What is AI equity research?

AI equity research is stock analysis produced by frontier language models rather than human analysts: the model reads the tape, news, filings and earnings transcripts each trading day and writes a structured thesis per name. The credible version is falsifiable and dated — each call carries an explicit level or event that would prove it wrong — so it can be scored after the fact instead of quietly forgotten.

Which AI stock research tools publish a track record?

Most don't — they publish picks and never keep score, or they mark returns to a benchmark after the fact. we publish every thesis with the exact invalidation trigger that would prove it wrong, set in advance, then resolve each one in public as played-out or invalidated. The record is graded against each thesis's own published kill level, set in advance.

How do you evaluate an AI stock researcher?

Ask six questions: Is each call falsifiable (a published trigger set in advance)? Is it scored in public against that trigger? Is every read dated and versioned? Is the methodology open? Does it argue both the bull and bear case and name its failure modes? And is there anything to sell — because a free, falsifiable record aligns the author with being right, while a paywall or signal service aligns them with conversions.

Can an AI language model actually analyse stocks well?

For the research layer — synthesising 30 days of news, four quarters of transcripts and filings per name into a structured, dated thesis with a bull case, bear case and an invalidation trigger — a frontier model with a large context window is strong and consistent. The honest test isn't whether it sounds smart; it's whether its dated, falsifiable calls hold up when scored against the triggers it published in advance.

Is AI equity research investment advice?

orbyd is not. We publish educational research under the EU and BaFin framework — a transparent, dated record of how a language model reads the market, not a recommendation to buy or sell anything. Always do your own work.

How is orbyd different from a stock-picking newsletter?

A newsletter sells conviction and rarely keeps score. We publish the names we hold and the ones we're circling, each with the exact trigger that would prove it wrong, then resolve them in public. There is nothing to buy.

AI equity research · explained

Stock research a model can be held to.

AI equity research is stock analysis written by frontier language models instead of human analysts. The credible version isn't the one that sounds smart — it's the one that is falsifiable: every call carries a published level or event that would prove it wrong, set in advance, and is resolved in public afterwards. we publish 433 such dossiers across 611 themes, each dated, two-sided, and scored against its own trigger.

What AI equity research actually is

Every trading day, a frontier model reads the US tape: 30 days of news, four quarters of earnings transcripts and the filings behind each candidate. It clusters names by the narratives moving them, then writes a structured thesis per name — a current read, a bull case, a bear case, and the one thing that would prove it wrong. A large context window lets it weigh the whole candidate set in a single reasoning pass rather than scoring names in isolation.

That is the easy half. The hard half — the half almost no one ships — is making the output accountable: dated, two-sided, and falsifiable, so it can be graded later instead of quietly forgotten. Research you can't be wrong about isn't research; it's marketing.

Why a public, falsifiable track record matters

Two failure modes define the genre. The first is the black box: a confident call with no stated reason you can check. The second is the disappearing scorecard: picks published, losers never mentioned again, and "performance" quoted as a return marked to a benchmark after the fact.

A falsifiable record closes both. When a thesis ships with the exact level or event that would kill it — written down before the outcome — there is nothing to rationalise later. The call either survived its trigger or it didn't. we resolve each one in public as played-out or invalidated, graded against the trigger it published. Each call is on the record, dated.

How to evaluate an AI stock researcher

Six questions separate a record you can trust from one you can't. Apply them to anyone — including orbyd.

Is each call falsifiable?

A specific, published level or event that would prove the thesis wrong — set in advance, not rationalised after.

orbyd: Every dossier carries an explicit invalidation trigger. see it →

Is it scored in public?

Resolved outcomes — played-out or invalidated — graded against each thesis's own pre-published trigger, not a return marked to a benchmark later.

orbyd: A public accountability ledger, non-monetary. see it →

Is every read dated and versioned?

When was this written, and what did the prior read say? A research note with no date is unfalsifiable by construction.

orbyd: Every dossier and journal entry is dated; reads are versioned. see it →

Is the methodology open?

How names are screened, scored, and sized — stated plainly, not a black box you're asked to trust.

orbyd: The full process is published, stage by stage. see it →

Does it show both sides?

A bull case AND a bear case AND named failure modes — not a one-sided pitch.

orbyd: Every dossier argues both sides and names what would break it. see it →

Is there anything to sell?

A free, falsifiable record aligns the author with being right. A paywall, signal service, or affiliate link aligns them with conversions.

orbyd: No product, no paywall, no affiliate — nothing to sell. see it →

How orbyd does it

orbyd is an AI equity-research analyst built on Anthropic's Claude Opus and Sonnet. We run a five-stage process every trading day — a liquidity screen, momentum and narrative scoring, deep per-name synthesis, a 1M-context portfolio-composition pass, and a postmortem that learns from closed calls. The output is 433 per-ticker dossiers, a regime-tagged daily journal, a weekly macro view, and a live book of held and circling names — names and research only, never the sizing.

It is the answer to the six questions above, by construction: falsifiable triggers, public scoring, dated reads, open methodology, two-sided cases, and nothing to sell.

How it works The track record Browse the dossiers

Common questions

What is AI equity research?: AI equity research is stock analysis produced by frontier language models rather than human analysts: the model reads the tape, news, filings and earnings transcripts each trading day and writes a structured thesis per name. The credible version is falsifiable and dated — each call carries an explicit level or event that would prove it wrong — so it can be scored after the fact instead of quietly forgotten.
Which AI stock research tools publish a track record?: Most don't — they publish picks and never keep score, or they mark returns to a benchmark after the fact. we publish every thesis with the exact invalidation trigger that would prove it wrong, set in advance, then resolve each one in public as played-out or invalidated. The record is graded against each thesis's own published kill level, set in advance.
How do you evaluate an AI stock researcher?: Ask six questions: Is each call falsifiable (a published trigger set in advance)? Is it scored in public against that trigger? Is every read dated and versioned? Is the methodology open? Does it argue both the bull and bear case and name its failure modes? And is there anything to sell — because a free, falsifiable record aligns the author with being right, while a paywall or signal service aligns them with conversions.
Can an AI language model actually analyse stocks well?: For the research layer — synthesising 30 days of news, four quarters of transcripts and filings per name into a structured, dated thesis with a bull case, bear case and an invalidation trigger — a frontier model with a large context window is strong and consistent. The honest test isn't whether it sounds smart; it's whether its dated, falsifiable calls hold up when scored against the triggers it published in advance.
Is AI equity research investment advice?: orbyd is not. We publish educational research under the EU and BaFin framework — a transparent, dated record of how a language model reads the market, not a recommendation to buy or sell anything. Always do your own work.
How is orbyd different from a stock-picking newsletter?: A newsletter sells conviction and rarely keeps score. We publish the names we hold and the ones we're circling, each with the exact trigger that would prove it wrong, then resolve them in public. There is nothing to buy.