Eqr Idea Generation

When to use

Use this when you need new names to work on — a screen came back, a theme is emerging, a peer moved, or the pipeline is thin — and you want to convert raw candidates into a ranked shortlist worth real diligence. The job is to find where you might have a differentiated view, not to confirm what is already consensus.

Not for: deep diligence on a name you've already chosen (use eqr-initiating-coverage), tracking a live position (use eqr-thesis-tracker), or building numbers (use eqr-model-update). This fills the funnel; it does not underwrite. Outputs are research and education, not personalized investment advice.

Method

1. Choose the source. Quant screen, catalyst-driven, thematic, spin-off/special-situation, anomaly (insider buying, guidance/estimate divergence, unusual price action), or peer read-across. Decision point: name the source so the bias of each is explicit.
2. Cast wide, then filter for a variant view. For each candidate ask: where could my view differ from consensus (numbers, narrative, or time horizon)? Discard names where you'd just be agreeing with the tape.
3. Do the 15-minute triage. Business quality, why it might be mispriced, obvious red flags (accounting, leverage, governance, liquidity), and whether it's investable (size, borrow, restrictions).
4. Write a one-line variant thesis per survivor: the specific thing you believe that the market does not.
5. Attach a kill test — the single fastest check that would disprove the idea. Cheap disconfirmation first.
6. Rank by edge × feasibility. Edge = size of variant view. Feasibility = how knowable it is with the time you have.
7. Assign next action. Kill, park (with a trigger), or promote to full diligence.

Example

Estimate-revision screen surfaces an industrial with rising forward EPS but a flat stock. Triage: quality decent, possible mispricing = market anchored to an old cyclical narrative; red flags none material; investable. Variant thesis: "Aftermarket mix shift lifts through-cycle margins 200bps that consensus still models as peak." Kill test: check whether aftermarket revenue is actually growing faster than OE in the last two 10-Qs. Edge high, feasibility high → promote. A second name fails its kill test in ten minutes (mix not shifting) → kill.

Pitfalls

• Consensus in disguise. Generating ideas that just restate what's priced in — no variant view, no edge.
• Falling in love pre-diligence. Skipping the kill test because the story is exciting. Disconfirm cheaply first.
• Screen worship. Treating a screen output as an idea; it's only a candidate until triaged.
• Ignoring investability. A brilliant thesis on an untradeable microcap or hard-to-borrow name is not an idea you can use.
• No ranking. A flat list of 40 names stalls; force edge × feasibility and assign an action.

Output format

IDEA GENERATION | source: <screen/catalyst/theme/anomaly/read-across> | as of <date>
CANDIDATE | ticker | business (1 line) | why mispriced | red flags | investable?
VARIANT THESIS: <the thing the market doesn't believe>
KILL TEST: <fastest disconfirming check>
RANK: edge 1-10 x feasibility 1-10
ACTION: kill / park (trigger: …) / promote to diligence
SHORTLIST (promoted): 1) … 2) … 3) …
Note: research/education, not personalized investment advice.

Reference

Research process: name the idea source so its bias is explicit, cast wide, and filter for a differentiated variant view rather than confirmation of consensus. Triage each candidate for business quality, the mispricing mechanism, red flags (accounting, leverage, governance, liquidity), and investability, then write a one-line variant thesis and the cheapest disconfirming kill test. Modeling: promoted names go to eqr-initiating-coverage for diligence and eqr-model-update for numbers; this stage does not underwrite. Disclosure: idea generation is preliminary research and education, screens and triage are not a recommendation, and nothing here is personalized investment advice.