 1950 to 2025 earned substantially more than a “Sell in May” investor, even before accounting for taxes and transaction costs. The seasonal pattern is real but not exploitable after costs.
The January Effect is one of the oldest documented market anomalies: small-cap stocks tend to outperform large caps in January, often significantly. The pattern was first identified in academic research in the 1970s.
Largely, yes. Research shows the January Effect was strongest from 1925-1980. After it became widely known and published in academic papers, institutional traders began front-running the pattern — buying small caps in December and selling in January. This arbitrage has significantly weakened the effect.
From 2000-2025, the Russell 2000 outperformed the S&P 500 in January only about 52% of the time — barely better than a coin flip. The January Effect is a textbook case of a well-known anomaly being arbitraged away.
September is the only month with a negative average return on the S&P 500 since 1950. At -0.5% on average, it stands alone as the calendar’s black sheep. Major September crashes include the 2001 post-9/11 sell-off, the 2008 financial crisis peak, and the 2022 bear market low.
September weakness is also an opportunity. If you practice dollar-cost averaging, September dips let you buy more shares at lower prices. Some of the best Q4 rallies start from September lows — the 2023 and 2024 Septembers were both positive, defying the historical average.
Yale Hirsch, creator of the Stock Trader’s Almanac, defined the Santa Claus Rally as the last five trading days of December plus the first two trading days of January. Over this seven-day window, the S&P 500 has averaged a gain of about +1.3% since 1950.
Hirsch famously noted: “If Santa Claus should fail to call, bears may come to Broad and Wall.” When the Santa Claus Rally fails (negative returns during this window), the following year has historically been below average. It is treated by some as a sentiment indicator — not a trading signal, but a warning sign.
The four-year presidential election cycle is one of the most studied seasonal patterns in finance. The data is remarkably consistent: pre-election years (year 3) are the strongest, with average S&P 500 returns of +16.4%, as incumbents stimulate the economy ahead of elections.
| Cycle Year | Avg S&P 500 Return | Pattern / Notes |
|---|---|---|
| Midterm Year (Year 2) | +6.0% | Uncertainty peaks, then markets rally into midterms |
| Pre-Election Year (Year 3) | +16.4% | Strongest year — politicians stimulate economy |
| Election Year (Year 4) | +7.5% | Uncertainty but generally positive; status quo bias |
| Post-Election Year (Year 1) | +6.5% | New admin settling in, policy uncertainty — 2026 is here |
2026 is a post-election year (Year 1 of the presidential cycle). Historically, post-election years are the second-weakest in the cycle, averaging +6.5%. New administrations often implement less market-friendly policies early in their term, saving stimulus for later when re-election pressure builds. However, recent post-election years (2017, 2021) significantly outperformed this average.
When you aggregate months into quarters, the seasonal story becomes clearer: Q4 is the powerhouse, Q3 is the laggard.
| Quarter | Avg Return | Character | Relative Strength |
|---|---|---|---|
| Q1 (Jan-Mar) | +2.1% | Tax refund flows, new year allocation | Moderate |
| Q2 (Apr-Jun) | +1.8% | Earnings season strength fading into summer | Moderate |
| Q3 (Jul-Sep) | +0.4% | Summer weakness, September drag | Weakest |
| Q4 (Oct-Dec) | +4.0% | Holiday rally, year-end positioning, earnings | Strongest |
Q4 alone accounts for roughly 40% of the average annual return. This is driven by earnings season optimism, holiday consumer spending data, year-end institutional buying, and the Santa Claus Rally. Q3 is weak primarily because of September drag and lower summer trading volumes.
Quarterly earnings seasons (mid-January, mid-April, mid-July, mid-October) create regular volatility spikes. The VIX typically rises 1-2 points in the two weeks before major tech earnings, then contracts sharply after results. Options traders can use this predictable vol expansion and contraction — but directional traders find it adds noise, not signal.
