Sector-specific metrics

Definition

Each sector’s economics make certain metrics load-bearing and standard ones misleading: a bank has no EBITDA, a SaaS company’s GAAP earnings understate a subscription machine, an E&P’s book value is a commodity-price derivative. Sector metrics are the dialect layer on top of ratio analysis — the platform maintains them so cross-sector screens don’t compare apples to interest-rate spreads.

How it works / structure

• Financials: net interest margin (NIM), efficiency ratio, book value and P/TBV (the native multiple), credit metrics (NPLs, reserve coverage, charge-offs), capital ratios (CET1 — the regulatory constraint); EV multiples are meaningless (deposits are the business, not debt).
• Software/SaaS: ARR and its growth, net revenue retention (NRR — expansion inside the installed base), gross margin (hosting vs license economics), Rule of 40 (growth + FCF margin — a practitioner convention, labeled as such), CAC payback.
• Energy (E&P): production growth, reserve replacement, finding & development costs, breakeven prices, PV-10 — all commodity-price-conditional (instrument-energy-futures linkage).
• Retail: same-store/comparable sales (the organic-growth line), inventory turns, sales per square foot; REITs: FFO/ AFFO (depreciation distorts GAAP earnings on property), cap rates, occupancy.
• Engine form: per-sector metric sets with definitions pinned to filed data where available (many sector metrics are company-defined non-GAAP — ARR definitions differ across issuers; the platform stores each issuer’s definition alongside the number).

When it applies

Any single-name fundamental work outside industrial-standard economics (fa-ratio-analysis hands off here); sector- relative screens (fa-multiples-comparables comps discipline requires native multiples); sector-rotation fundamentals (strategy-sector-rotation earnings context).

Risk profile & failure modes

• Non-GAAP definitional drift: issuer-defined metrics (ARR, adjusted FFO) can be redefined between quarters — trend breaks may be definitional, not operational; the stored-definition rule exists for this.
• Convention mistaken for evidence: Rule-of-40-style heuristics are practitioner conventions without peer- reviewed support — useful shorthand, labeled folklore- adjacent, never load-bearing.
• Cycle-position blindness: sector metrics have cycle norms (credit costs trough before recessions; E&P economics at strip prices vs spot) — levels without cycle context mislead.
• Cross-sector leakage: screening the whole market on any single sector’s dialect produces systematic nonsense at the boundaries.

Evidence & limits

Reporting requirements per industry are SEC-documented; metric conventions are practitioner-standard (Damodaran’s sector valuation chapters are the exposition cited). Return evidence attaches to the underlying phenomena (quality, value, revisions) covered in their entries — sector metrics are measurement dialect, and the platform claims no edge from the dialect itself.

Falsifiable-thesis examples

Illustrations only, not signals:

• “X (SaaS) will report NRR above 110% in each of the next four quarters (durable-expansion thesis)” — falsified by any print below.
• “Bank Y’s NIM will expand at least 15bp within two quarters of the next policy-rate increase (asset-sensitivity thesis)” — falsified by the conditional pair.

Cross-references

• The general layer it specializes: fa-ratio-analysis, fa-multiples-comparables
• Filed-data grounding: fa-financial-statements
• Macro linkages per sector: regime-rate-environments (banks), instrument-energy-futures (E&P), strategy-sector-rotation