FastDCF

The platform estimates a company value from historical financial data and projected future income.

Use it to compare the model estimate with the market price and review the main forecast and chart outputs.

The estimated price is the model's per-share valuation for the selected company and settings.

It is a valuation output, not a promise, target, or trade signal.

The forecast chart shows historical and projected net income.

The weekly price chart shows market-price history and, when requested, a retrospective valuation line.

The forecast is based on recent quarterly financial history and the current model settings.

This includes the selected discount rate and the active regression window length.

A ticker is the short stock-market symbol used to identify a company.

Examples include AAPL, MSFT, and NVDA.

Enter a ticker symbol, optionally set a valuation date, and run the calculation.

The result includes an estimated price, forecast, and chart view for that company.

The valuation date runs the model as if it were executed on that historical date.

The model uses the financial data available at that time and compares the result with the market price around that date. Reset returns the view to today.

The discount rate controls how strongly future projected income is reduced when converted into present value.

Higher rates usually lower the estimate. Lower rates usually raise it. In most cases the automatic rate is based on WACC-style inputs.

The forecast shows a high-level projection of future net income based on recent quarters.

It includes a central forecast path and discounted expected income values based on that projection.

The shaded bands show an uncertainty range around the central forecast.

They help show how wide the modeled outcome range may be, not a guaranteed result.

The weekly price chart shows market-price history for the selected ticker.

The vertical marker shows the active valuation date or today. The Log toggle changes only the chart scale.

Retrospective analysis reruns the model across multiple historical dates.

The result is a historical line showing how the model estimate would have moved over time.

Potential is the percentage gap between the estimated price and the market price.

Implied Disc. is the discount rate at which the model value would match the current market price.

Batch Calculation runs the same valuation model for multiple tickers.

Results appear in a sortable table so multiple companies can be compared quickly.

The batch limit is the maximum number of tickers allowed in one run.

Your available batch size depends on your account role and current service settings. Check the account page for the limits that apply to your access level.

Each row shows the main summary fields for one ticker.

This includes estimated price, market price, potential, discount rate, implied discount rate, and short-interest metrics.

A ticker may fail if required financial data is missing for that symbol or valuation point.

Batch runs continue for the remaining tickers even if one symbol cannot be processed.

Ticker lists store groups of symbols for batch runs.

Global lists are built into the app. Personal lists are saved to your account. Selecting a list fills the ticker field for reuse.

Save / update list stores the current symbols as a personal list.

Delete removes the selected personal list. Clear deselects the current list.

Calculation History stores previous valuation results for your account.

You can filter by ticker and date range, then review or clear the rows currently shown.

If the Underlying Data panel is shown, it displays the stored financial data behind a single-ticker result.

Filtered shows the dataset after valuation-date filtering. Full shows the complete raw stored dataset.

The account page shows your role-based limits and the default dashboard settings used in retrospective analysis.

This includes maximum batch size, retrospective data points, retrospective interval, and regression window length.

Retrospective data points control how many historical valuations are calculated.

Retrospective interval controls the spacing between them. More points produce a denser estimate line but require more calculations. Your current limits are shown in the account settings.

Regression window length is the number of recent quarterly observations used to fit the forecast trend.

A shorter window reacts faster to recent earnings changes. A longer window is smoother and gives more weight to longer history. The active default is shown in your current settings.

Reset UI settings restores your default retrospective data points, interval, and regression window length.

It does not change your account role or your batch limit.

DCF models work best for companies with relatively stable earnings patterns.

Results may be less reliable for companies with highly volatile earnings or business models that do not fit this approach well.

Estimates are usually less reliable when the business has unstable earnings, limited history, or unusual capital structure behavior.

Large changes in the discount rate or forecast assumptions can also move the result sharply.

Treat it as a research signal, not a trade instruction.

A large gap means the current model assumptions produce a very different estimate from the market price.

No. It is a research support tool.

Final investment decisions remain your responsibility.

Financial statement data is collected from public financial reporting sources and processed internally before being used in the model.

Market-price data and related market inputs are fetched separately for valuation and comparison.

Market data is fetched when calculations run and may be reused from a short cache.

The local financial database is also refreshed in the background on a regular schedule.

Results can change when the valuation date changes, when financial or market data updates, or when settings such as the discount rate or regression window length are changed.

Use the Contact page.

Include the ticker, approximate time, and what you expected versus what happened.