Reverse DCF Valuation Model

An educational Python toolkit for answering a central reverse-DCF question:

What growth, margin, discount-rate, or terminal-growth assumption is implied by a company's current enterprise value?

The project includes a forward discounted cash flow engine, numerical reverse-solvers, scenario and sensitivity analysis, live company-data helpers, plotting utilities, and Markdown report generation.

Disclaimer: This project is for education and portfolio demonstration only. It is not financial advice or an investment recommendation.

How It Works

The model projects revenue at a constant compound annual growth rate, applies a constant free cash flow margin, discounts forecast cash flows, and calculates a terminal value using the Gordon Growth Model.

Projected revenue -> Free cash flow -> Present value
                                      + Discounted terminal value
                                      = Enterprise value

Enterprise value - Net debt = Equity value
Equity value / Shares outstanding = Implied share price

A reverse DCF holds the other inputs constant and uses Brent's root-finding method to solve for the assumption that makes model enterprise value equal the selected market enterprise value.

Features

• Forward DCF valuation with yearly revenue, FCF, and discounted-FCF projections
• Reverse-solvers for revenue CAGR, FCF margin, WACC, and terminal growth
• Bear, base, bull, and market-implied scenarios
• WACC, terminal-growth, margin, and growth sensitivity tables
• Best-effort company lookup and financial statement loading through yfinance
• Field-level data-quality labels for loaded and estimated values
• Matplotlib charts and Markdown report-generation helpers
• Command-line solver for market-implied revenue growth

Installation

Python 3.10 or newer is required.

python -m venv .venv
.\.venv\Scripts\Activate.ps1
python -m pip install --upgrade pip
pip install -e .

Install all development and dashboard dependencies from the repository:

pip install -r requirements.txt
pip install -e .

The package also defines optional dependency groups:

pip install -e ".[app]"  # Streamlit
pip install -e ".[dev]"  # pytest, Jupyter, and ipykernel

Quick Start

Run a forward DCF in Python:

from reversedcf.dcf import DCFInputs, run_dcf

inputs = DCFInputs(
    current_revenue=391_000_000_000,
    revenue_cagr=0.08,
    fcf_margin=0.27,
    forecast_years=5,
    wacc=0.08,
    terminal_growth=0.025,
    net_debt=-50_000_000_000,
    shares_outstanding=15_263_000_000,
)

valuation = run_dcf(inputs)

print(f"Enterprise value: ${valuation.enterprise_value:,.0f}")
print(f"Implied share price: ${valuation.implied_share_price:,.2f}")

With the sample assumptions above, the model produces an enterprise value of approximately $2.50T and an implied share price of approximately $166.76.

Solve for the revenue CAGR implied by a target enterprise value:

from reversedcf.reverse import solve_required_revenue_cagr

implied_growth = solve_required_revenue_cagr(
    target_enterprise_value=2_850_000_000_000,
    base_inputs=inputs,
)

print(f"Market-implied revenue CAGR: {implied_growth:.1%}")

CLI

After installing the package, inspect the available commands:

reversedcf --help

Solve for market-implied revenue growth:

reversedcf solve-growth `
  --target-enterprise-value 2850000000000 `
  --current-revenue 391000000000 `
  --fcf-margin 0.27

Expected output:

Required revenue CAGR: 11.2%
Model enterprise value: $2.85T

Live Company Data

The data module can resolve common company names or ticker symbols and load best-effort valuation inputs from yfinance:

from reversedcf.data import load_company_valuation_inputs

company = load_company_valuation_inputs("AAPL")
print(company)

Live financial data may be missing, delayed, inconsistent across companies, or estimated from other fields. Review every loaded value before using it in a valuation.

Model Assumptions

The current DCF is intentionally simple and uses:

• Constant revenue CAGR during the explicit forecast period
• Constant FCF margin during the explicit forecast period
• Constant WACC
• Gordon Growth terminal value
• Net debt defined as debt minus cash
• Consistent monetary units across all inputs

WACC must be greater than terminal growth. Percentage inputs are decimals, so use 0.08 for 8%.

Project Structure

reverse_dcf/
|-- docs/
|   |-- dcf_background_knowledge.md
|   |-- dcf_theory_notes.md
|   `-- recreation_guide.md
|-- src/reversedcf/
|   |-- cli.py
|   |-- data.py
|   |-- dcf.py
|   |-- fin.py
|   |-- metrics.py
|   |-- plotting.py
|   |-- report.py
|   |-- reverse.py
|   |-- scenarios.py
|   `-- sensitivity.py
|-- pyproject.toml
|-- requirements.txt
`-- streamlit.py

Current Limitations

• The single-stage model does not capture changing margins, cyclicality, dilution, buybacks, or changing capital intensity.
• Valuation results can be highly sensitive to WACC and terminal-growth inputs.
• Reverse-solvers only succeed when a solution exists within their configured search bounds.
• The run-case-study and generate-report CLI commands expect supporting files that are not currently included in this repository.
• The dashboard entry file is currently named streamlit.py, which shadows the installed streamlit package when launched from the repository root.

Further Reading

• docs/dcf_background_knowledge.md explains DCF concepts for readers new to finance.
• docs/dcf_theory_notes.md provides a compact finance-theory reference.
• docs/recreation_guide.md walks through the project's architecture and implementation phases.

License

MIT