The “Monday Effect” (or “Weekend Effect”) was one of the first market anomalies documented in academic finance: Monday returns were historically negative while Friday returns were positive.
Mostly no. Research by Robins and Smith (2016) showed the Monday Effect largely disappeared after 2000. Algorithmic trading, extended-hours markets, and global 24-hour news cycles have eliminated most of the information asymmetry that caused it. If you are making investment decisions based on the day of the week, you are optimizing for something that no longer exists.
If seasonal patterns are so well-documented, why can’t you just trade them and get rich? Several structural forces work against you.
If a simple rule like “buy in November, sell in May” consistently generated risk-adjusted alpha, hedge funds would exploit it until the edge disappeared. The fact that seasonal patterns persist in weakened form suggests they are real enough to measure but not strong enough to trade profitably after costs — the market is efficient enough to erode the easy money.
Seasonality should not drive your investment decisions — but it can inform when and how you execute decisions you have already made. Here is the practical playbook.
If you are dollar-cost averaging monthly into an index fund, consider slightly front-loading contributions in historically weak months (August-September) and continuing normally in strong months. You are not timing the market — you are investing the same total amount but tilting the schedule to buy more during average dips.
September and October weakness creates natural tax-loss harvesting opportunities. If positions are underwater in September, harvesting losses before the Q4 rally lets you lock in tax benefits while potentially re-entering at lower prices (after the wash sale window).
Earnings seasons create predictable volatility expansions. Options sellers can time premium-selling strategies around these vol spikes — selling puts before earnings (high premium) or covered calls during low-vol summer months (lower premium but consistent income).
If you rebalance your portfolio annually, doing it in October (after September weakness) lets you buy beaten-down assets and sell winners from the year — aligning your rebalancing with the natural seasonal rhythm rather than the arbitrary January 1 date.
How has 2026 tracked against historical seasonal patterns? Here is the January-through-June scorecard.
| Month | 2026 Actual | Historical Avg | vs. History |
|---|---|---|---|
| January 2026 | +2.1% | +1.2% | Above avg |
| February 2026 | -1.8% | -0.1% | Below avg |
| March 2026 | +0.5% | +1.0% | Below avg |
| April 2026 | +2.3% | +1.5% | Above avg |
| May 2026 | +0.8% | +0.2% | Above avg |
| June 2026 | +1.2% | +0.1% | Above avg |
2026 has been a mixed bag for seasonal conformity. January and April came in above historical averages, while February underperformed significantly. The “Sell in May” crowd would have missed a solid May and June. As a post-election year, 2026 is tracking slightly above the +6.5% historical average for Year 1 of the presidential cycle — consistent with the trend of recent post-election years outperforming the long-run average.
The key lesson: seasonal patterns describe tendencies, not certainties. Any given year can deviate wildly from the historical mean, and 2026 is proving that once again.
Stock market seasonality is real in the data. November really is the best month on average. September really is the worst. The Santa Claus Rally occurs about 79% of the time. The presidential election cycle shows remarkably consistent patterns over decades.
But knowing the patterns is not the same as profiting from them. Transaction costs, taxes, the risk of being wrong in any given year, and the self-defeating nature of widely known anomalies all conspire against seasonal trading strategies. Every backtest looks profitable; real-world execution erodes the edge.
The most productive use of seasonal knowledge is not timing the market — it is informing how you execute a strategy you already have. Tilt your DCA contributions toward historically weak months. Schedule your rebalancing for October instead of January. Harvest tax losses in September. Sell options premium during earnings volatility spikes.
Seasonality is real in the data but not reliable enough to trade on. The best time to invest is when you have the money — and the best strategy is to keep investing consistently, regardless of what month it is.
If you are trying to predict stock prices, seasonal patterns are one small piece of a much larger puzzle. Combine them with fundamental analysis, an understanding of growth vs value dynamics, and a disciplined dollar-cost averaging approach for the best long-term results.
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