FastDCF

Forecast Foundation

Base Forecast

The model starts with quarterly net income history.

For a historical valuation date, it uses the information that would have been available at that time. The selected forecast model then turns that history into the base forward path.

Forecast Models

The default model is exponentially weighted linear regression. It uses the full available history but gives newer quarters more weight than older ones.

Linear regression uses the configured regression window length. Robust weighted regression keeps the weighting idea while reducing the effect of outliers. Damped Holt trend uses a smoothed level-and-trend forecast instead of a regression line.

Simulation and Valuation

Bootstrap Simulation

Historical deviations from the chosen base forecast are resampled into many randomized bootstrap paths.

This turns one deterministic forecast into a distribution of possible future net-income paths instead of a single line. Both the explicit forecast horizon and the number of bootstrap simulations are user-adjustable in the dashboard settings.

Terminal Value

FastDCF can add a terminal continuation value after the explicit forecast horizon. When enabled, it takes the last 4 forecast quarters as the terminal annual earnings base and applies a Gordon-growth continuation with a fixed long-run growth assumption.

The terminal-value toggle is a user setting, so you can keep the same forecast model and discount-rate setup while choosing whether the valuation stops at the explicit horizon or includes a continuation beyond it.

Discounting and Floor

Each simulated path is projected forward, discounted quarter by quarter, and combined with the starting equity base.

The configured equity floor is applied to each final common-equity path value, not to each quarter of projected income. That lets negative income paths remain visible while full wipeout paths contribute the floor value, which is zero by default.

Discounted Expected Income

The model also averages the discounted projected income paths before the final path-level equity floor is applied.

That average is the discounted expected future-income contribution shown in the diagnostics and used in the estimated-price composition split.

Estimated Fair Price

The discounted future-income value is combined with the latest balance-sheet snapshot, where the starting equity base is selected assets minus selected liabilities.

Dividing the resulting equity value by shares outstanding produces the estimated fair price per share. The dashboard breakdown popover shows how much of that estimate comes from the starting equity base versus discounted future incomes.

Market Inputs and Historical Mode

Discount Rate

The automatic discount rate is usually derived from a WACC-style pipeline that combines market beta, capital structure, and borrowing-cost estimates. The calculator also supports a financial-statement-only build-up path when configured that way.

If you manually override the discount rate in the dashboard, the model keeps the same fundamentals and forecast settings and only reruns the valuation with the new discounting assumption.

Market Price Inputs

The market-price comparison depends on market price mode. Latest close uses the most recently available close in the app. Live price uses live market quotes when that mode is enabled for your role.

If a comparison price is unavailable, the app can still return the DCF estimate and forecast, but market-dependent fields such as Potential and Implied Disc. remain unavailable.

Historical Valuations

When you set a past valuation date, FastDCF reruns the same valuation pipeline using only the financial rows that would have been known by that point in time.

The market comparison then uses the stored close on or before that date when it exists, so the retrospective view is not just today's model relabeled with an old date.

Shares Outstanding

The final equity value is converted to a per-share estimate using the selected shares outstanding denominator.

That same denominator is also used in the estimated-price breakdown so the per-share split stays aligned with the displayed estimate.

Why Net Income

Why not free cash flow?

Standard DCF work usually uses free cash flow. FastDCF uses net income as a long-run proxy for cash earning power because it is available consistently across the dataset and, over long horizons, earnings and cash generation can be close enough to support comparative valuation.

What tradeoff does that create?

This is a practical simplification, not a claim that net income is universally better than free cash flow.

No single metric is perfect: free cash flow can swing sharply for reasons that do not always reflect the underlying business trend, while net income can be distorted by non-cash accounting items such as impairments.

When is the estimate less reliable?

The model treats net income as a scalable long-term proxy rather than a literal replacement for cash-flow analysis.

The estimate is more defensible when net income and free cash flow are not structurally diverging, and less reliable for businesses with unusual accounting, heavy reinvestment needs, or persistent gaps between earnings and cash generation.