AI Prompts for Surfacing Market Intelligence on Wall Street

Welcome back to AI in Investment Research & Finance.

In previous editions, we highlighted how fund analysts and strategists use large language models (LLMs) to turn raw data into sharper content and use AI prompting to decode market trends, from value strategies and macro shifts to cross-asset signals. We’ve also explored exchange-traded fund (ETF) positioning, featuring funds such as MOAT, SPLV, JEPI, GLD, BITO, EDOG, AIQ, QGRW, OMAH, QAI, FLOT, IVV, as well as thematic comparisons like SLV vs SIL.

This time, we turn to the growing role of unstructured text data in financial research. From earnings call transcripts and app reviews to social media posts and headlines, these sources often contain early signals of risk, sentiment and strategic positioning. They are typically signals that rarely make it into spreadsheets.

Table of Contents

• This week’s focus: Unstructured data meets market research

• Five prompts for smarter financial text analysis

  • Message from Our Partner: I Hate it Here

    • AI Career Moves: Exciting AI Jobs This Week

    • AI in Markets: Key Trends & Upcoming Events

Recent studies show that social media platforms can influence short-term stock movements. Influential online figures, or finfluencers, can amplify crowd sentiment and contribute to herding behavior. But social media is just one piece. Language patterns in earnings calls, spikes in product complaints or sudden shifts in media tone may offer equally valuable clues, if you know where to look.

Why Text Data Matters More Than Ever

Academic studies have found strong links between social media sentiment and stock market activity. For instance, stocks with high social media coverage in one month often see elevated return volatility and trading volume in the next.

At the same time, the volume of financial text data has grown rapidly:

• Over 40,000 corporate transcripts annually

• Millions of customer reviews

• Real-time posts from verified financial accounts

• Continuous company-related news coverage

Much of this information goes underused due to its scale and lack of structure. Strategic prompting offers a practical way to interpret this content more efficiently and more consistently, than manual review.

That said, here are five Natural Language Processing (NLP) prompt structures designed to help analysts extract insight from unstructured financial content systematically and at scale.

Five Practical Prompts

Think of these as building blocks for integrating NLP into your market intelligence toolkit. Each prompt targets a common use case where traditional data sources fall short. You could also consider assigning a role (such as marketing manager) and specifying an output format (such as bullet points, table, etc).

📌 Task 1: Brand Sentiment Monitoring

Context: Track consumer sentiment changes that might affect revenue

Analyze these 5,000 customer tweets about [COMPANY] [as attached]. Identify the top 3 complaint themes. For each theme: (1) Show frequency (2) Provide two representative examples, (3) Assess severity on 1-10 scale. Focus on complaints that could impact sales negatively in the next quarter.

What you're looking for: A clear separation between minor feedback and issues with potential revenue impact.

📌 Task 2: Risk Signal Detection from Financial News

Context: Monitor for early warning signs in financial institutions

Review these 30 headlines about [BANK NAME][as attached] from the past 90 days. Flag any language suggesting liquidity stress, deposit flight or regulatory intervention. Rank the headlines by urgency and briefly explain your reasoning. Summarize key risk terms mentioned (e.g., ‘withdrawals,’ ‘downgrade,’ ‘bailout’) and assess the overall sentiment trajectory over time.

What you're looking for: Clear identification of risk-related terms, context-aware interpretation of headlines and a concise assessment of sentiment trends over time.

📌 Task 3: Competitive Intelligence

Context: Track competitor activities systematically

Extract strategic announcements from online mentions of [COMPETITOR] published in reputable outlets such as financial news sites (e.g., Bloomberg, Reuters), official company press releases, regulatory filings and leading industry trade press over the past 60 days. Classify each under headings, such as: Product Launch, Leadership Change, M&A, Partnership, etc. Include source credibility score and potential market impact.

What you're looking for: Accurate entity recognition and ability to distinguish between verified news and speculation.

📌 Task 4: Customer Feedback Analysis

Context: Connect user sentiment to potential business impact

Analyze the 1,000 most recent reviews for [FINTECH APP] [as attached].

For each review, identify and cluster complaint themes signalling potential churn or lower engagement. Quantify the frequency of each theme, compare trends versus the prior period [as attached] and highlight any increases. Estimate the potential revenue impact based on changes in complaint patterns, citing assumptions and methodology.

Present findings in a table: Theme, Frequency (current), Change vs. Prior, Estimated Revenue Impact (with rationale) and Example Comments.

Use proven analytical methods for theme extraction and revenue estimation. Include a brief summary of key risks for product and investment decision-makers.

What you're looking for: Links between specific customer issues and measurable business outcomes.

📌 Task 5: Management Communication Analysis

Context: Track changes in leadership tone and confidence

Compare CEO and CFO language across the last four quarterly earnings calls for [COMPANY] [found in the following links]. Identify shifts toward defensive language, uncertainty or reduced forward-looking confidence. Highlight specific phrases related to product/service demand, margins or regulatory risk. Summarize whether the upper management tone has become more cautious, stable or optimistic over time.

What you're looking for: Recognition of subtle linguistic changes that might indicate shifting business conditions.

Implementation Notes

Try these prompts with your own datasets to evaluate fit for your workflows. Monitor for accuracy, actionability and time savings compared to manual review.

In practice, the most effective results come from combining domain expertise with clear, systematic prompting, rather than relying on model complexity alone.

Have feedback or ideas for future editions? Let us know.

AI Career Moves: Exciting AI Jobs This Week

Looking for your next opportunity in AI? Explore these standout roles across industries and locations:

📩 Feel free to share this list with anyone looking for AI opportunities!

AI in Markets: Key Trends & Upcoming Events

🌏  World Summit AI – Amsterdam, October 8–9
The annual World Summit AI will take place at Taets Art & Event Park in Amsterdam. The 2025 program includes sessions on frontier AI, human-AI collaboration and responsible development.

🌏 AI Expo Europe – Bucharest, November 1–2
AI Expo Europe will take place at the Radisson Blu Hotel in Bucharest, with sessions on applied AI, company showcases and networking events. The conference focuses on developments in AI across Eastern Europe.

🤖 Microsoft research maps AI’s impact across occupations
A new study analyzes Copilot usage across roles to measure where generative AI is most applicable. Jobs involving writing and analysis show high overlap, while roles requiring physical presence or emotional judgment remain less affected.

🤖 Meta forms new AI lab focused on self-improving models
Meta has launched Meta Superintelligence Labs to build AI that improves with minimal input. The focus is on “personal superintelligence” to support users, alongside new hires and smartglasses development.

Stay tuned for next week’s edition, where we’ll explore new AI prompts for deeper sector analysis. To ensure our next newsletter lands in your Inbox, please add our email address [email protected] to your contacts.