← Finance

DCF Calculator

Estimate enterprise value from forecast FCF and discount rate assumptions. Compare terminal-value cases with a built-in sensitivity table.

Other languages 日本語 | English | Español | Français

Inputs

FCF rows (Year 1..N)

Bulk paste (CSV/TSV)

Advanced settings

Summary

Yearly detail

Year FCF Discount factor PV

Sensitivity (r × g)

FAQ

Which assumptions move DCF the most?

Discount rate and terminal value assumptions typically drive the largest changes. Use TV ratio and the sensitivity table to inspect impact.

Must terminal growth be lower than discount rate?

Yes in the Gordon growth model. If growth is equal to or above discount rate, the formula is not valid.

What does exit multiple apply to here?

In this version, exit multiple is applied to the final forecast-year FCF.

Can I get equity value and value per share?

Yes. Enter net debt and shares outstanding to calculate equity value and implied value per share.

What should I do first on this page?

Start with the minimum required inputs or the first action shown near the primary button. Keep optional settings at defaults for a baseline run, then change one setting at a time so you can explain what caused each output change.

How to use DCF Calculator effectively

What this calculator does

This page is for estimating outcomes by changing inputs in one controlled workflow. The model keeps your focus on variables, not output shape. Start with stable assumptions, then test sensitivity by changing one key input at a time to observe directional impact.

Input meaning and unit policy

Each input has an expected unit and a typical range. For reliable interpretation, check whether you are using the same unit system, period, and base assumptions across all runs. Unit mismatch is the most common source of unexpected drift in numeric results.

Use-case sequence

A practical sequence is: first run with defaults, then create a baseline log, then run one alternative scenario, and finally compare only the changed output metric. This sequence reduces cognitive load and prevents false pattern recognition in early experiments.

Common mistakes to avoid

Avoid changing too many variables at once, mixing incompatible data sources, and interpreting a one-time output without checking robustness. A single contradictory input can flip conclusions, so keep each experiment minimal and document assumptions as part of your note.

Interpretation guidance

Review both magnitude and direction. Direction tells you whether a strategy moves outcomes in the desired direction, while magnitude helps you judge practicality. If both agree, you can proceed; if not, rebuild the baseline and verify constraints before deciding.