Free DCF Valuation API — Intrinsic Value for Any Stock
Get a discounted cash flow fair value estimate for any US public company. Our model uses free cash flow projections, WACC, and terminal growth rates derived entirely from SEC filings — no proprietary data, no API key.
Endpoint
GET https://securitiesdb.com/api/v1/stocks/{ticker}/dcfWhat Is a DCF Model?
A Discounted Cash Flow (DCF) model estimates a company's intrinsic value by projecting future free cash flows and discounting them back to present value using the weighted average cost of capital (WACC).
Our implementation uses trailing free cash flow from the most recent 10-K filing, an implied terminal growth rate backed out from the current market price, and a WACC calculated from the capital asset pricing model (CAPM) with the Fama-French risk-free rate.
Fair Value = Σ(FCF × (1 + g)^t / (1 + WACC)^t) + Terminal Value / (1 + WACC)^n
Response Fields
| Field | Description |
|---|---|
| dcf.fair_value | Estimated intrinsic value per share |
| dcf.upside_pct | Percentage upside (or downside) vs current price |
| dcf.wacc | Weighted average cost of capital used |
| dcf.implied_growth | Terminal growth rate implied by market price |
| sensitivity_matrix | 3×3 grid of fair values across WACC/growth scenarios |
Python Example — Screen for Undervalued Stocks
import requests
tickers = ["AAPL", "MSFT", "GOOGL", "AMZN", "META"]
undervalued = []
for ticker in tickers:
r = requests.get(f"https://securitiesdb.com/api/v1/stocks/{ticker}/dcf")
if r.status_code != 200:
continue
dcf = r.json()["data"]["dcf"]
if dcf["upside_pct"] > 15:
undervalued.append({
"ticker": ticker,
"fair_value": dcf["fair_value"],
"upside": dcf["upside_pct"],
"wacc": dcf["wacc"],
})
print(f"Found {len(undervalued)} potentially undervalued stocks:")
for s in undervalued:
print(f" {s['ticker']}: Fair value ${s['fair_value']:.2f} "
f"(+{s['upside']:.1f}%, WACC={s['wacc']:.1%})")JavaScript Example
const res = await fetch("https://securitiesdb.com/api/v1/stocks/NVDA/dcf");
const { data } = await res.json();
console.log(`Fair Value: $${data.dcf.fair_value}`);
console.log(`Upside: ${data.dcf.upside_pct}%`);
console.log("Sensitivity Matrix:", data.sensitivity_matrix);