Seasonality and Cycle Studies
Seasonality identifies recurring patterns in market returns tied to calendar periods. The S&P 500 has historically returned +7.1% from November through April versus +1.8% from May through October (1950-2023). This isn't random—it reflects institutional behavior patterns, tax-related flows, and earnings calendar effects. The practical question isn't whether seasonality exists (it does), but whether the patterns remain strong enough to trade after everyone knows about them.
What Seasonality Measures (Why This Matters)
Seasonality studies identify return patterns that repeat across calendar periods:
- Monthly return tendencies
- Six-month "favorable" and "unfavorable" windows
- Multi-year cycles (presidential, decennial)
- Options expiration effects
The practical chain: Historical pattern → Statistical tendency → Probability tilt (not guarantee) → Conditional trading edge
Seasonality provides context, not certainty. A strategy that works 65% of the time still fails 35% of the time. The value is in combining seasonal tendencies with other factors, not trading them blindly.
Sell in May: The Data Behind the Strategy
The "Sell in May and Go Away" pattern divides the year into two six-month periods:
November through April (Favorable Period) May through October (Unfavorable Period)
Historical Performance (S&P 500, 1950-2023)
| Period | Average Return | Positive Years | Maximum Drawdown |
|---|---|---|---|
| Nov-Apr | +7.1% | 77% | -23.8% (2008-09) |
| May-Oct | +1.8% | 64% | -30.1% (2008) |
| Full Year | +9.0% | 73% | -38.5% (2008) |
Source: Stock Trader's Almanac historical analysis.
The calculation behind the difference:
If you invested $10,000 in 1950 and held only during November-April (switching to cash May-October):
Ending value (Nov-Apr only): approximately $1.2 million
If you held only during May-October:
Ending value (May-Oct only): approximately $25,000
The durable lesson: The favorable period captured the vast majority of long-term returns, but the unfavorable period was still positive on average. The strategy's edge comes from avoiding the worst months, not from the unfavorable period being negative.
Why the Pattern May Exist
- Mutual fund flows: Year-end bonuses and retirement contributions flow into markets in Q1
- Earnings calendar: Q1 and Q4 earnings seasons tend to show stronger growth revisions
- Institutional calendar: Portfolio managers reduce risk before summer vacations
- Tax-loss harvesting: Year-end selling creates January buying opportunities
The January Effect: Small-Cap Outperformance
The January Effect describes the tendency for small-cap stocks to outperform large-caps in January.
Historical Data (1926-2023)
| Metric | Small-Cap January | Large-Cap January | Difference |
|---|---|---|---|
| Average Return | +5.4% | +1.3% | +4.1% |
| Positive Januaries | 72% | 62% | — |
| Effect Strongest | First 5 trading days | — | — |
Source: Dimensional Fund Advisors size premium research.
Worked Example: January Effect Trade
You allocate $50,000 to a small-cap index ETF (IWM) on December 31st and sell on January 31st.
Historical expectation (based on averages):
- Expected return: approximately +5.4%
- Expected dollar gain: $50,000 × 5.4% = $2,700
Reality check:
- The effect has weakened since becoming widely known
- 2000s and 2010s showed diminished January Effect
- Tax-loss harvesting timing has shifted due to awareness
The practical point: Historical averages tell you what happened, not what will happen. The January Effect's weakening illustrates how published anomalies often fade.
Presidential Cycle: The Four-Year Pattern
Stock market returns have historically varied based on presidential term years.
Historical Returns by Year (S&P 500, 1950-2023)
| Term Year | Average Return | Positive Years |
|---|---|---|
| Year 1 (Post-Election) | +6.5% | 63% |
| Year 2 (Midterm) | +4.5% | 61% |
| Year 3 (Pre-Election) | +16.8% | 88% |
| Year 4 (Election Year) | +7.3% | 71% |
Source: Stock Trader's Almanac presidential cycle analysis.
Key observation: Year 3 (pre-election year) has been the strongest performer, with only 3 negative years since 1950.
Why the Pattern May Exist
- Policy calendar: Administrations front-load painful policies in years 1-2 and stimulate in year 3
- Fed coordination: Monetary policy historically loosened approaching elections
- Uncertainty resolution: Mid-term elections reduce policy uncertainty
The test: Does this pattern represent exploitable alpha or data mining? The small sample size (fewer than 20 full cycles since 1950) makes statistical significance questionable.
