Market Seasonality
Seasonality in the stock market, particularly in broad indices like the S&P 500, reflects recurring behavioral and macroeconomic patterns that influence investor sentiment and capital flows throughout the year. These patterns are statistical tendencies, not guarantees, shaped by decades of data and investor psychology. Traders may use seasonality to time entries and exits, but it is most effective when combined with broader macro and technical analysis to inform strategy. Below illustrates the S&P's performance, demonstrated with the SPY ETL, since 2000 with positive values in green and negative in red.
Monthly Trends: SPDR S&P 500 ETF Trust
September's historical underperformance, averaging -1.67% and positive less than half the time, is often attributed to post-summer portfolio rebalancing, tax-loss harvesting, and a lack of major earnings catalysts. In contrast, November tends to be the strongest month, with an average gain of 2.32% and a 76% win rate in the same time period, likely driven by institutional year-end positioning, holiday optimism, and early anticipation of Q4 earnings. Shown below are the average gains per month since 2000 as tracked by the SPY ETF along with an indicator detailing the monthly win rate.
Key Concepts:
- Certain months tend to favor bulls: November through April historically show stronger performance in equities.
- Summer often brings lower volatility: The “sell in May and go away” adage reflects seasonal slowdowns in trading activity.
- End-of-quarter window dressing can drive short-term rallies: Fund managers may buy outperformers to polish portfolios.
- Earnings season creates predictable volatility spikes: Swing traders can time entries around pre- and post-earnings trends.
- Seasonality pertains to more than just markets. Certain stocks have proven performance record over specific months of the year.
- Commodities follow seasonal supply cycles: Agriculture, energy, and metals often move with harvests, weather, and demand shifts.
AImy Simplifies Seasonal Analysis
AImy elevates seasonality analysis by uncovering recurring monthly and quarterly performance patterns across both broad market indices and individual equities. She identifies statistically significant seasonal trends and overlays them with price behavior to highlight optimal entry and exit windows. Each seasonality signal is backed by decade of performance data to ensure statistical relevance. By automating the detection of calendar-based tendencies and filtering out noise from anomalous years, AImy enables traders to anticipate market rhythm with precision. Whether you're aligning trades with high-probability seasonal setups, avoiding historically weak periods, or integrating seasonality into broader strategy models, AImy transforms raw historical data into actionable, time-sensitive insights.
Monthly Trends: SPDR S&P 500 ETF Trust
September's historical underperformance, averaging -1.67% and positive less than half the time, is often attributed to post-summer portfolio rebalancing, tax-loss harvesting, and a lack of major earnings catalysts. In contrast, November tends to be the strongest month, with an average gain of 2.32% and a 76% win rate in the same time period, likely driven by institutional year-end positioning, holiday optimism, and early anticipation of Q4 earnings. Shown below are the average gains per month since 2000 as tracked by the SPY ETF along with an indicator detailing the monthly win rate.
Key Concepts:
Key Concepts:
- Certain months tend to favor bulls: November through April historically show stronger performance in equities.
- Summer often brings lower volatility: The “sell in May and go away” adage reflects seasonal slowdowns in trading activity.
- End-of-quarter window dressing can drive short-term rallies: Fund managers may buy outperformers to polish portfolios.
- Earnings season creates predictable volatility spikes: Swing traders can time entries around pre- and post-earnings trends.
- Seasonality pertains to more than just markets. Certain stocks have proven performance record over specific months of the year.
- Commodities follow seasonal supply cycles: Agriculture, energy, and metals often move with harvests, weather, and demand shifts.
Next Lesson: Quarterly Earnings Announcements
AImy Simplifies Seasonal Analysis
AImy elevates seasonality analysis by uncovering recurring monthly and quarterly performance patterns across both broad market indices and individual equities. She identifies statistically significant seasonal trends and overlays them with price behavior to highlight optimal entry and exit windows. Each seasonality signal is backed by decade of performance data to ensure statistical relevance. By automating the detection of calendar-based tendencies and filtering out noise from anomalous years, AImy enables traders to anticipate market rhythm with precision. Whether you're aligning trades with high-probability seasonal setups, avoiding historically weak periods, or integrating seasonality into broader strategy models, AImy transforms raw historical data into actionable, time-sensitive insights.