AI Meets Market Seasonality On Wall Street: Enhancing Traditional Patterns with Modern Tools

AI in Action: Practical Frameworks for Seasonal Analysis 

AI models help shift raw data into better structured, decision-ready insight. Today we share three AI prompts that can help turn seasonality into actionable strategies. For stronger results, consider assigning roles (quant analyst, risk manager, portfolio strategist) and defining outputs (tables, guidelines, confidence levels). Also ask AI to provide credible sources (citations).

Here are three examples:

📌 Prompt 1: Enhanced Strategy Development

Analyze seasonal trends in the US consumer discretionary sector from 2015 to 2025. Identify months that consistently show strength or weakness. Then use momentum indicators (such as RSI, MACD or Stochastic Oscillator) to help pinpoint the best times in September 2025 to buy in or sell out. Present results in a table showing average monthly returns and volatility and suggest simple entry and exit rules. End with one practical tip US retail investors can use to improve their timing when rotating in or out of the sector. Support all findings, data and recommendations with clear, up-to-date citations.

📌 Prompt 2: Alternative Data Integration

Analyze US stock market seasonality since January 2019 by examining (1) SPY options volume and open interest around monthly expirations and (2) trading volume spikes in SPY during the last three trading days of each quarter. Note any patterns that become stronger or weaker during months with Federal Open Market Committee (FOMC) meetings where interest rate decisions are announced. Present results in a simple table showing each event, its typical short-term effect on the S&P 500 index, and how often it occurs (e.g., “number of months or quarters out of total analyzed and the corresponding percentage”). Finish with one straightforward timing tip any SPY investor can use and support all findings with up-to-date citations.

Prompt 3: AI Agent Briefing

Note: This prompt positions the AI as a strategy agent for quick daily briefings.

You are an AI-powered strategy agent providing a daily briefing on the S&P 500 index (or [insert asset/sector]). After the close of t​oday's US regular trading day, compare today’s market moves with established seasonality patterns (e.g., September–October weakness, November–April strength). Summarize your analysis in three sections:

Alignment with seasonal trends: Is today’s move consistent with historical seasonality?

Current risk signals: Highlight notable market risks or catalysts observed today (e.g., changes in volatility, major macro events, sentiment shifts).

Tactical action points: List 2–3 clear, practical next steps or considerations as bullet points.

Keep the entire summary under 250 words. Support your points with specific data or credible references where available.

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ETF Spotlight: Pacer CFRA-Stovall Equal Weight Seasonal Rotation ETF Strategy (SZNE)

Quick Overview:

• Price: $34.83

• 52-Week Range: $30.02 – $39.55

• Dividend Yield: 1.10%

• Year-to-Date Performance: -5.04%

The Pacer CFRA-Stovall Equal Weight Seasonal Rotation ETF (SZNE) demonstrates how systematic seasonal approaches can work using traditional methods. The fund rotates semi-annually between six sectors of the S&P 500 Equal Weight Index, allocating to cyclical sectors from November through April and shifting to defensive areas from May through October. At present, the portfolio reflects its defensive autumn stance, with roughly half in health care and half in consumer staples.

SZNE holds 100 securities, with leading positions in Estee Lauder (EL), Archer-Daniels-Midland (ADM), Dollar Tree (DLTR) and PepsiCo (PEP). It manages $15.6 million in assets and carries an expense ratio of 0.60%.

Let’s now see how AI prompts can help investors analyze the SZNE fund.

📌 AI Prompt to Analyze SZNE’s Portfolio Return Enhancement:

Act as a portfolio analyst. Using publicly available monthly and annual return data from January 2019 to December 2024, analyze the following ETFs: 
- Pacer CFRA-Stovall Equal Weight Seasonal Rotation ETF (SZNE),
- Invesco S&P 500 Equal Weight ETF (RSP),
- SPDR S&P 500 ETF Trust (SPY).

For this period, complete these tasks: 
- Compare monthly returns for September and October each year across all three ETFs. 
- Determine if holding both SZNE and SPY together would have produced higher returns or reduced losses in September and October in any individual year. 
- Compare the annual returns for all three ETFs. 
- Recommend whether SZNE is suited to long-term investing or short-term trading, and explain which types of investors may benefit most. 

For each key finding, use clear numbers or direct comparisons, and always support your results with credible references or up-to-date citations.

This prompt directs the AI to perform a comparative, evidence-based analysis of key ETFs, quantifying their returns during specific periods,

Moving Forward

The goal for today’s newsletter has been to enhance known seasonal patterns with AI tools that can process information at scales quite difficult with traditional analysis tools. As markets become increasingly complex, combining the wisdom of historical patterns with AI's analytical capabilities offers a path toward more precise seasonal positioning.

Thank you for reading AI in Investment Research & Finance. Here’s to spotting tomorrow’s market stories today.  

Until next time