Monthly Return Patterns: The Calendar Effect
Individual months show distinct return tendencies.
S&P 500 Monthly Returns (1950-2023)
| Month | Average Return | Positive Months | Ranking |
|---|---|---|---|
| January | +1.2% | 62% | 6th |
| February | +0.1% | 53% | 10th |
| March | +1.1% | 66% | 7th |
| April | +1.5% | 71% | 2nd |
| May | +0.2% | 57% | 9th |
| June | +0.1% | 52% | 11th |
| July | +1.2% | 60% | 5th |
| August | -0.1% | 52% | 12th |
| September | -0.5% | 45% | Last |
| October | +0.8% | 59% | 8th |
| November | +1.6% | 68% | 1st |
| December | +1.3% | 74% | 4th |
Pattern highlights:
- September is the worst-performing month historically
- November-December-January cluster shows consistent strength
- August-September-October shows the weakest stretch
Options Expiration Week Effects
The third Friday of each month (options expiration) creates measurable effects.
Expiration Week Tendencies
| Period | Observation |
|---|---|
| Week before expiration | Increased volatility, pinning toward max pain levels |
| Expiration week | Often bullish bias as hedges unwind |
| Week after expiration | Frequently shows reversals of expiration-week moves |
Triple/Quadruple Witching (March, June, September, December): Stock index futures, stock index options, stock options, and single-stock futures expire simultaneously.
- Volume typically spikes 30-50% above average
- The final hour of expiration Friday sees 2-3x normal volume
- Direction is unpredictable; magnitude is elevated
Limitations and Risks (Why Seasonality Fails)
Seasonality-based strategies have specific weaknesses:
-
Sample size concerns: 73 years of data means approximately 18 presidential cycles—not enough for statistical confidence.
-
Regime changes: The pattern from 1950-1980 may not apply to 2000-2024 markets with algorithmic trading and globalization.
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Arbitrage decay: Once a pattern becomes known, traders exploit it, reducing or eliminating the edge. The January Effect has weakened significantly since its 1980s discovery.
-
Outlier dominance: A few extreme years can dominate averages. The 2008 financial crisis affected every seasonal pattern for years.
-
Opportunity cost: Sitting in cash during "unfavorable" periods means missing rallies. May-October 2020 returned +18.6%—missing it was costly.
-
Transaction costs and taxes: Frequent trading based on monthly patterns generates costs that may exceed the edge.
Worked Example: When Seasonality Failed
2020 "Sell in May" scenario:
If you sold the S&P 500 on April 30, 2020 at 2,912 and bought back on October 31, 2020:
- Buy-back price: 3,270
- Missed gain: +12.3%
Following the historical pattern would have cost you significant returns because the post-COVID rally dominated seasonal tendencies.
The point is: Seasonality describes averages. Any single year can deviate dramatically from the average.
How to Use Seasonality Responsibly
Seasonality works best as:
- Context, not trigger: Combine with technical signals (breadth, momentum) rather than trading on calendar alone
- Risk adjustment: Reduce position sizes during historically weak months; increase during strong months
- Confirmation filter: A bullish technical setup in November carries higher odds than the same setup in September
- Watchlist timing: Look for entries during seasonally weak periods for better prices
Seasonality fails when used as:
- Standalone system: Calendar-only timing ignores current market conditions
- Rigid rule: "Always sell on May 1st" ignores the year's unique characteristics
- Short-term trading: Monthly effects are too noisy for tactical trading
Next Steps
- Review the current seasonal window—are we in the favorable (Nov-Apr) or unfavorable (May-Oct) period?
- Check the presidential cycle year—year 3 historically shows the strongest returns; year 2 the weakest
- Note upcoming options expiration dates—triple witching months (March, June, September, December) show elevated volatility
- Combine with current technicals—bullish seasonal periods with confirmed uptrends historically outperform either factor alone
- Track your own observations—document whether seasonal patterns hold in real-time to build personal conviction (or skepticism)
Related: Market Breadth Indicators to Watch | Combining Indicators Without Double Counting Signals | How Technical Signals Tie into Macro Context