EquityLens

Professional web-based DCF valuation with peer group analysis

Modern web application for Discounted Cash Flow (DCF) company valuations with integrated multiplier analysis. Based on proven Excel models with automatic WACC iteration and comprehensive peer group valuation.

DISCLAIMER

This tool is provided for educational purposes only as part of the Financial Analysis course project.

• This software is provided "as is" without warranty of any kind, express or implied
• No liability is accepted for any financial decisions made based on this tool
• The valuations and analyses are for educational demonstration purposes only
• Always consult qualified financial professionals for investment decisions
• The developers assume no responsibility for any losses or damages arising from the use of this tool

Key Features

DCF Valuation

• WACC Iteration - Automatic iterative calculation (up to 25 steps)
• CAPM Model - Cost of equity based on Capital Asset Pricing Model
• Dual Approach - Entity & Equity methods in parallel
• Tax Shield - Tax benefits from debt financing
• Historical Base - 4 years of historical data

Multiplier Analysis (Peer Group)

• 5 Multiples - EV/Sales, EV/EBIT, EV/EBITDA, P/E, P/B
• Peer Comparison - Unlimited number of comparable companies
• Statistics - Min, Mean, Median, Max + Target comparison
• Visual Indicators - Over-/Undervaluation at a glance

Additional Features

• Sensitivity Analysis - WACC ±2%, Terminal Growth ±1%
• Scenario Comparison - Best/Base/Worst Case
• Excel Import/Export - Import data from Excel
• Persistence - Save and load analyses
• Interactive Charts - Waterfall, Fair Value, Cash Flow

Tech Stack

Backend: FastAPI (Python) • Pydantic • openpyxl
Frontend: React 18 • Vite • TailwindCSS • Recharts • Framer Motion
Data: JSON-based persistence

Installation

Prerequisites:

• Python 3.8+
• Node.js 16+
• macOS/Linux (for start scripts)

Quick Start

# Start (automatically installs all dependencies)
./start.sh

# Stop
./stop.sh
# or Ctrl+C

The start script:

• Frees ports 8000/3000
• Creates Python venv automatically
• Installs all dependencies
• Starts backend + frontend

Access:

• Frontend: http://localhost:3000
• Backend API: http://localhost:8000
• API Docs: http://localhost:8000/docs

Manual Installation

Backend Setup

cd backend
python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt
python main.py

Frontend Setup

cd frontend
npm install
npm run dev

Usage

1. New DCF Analysis

1. Base Data - Company name, shares, debt, current price
2. Historical Data (4 years) - Revenue, EBIT, D&A, NWC, CapEx, Interest, Leverage
3. CAPM Parameters - Risk-free Rate, Beta, Market Return, Cost of Debt, Tax Rate
4. Forecast - Revenue growth, Terminal Growth, Forecast years
5. Calculate - Automatic WACC iteration and valuation

2. Multiplier Analysis (Optional)

1. Target Metrics - Revenue, EBITDA, EBIT, EPS, Book Value, Debt
2. Peer Companies - Comparable companies with same metrics
3. Automatic Calculation - Statistics and fair value estimates

3. Advanced Analyses

• Sensitivity Analysis - WACC ±2%, Growth ±1% (9x9 matrix)
• Scenario Comparison - Best/Base/Worst case scenarios
• Excel Import - Import data from Excel template

Save & Load

• Analyses are saved automatically
• Click on saved analysis to load
• Delete with trash icon

API Endpoints

• GET /api/analyses - Retrieve all analyses
• POST /api/analyses - Create new analysis
• GET /api/analyses/{id} - Get specific analysis
• PUT /api/analyses/{id} - Update analysis
• DELETE /api/analyses/{id} - Delete analysis
• GET /api/excel-template - Download Excel template
• POST /api/excel-import - Import Excel data
• POST /api/sensitivity - Sensitivity analysis
• POST /api/scenario - Scenario comparison

API Documentation: http://localhost:8000/docs

Calculation Methodology

DCF Method

Historical Analysis → CAPM → Forecast → WACC Iteration → Valuation

1. CAPM: re = rf + β × (rm - rf)
2. WACC Iteration: Up to 25 steps until convergence (Δ < 0.0000001)
3. Entity Approach: EV = Σ PV(FCF) + PV(Terminal Value)
4. Equity Approach: Equity = Σ PV(FCFE) + PV(TV Equity)
5. Fair Value: Share Price = Equity Value / Shares

