Effective Prompts For Investment Research

INTRODUCTION

Hey, it's Matt. Welcome to AI Street Markets Edition—where I use AI tools for investment analysis.

Thanks for being here!

Each Sunday, we'll explore how Large Language Models can help with investment analysis—though I should note upfront that I'm learning alongside you. And for simplicity’s sake, when I say “AI,” I’m referring to Large Language Models.

The technology is not, at least currently, as good as a seasoned investment analyst, but it now gives investors the ability to examine more companies and test more hypotheses.

My conversation with former JPMorgan AI Exec Tucker Balch sums it up best:

Understanding AI's Limitations

While AI tools might seem to 'think' like analysts, they're really just pattern-matching machines working with probabilities. Here’s a breakdown on why they’re not “reasoning.”

This creates a challenge: When AI analyzes recent information not in its original training, it's more likely to make mistakes or "hallucinate" false connections.

This can't be completely eliminated, as financial markets produce new information every day. One way to mitigate this is with specific prompts.

Effective Prompts

LLMs have exploded in the two years since OpenAI released ChatGPT in part because it’s so easy to use. You don’t need any coding experience.

But you can improve outputs from LLMs with effective guidance or prompts.

For example, this is pretty vague:

Review Jefferies’ most recent financial performance.

This type of prompt is more likely to give you a broad overview of the bank’s fourth-quarter earnings, which reported earnings last week.

You’re more likely to get a precise response by providing clear instructions and defining the following:

Effective Prompts Structure

• Role 

  • Be specific about the expertise you want the AI to adopt. Instead of just saying "analyze this," tell it to act as an equity analyst, credit analyst, or another relevant expert. This helps frame the analysis appropriately.

• Task

  • Break down exactly what you want analyzed, including:

    • Time period (specific quarter/year)

    • Metrics to focus on

    • Types of comparisons (YoY, sequential, vs peers)

    • Specific aspects of the business to examine

• Output 

  • Specify how you want the information presented:

    • Format (bullet points vs paragraphs)

    • Order of information

    • Level of detail

    • Whether to include specific quotes

    • Types of metrics to highlight

    • How to handle forward-looking statements

So this structured approach typically produces more reliable and useful analysis. Going back to Jefferies:

Role: You are an equity analyst specializing in investment banking, covering Jefferies.

Task: Review Jefferies' most recent quarter (Q4 2024) investment banking performance:

1. Highlight key Q4 2024 metrics:

• Investment banking revenue and QoQ change

• Revenue mix (M&A vs Capital Markets)

• Specific deal statistics mentioned by management

• Any notable changes in client activity or deal types

2. Provide relevant context:

• YoY comparison with Q4 2023

• Key trends over past 2-3 quarters

• Market share gains/losses explicitly mentioned

• Any changes in competitive positioning vs bulge bracket banks

3. Extract management's forward-looking commentary ONLY from Q4 call:

• Pipeline comments with specific metrics if provided

• Any guidance on deal timing or conversion

• Client activity trends

• Areas of strategic focus

Format: Start with Q4 performance, followed by YoY/sequential trends, then management outlook. Only include metrics and commentary explicitly stated in earnings calls. Quote management directly when discussing outlook.

For this prompt, I used Bigdata.com, in part because I was able to get some hands-on training a couple weeks ago. (Thanks Dan!)

I clicked on “@earningscalls” to reference the most recent quarter.

I got the following result:

I’ve added the text below for readability.

In the screenshot, you’ll see blue footnotes that you can click to view the source of the information.

It’s already underlined — no need to hunt for it. Given the risk of hallucinations, the data needs to be auditable.

Key Takeaways from Today's Edition

• AI tools can help scale analysis but require verification

• Effective prompting is crucial—being specific about role, task, and desired output dramatically improves results

I hope this was helpful. I have access to the tools below and I’m getting up to speed on how best to use them. (And access to more platforms in the coming weeks.)

• Aiera

• Bigdata.com

• DoTadda

• Hudson Labs

• Marvin Labs

I want to make this as interactive as possible. Feel free to reply to this email with your thoughts and suggestions.

See you next week!

-Matt