How AI Prompts Help Detect Market Shifts on Wall Street

This Week’s Focus: Spotting Market Signals in Rough Drafts

Markets rarely unfold in neat sequences. Each week feels like a rough draft: fragmented, noisy and open to interpretation. The challenge is to spot signals early, before they harden into consensus or headline news.

Consider two seemingly unrelated trends in 2025. European ETFs drew $207 billion in the first seven months of the year, up 60% from 2024. Meanwhile, Buffer ETFs have nearly doubled in number over the past two years and reached approximately 350 funds managing about $70 billion in assets.

Most would see separate data points. But here's what connects them: one signals global growth confidence, the other suggests domestic caution. Together, they reveal a fundamental split in investor psychology that often precedes major market shifts.

This is where AI prompts provide crucial structure. They surface these hidden connections, quantify turning points and flag risk imbalances consistently across markets. The aim isn't replacing judgment but rather sharpening it by transforming market noise into readable signals.

Think of prompts as reusable frameworks that systematically:

• Detect patterns across timeframes and asset classes;

• Quantify inflection points before consensus forms;

• Flag imbalances between perceived risk and opportunity;

• Adapt analysis to specific portfolio needs.

The real value lies in consistent signal detection that scales human judgment without replacing the critical thinking that separates successful investors from algorithms.

Here’re three specific prompt frameworks for detecting market shifts while they're forming and turning disconnected data into coherent early warnings.

Practical Examples: AI Prompts in Action 

AI prompts are structured instructions you give to a language model. By assigning a clear role, such as quantitative analyst, portfolio manager or risk manager, and by specifying the desired output (confidence levels, backtested data, concise summaries, tables or bullet points), prompts turn raw market data into forward-looking insight.

📌 Prompt 1: Institutional Bitcoin Holdings: Detecting Market Power Shifts Early

By 2025, institutions, ETFs, treasuries, even governments, control around 15% of Bitcoin supply. BlackRock’s iShares Bitcoin Trust (IBIT) alone has passed $80 billion in assets. This concentration creates both stability and systemic volatility.

“Track quarterly institutional Bitcoin holdings across ETFs, treasuries and regulatory filings. Compare growth against total circulating supply and retail demand. Flag inflection points where institutional ownership outpaces retail. Alert when institutional share crosses thresholds historically tied to liquidity or volatility shifts.”

The same framework applies wherever flows and concentration drive outcomes, from AI-themed ETFs to equity sector rotations.

📌 Prompt 2: Equity Market Breadth: Gauging Investor Risk Appetite

The Nasdaq 100 Index is up more than 11.8% year-to-date, while small-cap benchmarks, like the Russel 2000 hover near 5.0%. This divergence signals investor preference for mega-caps and tech names over potentiall riskier segments, a setup that can precede either a catch-up rally or a broader cooling.

“Track rolling 1-month, 3-month and 6-month return spreads between the Nasdaq 100, S&P 500, Russell 2000 and S&P 600. Highlight when spreads exceed 2 standard deviations above their historical averages. Cross-reference signals with the VIX term structure (spot vs. futures curve) and high-yield credit spreads.
If spreads widen while volatility curves invert and credit spreads rise, classify conditions as 'risk-off' and explain the implications for equity market breadth, portfolio hedging and sector allocation."

Instead of guessing outcomes, prompts structure breadth data to reveal pressure points and offer early insight into shifts in risk appetite.

📌 Prompt 3: Macro Signals — Bond Yields and Equity Risk Premium

In August 2025, U.S. 10-year Treasury yields rose above 4.2%, pushing the equity risk premium to decade lows. With bonds competing directly with equities for capital, valuation pressure becomes unavoidable.

"Calculate weekly equity risk premium: forward S&P 500 earnings yield minus 10-year Treasury yield. Compare current spread to 2-year and 5-year rolling averages. Flag periods when spread falls below 1 standard deviation, indicating compressed equity risk premiums. Track correlation with sector rotation patterns."

This turns bond and earnings data into a forward-looking signal. It is not a black-box forecast, but a more structured evaluation of valuation risk as conditions evolve.

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ETF Spotlight: JPMorgan Income ETF (JPIE) 

Continuing with the idea of reading markets early, the JPMorgan Income ETF (JPIE) shows how an income fund can also serve as a market signal. With a dividend yield near 6% and more than 2,000 holdings, JPIE may show how fixed income managers are positioning around credit risk, housing exposure and interest rate uncertainty.

Launched in 2021, JPIE is actively managed with a short duration of 2.35 years and a high turnover rate of 120%. Its net expense ratio of 0.39% is relatively competitive within the multisector bond category.

The ETF’s $4.6 billion portfolio includes:

• Agency & Non-Agency MBS: ~45% 

• CMBS: 16.7%

• ABS: 13.6%

• High-Yield Corporates: 12.5%

• Investment Grade Corporates: 2.8%

• EM Debt: 4.9%

• Cash/Other: ~5.6%

Roughly half the holdings are rated AA/AAA, while ~30% fall in BBB–B and 15% are below B or unrated. The mix shows caution on duration risk but conviction in mortgage-backed income streams, with selective high-yield exposure.

For investors, JPIE’s positioning can be read as a proxy for broader credit-market sentiment. Adjustments in its allocations provide early markers of how professionals interpret credit spreads, housing stability, and Fed policy shifts.

📌 AI Prompt Example for Further Exploration

“As a fixed-income strategist, review JPIE’s quarterly portfolio shifts in agency MBS, CMBS, ABS, high yield, and EM debt using SEC 13F filings, fund fact sheets, and Morningstar data. Highlight implications for Fed policy, mortgage credit risk and short-duration income demand. Compare JPIE’s yield with the U.S. Aggregate Bond Index (Bloomberg ticker: LBUSTRUU) to detect divergences signaling broader bond market adjustments.”

In Closing: Be the Early Reader of Market Stories

Markets will always be noisy and unfinished drafts. Structured prompting provides the scaffolding to interpret them systematically, before signals turn into consensus narratives. From institutional Bitcoin flows to ETF positioning, the edge lies not in predicting markets but in detecting when they are quietly rewriting themselves.

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

Until next time