Comprehensive LBO Model for Financial Analysis

A sophisticated Python-based LBO model featuring covenant tracking, sensitivity analysis, Monte Carlo simulation, and professional PDF reporting with organized output management.

pip install -r requirements.txt

Command Line:

cd src/modules
python orchestrator_advanced.py

Interactive Web App:

streamlit run streamlit_app.py

All analysis files are automatically saved to the output/ folder:

• PDF Report: Comprehensive analysis document
• Charts: Professional visualizations and analysis charts
• Console: Detailed financial metrics and results
• Interactive App: Real-time assumption testing and live covenant tracking

• IRR: ~11–13% | MOIC: ×ばつ | Hold: 5 years
• Leverage (lease-adjusted): ~60–65% of EV at entry
• Covenants: Net Debt/EBITDA ≤ ×ばつ (default), ICR ≥ ×ばつ (default) — both configurable in data/accor_assumptions.csv
• IFRS-16: Lease-in-debt; lease interest included in ICR; lease liability included in net debt at exit
• Working capital: days-based (AR/AP/deferred revenue)

• Sensitivity Analysis: IRR vs Terminal EBITDA Margin (±400 bps) and Exit Multiple (×ばつ)
• Monte Carlo: 400 scenarios (configurable), with printed priors and success rule: σ(growth)=±150 bps, σ(margin)=±200 bps, σ(multiple)=×ばつ; success = no covenant breach + positive exit equity + IRR ≥ 8%
• Deterministic Stress: Named downside with four outputs (IRR, trough ICR, max ND/EBITDA, Breach Y/N)
• Equity Cash-Flow Vector: IRR computed from the exact equity vector printed in the PDF

• Comprehensive PDF reports with executive summary
• Professional chart generation (covenant tracking, exit bridge, deleveraging path)
• Sources & Uses waterfall analysis
• Exit equity bridge visualization
• Interactive Streamlit app for real-time scenario analysis

lbo-stack/
├── src/modules/
│ ├── orchestrator_advanced.py # Main LBO model
│ ├── lbo_model.py # Core financial modeling
│ └── fund_waterfall.py # Fund economics and waterfall
├── data/
│ ├── accor_assumptions.csv # Model assumptions
│ └── accor_historical_recreated.csv # Historical financial data
├── output/ # Generated analysis outputs
│ ├── accor_lbo_enhanced.pdf # Comprehensive analysis report
│ ├── covenant_headroom.png # Covenant compliance tracking
│ ├── deleveraging_path.png # Debt reduction visualization 
│ ├── exit_equity_bridge.png # Exit value waterfall chart
│ ├── monte_carlo.png # Monte Carlo simulation results
│ ├── sensitivity_heatmap.png # Sensitivity analysis heatmap
│ └── sources_uses.png # Sources & uses of funds
├── streamlit_app.py # Interactive web application
├── requirements.txt # Python dependencies
└── README.md # This documentation

Comprehensive analysis document containing:

• Executive summary with key metrics
• Detailed equity cash flow vector analysis
• Embedded charts and visualizations
• Monte Carlo simulation summary
• Sensitivity analysis results table

Visual tracking of covenant compliance throughout the investment period:

• Net Debt/EBITDA ratio vs covenant threshold
• Interest Coverage Ratio (ICR) vs minimum requirement
• Color-coded compliance indicators

Debt reduction visualization showing:

• Total debt outstanding over time
• Annual debt paydown amounts
• EBITDA growth trajectory
• Net leverage ratio evolution

Exit value waterfall chart displaying:

• Enterprise value at exit
• Less: Outstanding debt
• Transaction costs and fees
• Net proceeds to equity investors

Monte Carlo simulation results featuring:

• IRR distribution histogram
• Success rate analysis
• P10/P50/P90 percentile markers
• Risk assessment metrics

Two-dimensional sensitivity analysis showing:

• IRR sensitivity to Terminal EBITDA Margin (±400 bps) and Exit Multiple (×ばつ) assumptions
• Color-coded heat map for visual impact assessment
• Base case positioning within scenario range

Sources and uses of funds at transaction entry:

• Equity contribution breakdown
• Debt facilities sizing
• Transaction costs allocation
• Total use of funds summary

Professional Streamlit application featuring:

• Real-time assumption testing: Adjust entry/exit multiples, leverage, covenants
• Live covenant monitoring: Watch ICR and Net Debt/EBITDA move with assumptions
• Interactive Monte Carlo: Configure scenarios (100/200/400) with reproducible seeds
• One-click PDF generation: Produces the exact same report as CLI version
• Professional KPI dashboard: IRR, MOIC, covenant status with visual indicators

Edit data/accor_assumptions.csv to modify:

• Revenue growth assumptions
• EBITDA margin projections
• Entry/exit multiples
• Debt structure and pricing
• Covenant levels (defaults: Net Debt/EBITDA ≤ ×ばつ, ICR ≥ ×ばつ)

Sample Results (from run: 2025年08月13日, seed=42)

• Entry Multiple: ×ばつ | Exit Multiple: ×ばつ
• Target Leverage (lease-adjusted): ~60–65% of EV
• Max Net Debt/EBITDA (observed): ×ばつ vs covenant ×ばつ
• Min ICR (observed): ×ばつ vs covenant ×ばつ
• Covenant Status: Compliant (no breaches)

Monte Carlo (N=400, seed=42)

• Success Rate: ~70–80%
• Median IRR: ~12–13%
• P10–P90: ~9–17%
• Success rule: no covenant breach + positive exit equity + IRR ≥ 8%

• Lease-adjusted leverage and explicit covenants with headroom charts
• Sources & Uses, Exit Equity Bridge, Deleveraging Walk embedded in the PDF
• Equity cash-flow vector printed and reconciled to IRR
• Reproducible: one command regenerates the exact PDF; RNG seed pinned
• Unit tests for IRR monotonicity and equity-vector reconciliation
• Interactive Streamlit app for live scenario testing and covenant monitoring

• Python 3.11+ compatible
• Pandas/NumPy for financial modeling
• Matplotlib for professional charts with Agg backend
• FPDF2 for PDF generation
• Organized output management with dedicated folder structure

MIT License - See LICENSE file for details