AI can make equity research faster, but only if the evidence trail survives the summary.
The useful version is not "tell me whether this ticker is good." That turns the model into a confident narrator with no receipts.
The useful version is closer to: show me what the filings say, what changed from the last period, what management claims, what crowd discussion is reacting to, and what is still unknown.
This is research education only, not financial advice.
The source trail rule
For any company note, I want every important claim to carry six things:
- Source type: 10-K, 10-Q, 8-K, S-1, earnings release, transcript, news article, or company page
- Filing or publication date
- Exact excerpt or table row
- Why it matters
- Confidence level
- What is still unknown
If a model cannot cite the source, it should stop instead of sounding useful.
A 10 minute first-pass workflow
Start with the business model. Pull the company description, segment note, and latest risk factors. Write one plain-English paragraph on what the business actually does.
Then compare the latest filing against the prior comparable period. Look at revenue, gross margin, operating margin, cash flow, debt, customer concentration, and share count.
Next, check liquidity and dilution context. Cash, debt maturities, operating cash flow, going concern language, shelf registrations, ATM programs, warrants, convertibles, and recent financings all belong in the first-pass note.
After that, read management's own warnings. Risk factors are full of boilerplate, but changes in risk language can be useful. Mark each risk as generic, company-specific, or newly intensified.
Then separate facts from narrative. Put verified filing-backed facts in one column and management claims in another.
Finally, summarize news, Reddit, and StockTwits as attention context only. Crowd chatter can explain why people are looking at a ticker, but it is not evidence of business quality.
Prompts that force better answers
Company snapshot:
Create a source-aware company snapshot from the provided filings and source excerpts.
Include the business model, revenue streams, customer or segment concentration if disclosed, key recent change, five citations, unknowns, and areas needing human review.
Do not make price targets, buy or sell recommendations, or return predictions.
If evidence is missing, write "not established from provided sources."
Filing comparison:
Compare the latest filing against the prior comparable filing.
Focus on revenue, margins, cash, debt, operating cash flow, share count, segment performance, risk language, legal issues, regulatory issues, and customer changes.
For every item, include the exact source excerpt or table row and why a researcher should care.
Crowd sanity check:
Summarize recent Reddit, StockTwits, and news discussion as attention context only.
List the claims that require filing verification, obvious rumors or unsupported statements, and what should be checked in primary sources before trusting the discussion.
Do not recommend trades.
Red flags
A research note needs human review if it:
- Cites a filing but cannot quote the excerpt
- Mixes quarterly and annual numbers without labeling them
- Treats adjusted EBITDA as cash flow
- Ignores share count changes
- Uses social sentiment as investment evidence
- Summarizes a risk factor without checking whether it changed
- Uses news as proof when the primary filing says something narrower
Why I care about this
I have been working on Tomorrow Terminal, a source-aware stock research workflow that combines filings, financials, market data, news, Reddit, and StockTwits into one company profile.
The bigger point is simple: the AI paragraph is not the product. The product is the evidence trail, the refusals, the dates, the excerpts, and the parts that still need a human.
Research only. Not financial advice.
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