Blog/Markets
AI-Assisted Comparable Company Screens
·~15 min read
Using LLMs to accelerate peer selection and footnote reconciliation—while keeping your comps defensible and version-controlled.
Judgment vs housekeeping
Comparable-company work is half judgment and half housekeeping: normalizing adjustments, reconciling fiscal periods, catching restatements, and defending why a peer belongs in the set. Associates spend hours on housekeeping that does not improve the thesis—it delays it. LLMs can accelerate the mechanical layer when guided by explicit rules.
Peer selection with guardrails
AI can propose peers from business descriptions, segment mix, and geography—but proposals are not approvals. House rules matter: minimum liquidity, ADR treatment, conglomerate splits, and "no terminal peers" lists for sectors with broken multiples. Encode those rules in a data dictionary the model must cite when suggesting names.
Normalization & adjustments
EBITDA adjustments, stock-based compensation treatment, and lease accounting differences destroy comps when handled inconsistently. Automation should flag outliers—margins that imply impossible unit economics, revenue recognition footnotes that break comparability—not silently smooth them to fit a median.
Footnote reconciliation
The highest-value LLM task in comps is often footnote triage: surfacing non-recurring items, segment reclassifications, and guidance language that changes run-rate earnings. Pair extraction with human approval and page-level citations so IC materials stay defensible.
Firm data dictionary
Speed without semantic drift requires a shared vocabulary: how you define enterprise value, which multiples are primary, how you treat net debt and minorities. Without that dictionary, every analyst's comp sheet is a dialect—and automation amplifies the chaos.
Versioning for IC defense
The control is simple: machines propose peers and adjustments; humans approve; comps stay versioned for IC defense. When a partner asks why Company X left the set in v3, you show the diff—not a shrug about "the model thought it was irrelevant."
Where LLMs stop
LLMs should not invent synergies, fabricate consensus estimates, or override house policy on outlier removal. They accelerate evidence gathering; sponsors and analysts still own the valuation range and the story in the room.
Closing thought
AI-assisted comps win when they are faster and more consistent—peer sets and adjustments that a new team member can audit without a oral tradition lecture.
© 2026 QuantRidge. Educational content; not tax or investment advice.