February stock trading: What the charts show about historical patterns

Stocks & Translation: Seasonality in Market Returns - A Detailed Summary

Key Concepts:

• Seasonality: Recurring calendar patterns in market returns. Not a predictive tool, but a tendency for historical behavior.
• Median Return: The middle value in a dataset, used to avoid skewing results from extreme values.
• VIX: Volatility Index, often referred to as the “fear gauge,” measuring market expectations of volatility.
• Presidential Cycle: The four-year cycle of a US presidential term and its potential impact on market performance.
• Win Percentage: The percentage of years a particular month has shown positive returns.

I. Introduction to Seasonality & Its Limitations

Jared Blickery of Stocks & Translation introduces the concept of seasonality in stock market returns, emphasizing it’s a recurring pattern, not a definitive prediction. He stresses that seasonality should be used as contextual information alongside other analytical tools – technical analysis, fundamental analysis, and chart reading – and not as a standalone trading system. The focus is on understanding potential market headwinds and tailwinds, not pinpointing exact market tops and bottoms.

II. Historical S&P 500 Seasonality (1990-Present)

Analysis of the S&P 500 from 1990 to the present, using median monthly returns (to mitigate the impact of outliers), reveals the following:

• January: Typically up 1.7%, with a 58% win percentage (actual return for the current year was 1.4%).
• February: Typically up 0.8%, with a 58% win percentage.
• March, April, & May: Generally positive, with gains exceeding 1% each month. April and May boast win percentages above 70%.
• Summer & Early Fall: Relatively small gains, with July standing out at 1.8%.
• October – December: Strong bullish trend, with consistent gains.

Blickery notes that a win percentage of 70% or higher is generally preferred, as stocks tend to rise more often than fall.

III. Extended Historical Analysis (1928-Present – Day of Week Matching)

A second model examines data back to 1928, but with a specific filter: only years where the day of the week for a given date matches the corresponding day in 2026. This results in approximately 13 matching years per date. This analysis suggests:

• Trend Up to Late April: A general upward trend in returns through late April.
• Dip in May: A slight dip in returns during May.
• Rally to Early August: A strong rally into the beginning of August.
• Dip in Late September: Another dip in returns towards the end of September.
• Rally into Early December: A significant rally leading into the beginning of December.
• Sideways/Upward Trend to Year-End: A relatively stable or slightly upward trend towards the end of the year.

The speaker reiterates that these are tendencies, not guarantees.

IV. VIX Seasonality (1990-Present)

The analysis extends to the VIX (Volatility Index), the “fear gauge,” since 1990. Key observations include:

• Initial Volatility: The VIX typically starts the year around 19, currently lower at 15.
• Volatility Increase to Mid-March: Volatility tends to increase through mid-March.
• Summer Dip: Volatility generally decreases over the summer months.
• October/November Peak: October and November typically experience the highest VIX readings of the year – considered “prime time cash crash season.” Despite this, median returns for these months remain positive, but with significant outlier risk.
• Year-End Volatility Reduction: Volatility decreases towards the end of the year, coinciding with the typical year-end rally.

V. Interplay of Returns and Volatility

Blickery highlights the relationship between stock returns and the VIX. While October and November can see high volatility, they also often deliver positive returns, albeit with the potential for substantial losses (outliers). The reduction in volatility at year-end often accompanies the year-end rally.

VI. External Factors & Conclusion

The speaker acknowledges that unforeseen events, such as tariffs or other major news, can significantly impact market behavior and deviate from seasonal patterns. He concludes that seasonality is a “handy guide” for understanding potential market direction, but it’s not a foolproof predictor.

Notable Quote:

“Think of it as a way to set up expectations for market headwinds and tailwinds and not as a timing tool for exact tops and bottoms.” – Jared Blickery

Data & Statistics:

• S&P 500 Median Monthly Returns (1990-Present): Specific figures for each month are provided (see Section II).
• Win Percentages: Percentage of years with positive returns for each month (see Section II).
• VIX Median Values (1990-Present): Average VIX levels throughout the year are discussed (see Section IV).
• Historical Data Range: Analysis spans from 1928 to the present, with a primary focus on 1990-present.

Synthesis:

This analysis provides a detailed overview of historical seasonality in stock market returns, focusing on the S&P 500 and the VIX. While acknowledging the limitations of relying solely on seasonal patterns, the presentation offers valuable insights into potential market trends throughout the year. The emphasis on using seasonality as a contextual tool alongside other analytical methods underscores a prudent approach to investment strategy. The inclusion of both long-term (1928) and more recent (1990) data, along with the use of median returns, enhances the robustness of the findings.