How AI Stock Research Works: From Public Filings to a Sourced Report

The short answer

Between “analyze this ticker” and a finished report, rigorous AI stock research runs four steps in order. First, it gathers the public record: regulatory filings, earnings call transcripts, market data, and dated reporting. Second, it grounds every material claim in a cited source, cutting or flagging anything it cannot trace. Third, it structures the analysis into a fixed set of sections, so the same questions get asked of every company. Fourth, it validates the finished report — completeness, internal consistency, sourcing — before anything reaches the reader.

The important thing to understand is that the discipline is architectural. A language model on its own will produce fluent, confident analysis whether or not the underlying facts hold up. What makes the output research rather than prose is the structure built around the model: what it is required to read, what it is required to cite, what sections it must complete, and what checks it must pass. Each step below describes what that structure should do, regardless of which platform implements it. For what AI stock research is as a category — and the standards a reader should demand of it — see the pillar guide to AI stock research. This page covers the process.

Step 1: Gathering the public record

The input to AI stock research is public information: annual and quarterly filings, earnings call transcripts, press releases, market and price data, and dated news reporting. Nothing proprietary, nothing private — the same documents any investor could read, which is exactly the point. Most investors never read them, because the volume is punishing. A single company can produce hundreds of pages of filings a year, plus four earnings calls, plus a steady stream of disclosures about insider transactions, share issuance, and legal proceedings.

Reading breadth is AI’s genuine edge in this step. A model can work through years of filings and every recent transcript without fatigue, without skimming the risk-factor section because it is boring, and without anchoring on whichever document it read first. The gathering step should be broad by design: not just the latest quarter’s headline numbers, but the balance sheet history, the dilution record, management’s past statements against subsequent results, and the news timeline with dates attached. Errors of omission at this stage propagate through everything downstream, so the standard is coverage — pull the whole record, then let the later steps decide what matters.

Step 2: Grounding claims in sources

Grounding is the anti-hallucination discipline, and it is what separates a research workflow from asking a chatbot. Language models can generate plausible-sounding financial figures that do not exist — a fabricated revenue number reads exactly as fluently as a real one. The defense is not hoping the model is careful; it is requiring claim-level citations, meaning every material factual claim in the report traces to a specific source a reader can check.

Grounding also means a source hierarchy. Not all documents deserve equal weight: audited filings outrank company press releases, press releases outrank analyst commentary, and commentary outranks social sentiment. When sources conflict — a management claim on an earnings call versus what the filed numbers show — the hierarchy decides which one anchors the analysis, and the conflict itself becomes a finding worth reporting.

The operational rule is simple: an unsourced claim is cut or flagged, not kept. If a statement cannot be traced to the public record, it does not get to sit in the report dressed as a fact. This rule costs something — it removes confident-sounding material a chatbot would happily include — and that cost is the price of a report a reader can verify rather than merely believe.

Step 3: Structuring the analysis

Left to itself, a model will write about whatever the company’s narrative makes salient — the exciting product, the big partnership — and quietly skip what the narrative omits. A fixed section structure prevents this by asking the same questions of every stock in the same order: How does the business actually make money? What do the valuation scenarios look like, and with what probabilities? What are the risks? What, specifically, would break the thesis?

Fixed structure does two jobs. First, completeness: nothing material gets skipped because the ticker was glamorous or the filing was tedious. A structure that mandates a dilution review will surface share issuance whether or not the company’s story mentions it. Second, comparability: when every report answers the same questions in the same order, a reader can hold two companies side by side and compare like with like, instead of comparing one company’s highlight reel to another’s risk disclosure.

The structure should force falsifiability in particular. An analysis worth reading states the conditions under which it would be wrong — its kill criteria — before reaching a conclusion. As a concrete illustration from an example analysis of NVIDIA (NVDA): the thesis treats the company as a system-level AI factory supplier driven by earnings revisions, and its stated kill criteria include hyperscaler capex plans rolling over for two consecutive quarters, gross margin structurally falling below 68% without a clear mix-transition explanation, and competitor accelerators taking enough share to flatten data center growth. Each is checkable against future public evidence. That is structure doing the work: the thesis-breakers exist because a section demanded them, not because the model volunteered them.

Step 4: Validation before delivery

The final step is a set of independent checks on the finished report, run before it reaches the reader. Validation asks questions the drafting process cannot be trusted to answer about itself: Is every required section present and substantive? Do the numbers agree with each other — do the valuation scenario probabilities behave like probabilities, does the verdict match the analysis beneath it? Is the report about the company that was asked for? Are the claims sourced?

The property that matters most is what happens on failure. A report that fails validation is not delivered — it is not trimmed, softened, or shipped with a caveat. Delivered-worse is the failure mode of systems that treat output as the goal; not-delivered is the behavior of systems that treat verified output as the goal. For the reader, this converts an invisible quality question (“did anyone check this?”) into a structural guarantee: if the report arrived, it passed.

Validation is also where honest framing gets enforced. A finished report should carry its own context — the date the analysis was run and the price it was run against — because research is a snapshot of the public record at a moment in time, and a report that hides its age is quietly misleading.

What this process cannot do

The workflow above raises the floor of research quality. It does not manufacture certainty, and its limits should be stated plainly.

• No private information. The input is the public record. Anything a company has not disclosed — deteriorating demand not yet in the numbers, a deal not yet announced — is invisible to the process.
• No prediction. The output is analysis of what is known, organized into scenarios. Valuation scenarios with probabilities are structured judgments about ranges of outcomes, not forecasts of what will happen.
• Errors are inherited from sources. If a filing is misstated or a transcript is wrong, a faithfully sourced report reproduces the error. Grounding guarantees traceability, not truth.
• Staleness is real. Research is dated the moment it is finished. Material news after the analysis date is not in the report, which is why the “as of” date matters.
• Probabilities are judgments. A 55% base case is a disciplined estimate, not a measured frequency. Two rigorous processes can weigh the same evidence differently.

Finally, the process produces research, not advice. It cannot know a reader’s financial situation, time horizon, or risk tolerance, and what the reader does with a report remains their decision.

How Monsaic implements this workflow

Monsaic is an AI stock research platform that implements the four-step workflow as a fixed discipline. Every Monsaic report follows the same fifteen-section structure — from executive summary and fundamental analysis through dilution and revenue quality — so the same questions are asked of every ticker. Every material claim carries claim-level citations, and each report ships with its source list, an “as of” analysis date, and price-as-of context. Valuation is expressed as four scenarios — bull, base, bear, and tail — each with a price target, a probability, what must be true, and what breaks it. Kill criteria are defined before the grade, so the verdict is falsifiable by construction, and forensic grades (A+ through F) cover management and governance, legal and regulatory exposure, dilution, and revenue quality, alongside a 1–10 narrative-versus-substance score. The report’s overall verdict carries a stated conviction level, so the bottom line survives even a fast read. The full research discipline, section by section, is documented in the Monsaic methodology.

FAQ

See what the process produces

The pipeline this page describes ends in something you can read. A covered stock’s excerpt shows what filings, transcripts, and market data become once the sourcing discipline is applied — verdict first, with the condition that would break it stated up front.

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Keep reading

• AI stock research, done rigorously — the Monsaic pillar
• The Monsaic methodology
• How to evaluate AI-generated stock reports
• Monsaic vs. ChatGPT for stock research
• Generate your first Monsaic