Comparable Company Valuation

Use trading multiples with consistent peer selection and metric definitions.

D3 · Financial Modeling

Comparable Company Valuation

Use trading multiples with consistent peer selection and metric definitions.

Architecture Inputs Formula logic Worked example Controls Common errors Q&A

Model architecture

Define peer set.
Normalize financial metrics.
Calculate enterprise and equity multiples.
Apply range to target metrics.
Bridge to equity value.

A professional model should make the decision logic visible. Inputs belong in a controlled assumption area; calculations should be formula-driven; outputs should state units, dates and scenarios; checks should be obvious and actionable.

Core inputs

Share price and diluted shares
Net debt and other claims
LTM/NTM revenue, EBITDA, EBIT, earnings
Peer business and geography
Growth and margin

Evidence rule: historical figures should reconcile to approved financial statements or management data. Forecast assumptions should show an owner, date, source and rationale.

Formula logic

Relationship	Use
`Enterprise value = Equity value + Net debt + Other claims − Non-operating investments`	Model formula / relationship
`EV/EBITDA = Enterprise value ÷ EBITDA`	Model formula / relationship

Use the formulas as design relationships, not as substitutes for the accounting policy, contract definition or transaction facts relevant to the model.

Practical example

A target with ₹30 crore EBITDA valued at 12x implies ₹360 crore enterprise value. Subtract ₹60 crore net debt to obtain ₹300 crore equity value before other adjustments.

How to implement

Load the historical base and reconcile it.
Put assumptions in dedicated cells.
Build the schedule from operational drivers.
Link outputs to financial statements and dashboards.
Run base, upside and downside checks.

Control checks

Same period metrics across peers
Negative or distorted multiples flagged
Lease accounting treated consistently
Dilution reflected
Peer dispersion explained

Finin2min crux: the model is credible only when a reviewer can trace a conclusion to evidence, assumptions and formula logic without guessing.

Common modeling errors

Mixing current market cap with old financial metrics
Ignoring cyclicality
Using median without reviewing outliers
Comparing asset-light and capital-heavy businesses
Applying equity multiples to enterprise metrics

Practical Q&A

Should the model contain all possible detail?

No. It should contain enough detail to answer the decision question and explain material risks. Excess detail can hide the drivers.

Should a formula ever contain a hardcoded number?

Only for constants that are genuinely universal or immaterial. Business assumptions should be linked to visible input cells.

What is the minimum review standard?

Reconcile historical data, test key formulas independently, scan for hardcodes and errors, verify scenario switches, and review outputs under downside assumptions.

Source framework: ICAI Ind AS resources, notified accounting standards, Schedule III presentation principles, transaction documents and approved management data. The linked workbook templates are educational starting points, not valuation opinions.