Multiplier Method

Calculate Peer Multiples → Statistics → Value Target

• Entity: EV/Sales, EV/EBIT, EV/EBITDA
• Equity: P/E, P/B
• Statistics: Min, Mean, Median, Max
• Valuation: Target × Median Multiple

Architecture

System Architecture

graph TB
    subgraph "Frontend (React + Vite)"
        UI[User Interface]
        App[App Component]
        DCF[DCF Input Forms]
        Multi[Multiplier Section]
        Sens[Sensitivity Analysis]
        Scen[Scenario Comparison]
        Export[Export Buttons]
        Charts[Interactive Charts]
    end
    
    subgraph "Backend (FastAPI)"
        API[REST API]
        CAPM[CAPM Calculator]
        WACC[WACC Iteration]
        DCFCalc[DCF Calculator]
        MultCalc[Multiplier Calculator]
        SensCalc[Sensitivity Calculator]
        ScenCalc[Scenario Calculator]
        Excel[Excel Import/Export]
    end
    
    subgraph "Data Layer"
        JSON[(JSON Storage)]
        Template[Excel Template]
    end
    
    UI --> App
    App --> DCF
    App --> Multi
    App --> Sens
    App --> Scen
    App --> Export
    App --> Charts
    
    DCF --> API
    Multi --> API
    Sens --> API
    Scen --> API
    Export --> API
    
    API --> CAPM
    API --> WACC
    API --> DCFCalc
    API --> MultCalc
    API --> SensCalc
    API --> ScenCalc
    API --> Excel
    
    CAPM --> WACC
    WACC --> DCFCalc
    DCFCalc --> JSON
    MultCalc --> JSON
    SensCalc --> JSON
    ScenCalc --> JSON
    Excel --> Template
    
    style UI fill:#e1f5ff
    style API fill:#fff4e1
    style JSON fill:#e8f5e9

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DCF Calculation Flow

flowchart TD
    Start([User Input]) --> Validate{Valid Input?}
    Validate -->|No| Error[Return Error]
    Validate -->|Yes| Historical[Calculate Historical Metrics]
    
    Historical --> CAPM[Calculate CAPM<br/>re = rf + beta * market risk premium]
    CAPM --> WACC1[Initial WACC = 8%]
    
    WACC1 --> Forecast[Generate Forecast<br/>Revenue, EBIT, FCF, FCFE]
    Forecast --> Terminal[Calculate Terminal Value<br/>TV = FCF * 1+g / WACC-g]
    
    Terminal --> PV[Calculate Present Values<br/>Discount all cash flows]
    PV --> EV[Calculate Enterprise Value<br/>EV = Sum of PV FCF + PV TV]
    
    EV --> Equity[Calculate Equity Value<br/>Equity = EV - Net Debt]
    Equity --> NewWACC[Calculate New WACC<br/>Based on EV and Equity]
    
    NewWACC --> Converged{WACC<br/>Converged?}
    Converged -->|No<br/>Iteration < 25| Forecast
    Converged -->|Yes| SharePrice[Calculate Share Price<br/>Price = Equity / Shares]
    
    SharePrice --> Multiplier{Multiplier<br/>Data?}
    Multiplier -->|Yes| PeerCalc[Calculate Peer Multiples<br/>EV/Sales, EV/EBIT, etc.]
    Multiplier -->|No| Results
    
    PeerCalc --> Stats[Calculate Statistics<br/>Min, Mean, Median, Max]
    Stats --> TargetVal[Calculate Target Valuation<br/>Using Median Multiples]
    TargetVal --> Results
    
    Results[Return Complete Results]
    
    style Start fill:#e1f5ff
    style CAPM fill:#fff4e1
    style WACC1 fill:#fff4e1
    style Converged fill:#ffe1e1
    style Results fill:#e8f5e9

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Project Structure

.
├── backend/
│   ├── main.py              # FastAPI Backend
│   ├── requirements.txt     # Dependencies
│   ├── venv/               # Virtual Environment
│   └── data/               # Saved analyses (not in Git)
├── frontend/
│   ├── src/                # React Components
│   ├── package.json        # Dependencies
│   └── vite.config.js      # Build Config
├── start.sh                # Startup Script
├── stop.sh                 # Shutdown Script
└── README.md

Notes

• Saved analyses are stored in backend/data/ (not in Git)
• Excel template can be downloaded via /api/excel-template
• API documentation available at http://localhost:8000/docs
• Production build: cd frontend && npm run build

License

MIT License

Academic Context

EquityLens was created as a project for the Financial Analysis course. It demonstrates the implementation of DCF valuation models and peer group analysis in a modern web application.

For educational purposes only - Not for production financial decisions.