DCF Valuation Tool

A Python implementation of a Discounted Cash Flow (DCF) valuation model: take a company's fundamentals as input, get an intrinsic value per share as output, and stress-test the result with a two-way sensitivity analysis on WACC and terminal growth.

I built this to put into code the valuation logic I study at Bocconi and encounter in M&A-related research — every intermediate step (cash flows, discount factors, terminal value) is exposed rather than hidden, because in a DCF the assumptions are the analysis.

How it works

The model follows the standard unlevered DCF:

1. Project Free Cash Flow to the Firm over an explicit horizon:

FCFF = EBIT × (1 − tax rate) + D&A − CapEx − ΔNWC

Revenue grows at user-defined rates (one per year); margins and reinvestment are modeled as percentages of revenue.

2. Terminal value at the end of the horizon (Gordon Growth):

TV = FCFF_final × (1 + g) / (WACC − g)

3. Discount each cash flow and the terminal value at the WACC.

4. Bridge to equity:

Equity value = Enterprise value − Net debt
Value per share = Equity value / Shares outstanding

5. Sensitivity analysis — value per share across a WACC × terminal growth grid, as a pandas table and a heatmap. In the example, terminal value is ~73% of enterprise value, which is exactly why this table matters.

Usage

from dcf import CompanyInputs, run_dcf, sensitivity_table

company = CompanyInputs(
    name="Alpina Industries",
    revenue=1200.0,
    ebit_margin=0.14,
    tax_rate=0.26,
    depreciation_pct=0.045,
    capex_pct=0.055,
    nwc_pct=0.10,
    revenue_growth=[0.07, 0.06, 0.05, 0.04, 0.03],
    terminal_growth=0.02,
    wacc=0.09,
    net_debt=350.0,
    shares_outstanding=100.0,
)

result = run_dcf(company)
print(result.value_per_share)   # 14.51

table = sensitivity_table(company,
                          wacc_range=[0.07, 0.08, 0.09, 0.10, 0.11],
                          growth_range=[0.010, 0.015, 0.020, 0.025, 0.030])
print(table)

Or run the full worked example:

python examples/example_valuation.py

Example sensitivity output (value per share, €):

WACC \ g	1.00%	1.50%	2.00%	2.50%	3.00%
7.00%	18.30	19.90	21.83	24.18	27.12
8.00%	15.13	16.25	17.56	19.10	20.96
9.00%	12.75	13.57	14.51	15.59	16.85
10.00%	10.91	11.53	12.22	13.01	13.92
11.00%	9.43	9.91	10.45	11.04	11.72

Project structure

dcf/
├── model.py         # inputs, FCFF projection, terminal value, DCF engine
└── sensitivity.py   # two-way sensitivity table + heatmap
examples/
└── example_valuation.py

Requirements

Python 3.10+, pandas, numpy, matplotlib:

pip install -r requirements.txt

Limitations & possible extensions

This is deliberately a clean core model, not a terminal. Known simplifications: WACC is an input (no bottom-up beta / capital structure build), FCFF drivers are flat percentages of revenue, and there is no mid-year convention. Natural next steps: exit-multiple terminal value, scenario presets (bull/base/bear), and pulling fundamentals from an API instead of manual input.

Author

Alessandro Gaudio