DCF Valuation Simulator

A Monte Carlo simulation tool for enterprise valuation using discounted cash flow (DCF) analysis. This interactive web application helps financial analysts and investors understand valuation uncertainty through probabilistic modeling.

Features

• Monte Carlo Simulation: Run thousands of simulations to generate probability distributions of enterprise value
• Interactive Dashboard: Real-time parameter adjustment with immediate visual feedback
• Sensitivity Analysis: 2D heatmaps and 3D surface plots showing how WACC and growth rates impact valuation
• Statistical Insights: Comprehensive statistics including mean, median, confidence intervals, and percentiles
• Data Export: Download simulation results as CSV for further analysis

Installation

1. Clone the repository:

git clone https://github.com/dafahentra/dcf-valuation-simulator.git
cd dcf-valuation-simulator

2. Install required packages:

pip install -r requirements.txt

Requirements

streamlit==1.28.0
numpy==1.24.3
pandas==2.0.3
matplotlib==3.7.2
seaborn==0.12.2
plotly==5.17.0

Usage

1. Run the Streamlit app:

streamlit run simulation.py

2. Open your browser and navigate to http://localhost:8501

3. Adjust parameters in the sidebar:

   • WACC: Weighted Average Cost of Capital (8% - 16%)
   • Growth Rate: Revenue growth rate (3% - 10%)
   • Margin: Operating margin (10% - 20%)
   • Terminal Growth: Long-term growth rate (2% - 6%)

4. Click "Run Simulation" to generate valuation distribution

5. Use "Run Sensitivity Analysis" to explore how WACC and growth rate combinations affect valuation

How It Works

DCF Model Components

The simulator implements a 5-year DCF model with terminal value:

1. Revenue Projection: Revenue(t) = Revenue(0) × ∏(1 + growth(t))
2. NOPAT Calculation: NOPAT(t) = Revenue(t) × Margin(t)
3. Asset Calculation: Assets(t) = Revenue(t) / Asset_Turnover
4. Free Cash Flow: FCFF(t) = NOPAT(t) - Net_Investment(t)
5. Terminal Value: TV = FCFF(5) × (1 + g) / (WACC - g)
6. Enterprise Value: EV = Σ[FCFF(t) / (1 + WACC)^t]

Monte Carlo Simulation

Each simulation run:

• Randomly generates growth rates from Normal(μ_growth, σ_growth)
• Randomly generates margins from Normal(μ_margin, σ_margin)
• Randomly generates terminal growth from Normal(μ_terminal, σ_terminal)
• Calculates enterprise value using the DCF formula

Parameters

Primary Parameters

• Initial Revenue: Starting revenue (default: 100)
• Initial Assets: Starting assets (default: 80)
• Assets Turnover: Revenue/Assets ratio (default: 1.3)
• WACC: Discount rate for cash flows

Volatility Parameters

• Growth Std Dev: Standard deviation of growth rate
• Margin Std Dev: Standard deviation of operating margin
• Terminal Growth Std Dev: Standard deviation of terminal growth

Output

Simulation Results

• Distribution Plot: Histogram showing probability density of valuations
• Summary Statistics: Count, mean, std dev, min, max, and percentiles
• Box Plot: Visual representation of quartiles and outliers
• Confidence Intervals: 95% CI for valuation estimates

Sensitivity Analysis

• 2D Heatmap: Valuation matrix for WACC vs Growth Rate combinations
• 3D Surface Plot: Three-dimensional visualization of valuation surface

Example Output

Expected Value: 312.45
95% Confidence Interval: [245.32, 389.78]
Median: 308.91
Standard Deviation: 42.18

License

MIT License

Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

Author

Dafa Hentra Anjana

Acknowledgments

• Built with Streamlit for rapid web development
• Inspired by traditional DCF valuation methods in corporate finance