Financial Modeling Suite

A comprehensive financial modeling toolkit for investment analysis, valuation, and risk assessment using industry-standard methodologies.

Scripts Included

1. Discounted Cash Flow (DCF) Analysis

Module: dcf_model.py

Build complete DCF models with multiple growth scenarios, calculate terminal values using perpetuity growth and exit multiple methods, determine WACC, and generate enterprise and equity valuations.

Key Features

• Historical financial data input
• Projection engine with customizable assumptions
• WACC calculation using CAPM
• Terminal value (growth or exit multiple)
• Sensitivity analysis on key drivers

Example Usage

from dcf_model import DCFModel

model = DCFModel("TechCorp")
model.set_historical_financials(revenue=[800, 900, 1000], ebitda=[160, 189, 220], ...)
model.set_assumptions(projection_years=5, revenue_growth=[0.15, 0.12, 0.10, 0.08, 0.06])
model.calculate_wacc(risk_free_rate=0.04, beta=1.2, market_premium=0.07, ...)
model.calculate_enterprise_value()
print(model.generate_summary())

2. Sensitivity Analysis

Module: sensitivity_analysis.py

Test key assumptions impact on valuation with one-way and two-way sensitivity tables, tornado charts, and breakeven analysis.

Key Features

• One-way sensitivity with percentage ranges
• Two-way data tables (Excel-style)
• Tornado analysis for driver ranking
• Scenario comparison tables
• Breakeven search algorithm

Example Usage

from sensitivity_analysis import SensitivityAnalyzer, create_data_table

analyzer = SensitivityAnalyzer(model)
tornado = analyzer.tornado_analysis(variables, output_func)
data_table = create_data_table(
    row_variable=("WACC", [0.08, 0.10, 0.12], wacc_update),
    col_variable=("Growth", [0.02, 0.03, 0.04], growth_update),
    output_func=model.calculate_value
)

3. Monte Carlo Simulation

Module: monte_carlo.py

Run thousands of scenarios with probability distributions to quantify uncertainty and generate confidence intervals.

Key Features

• Multiple distribution types: normal, lognormal, triangular, uniform, PERT
• Correlated variable sampling via Cholesky decomposition
• Statistical summary (mean, median, percentiles, skewness)
• Confidence intervals (90%, 95%)
• Value at Risk (VaR) and Conditional VaR
• Sensitivity contribution analysis
• Probability of target achievement

Distribution Types

Example Usage

from monte_carlo import MonteCarloSimulator

sim = MonteCarloSimulator(seed=42)
sim.add_normal('revenue', mean=1000, std=100)
sim.add_triangular('margin', low=0.15, mode=0.20, high=0.28)
sim.add_pert('growth', low=0.05, mode=0.10, high=0.20)

# Set correlations
sim.set_correlations({('growth', 'margin'): 0.3})

# Run simulation
results = sim.run_simulation(dcf_model, n_iterations=5000)
print(sim.generate_report('enterprise_value'))
print(f"Probability of EV > $15M: {sim.probability_of_target('enterprise_value', 15000):.1%}")

4. Scenario Planning

Module: scenario_planner.py

Build and compare multiple scenarios with probability weighting, decision tree analysis, and economic stress testing.

Key Features

• Best/base/worst case scenarios
• Custom scenario definitions
• Probability-weighted expected values
• Decision tree analysis with expected value calculation
• Pre-built economic scenarios (recession, boom, stagflation, etc.)
• Upside/downside analysis

Pre-Built Economic Scenarios

• recession: -2% GDP, 8% unemployment
• stagflation: 0.5% GDP, 6% inflation
• expansion: 3.5% GDP, 4% unemployment
• boom: 5% GDP, 3.5% unemployment
• deflation: -1% GDP, -1% inflation
• normalization: 2% GDP, 2% inflation

Example Usage

from scenario_planner import ScenarioPlanner, DecisionTreeAnalyzer

planner = ScenarioPlanner()
planner.set_model(investment_model)
planner.add_standard_scenarios({
    'revenue': (800, 1000, 1200),
    'growth': (0.02, 0.05, 0.10),
    'margin': (0.10, 0.15, 0.22)
})
results = planner.run_scenarios()
ev = planner.calculate_expected_value('exit_value')

# Decision tree
tree = DecisionTreeAnalyzer("Acquisition Decision")
tree.add_decision("Acquire", [
    ("High Synergies", 0.3, 2_000_000),
    ("Low Synergies", 0.4, 800_000),
    ("Failure", 0.3, -500_000)
])
print(f"Expected Value: ${tree.calculate_expected_value():,.0f}")

5. LBO Model

Module: lbo_model.py

Leveraged buyout analysis with sources & uses, debt schedules, returns calculation, and waterfall distributions.

Key Features

• Sources & uses of funds
• Multiple debt tranches (senior, sub, mezz, PIK)
• Amortization schedules
• IRR, MOIC, cash return calculations
• Equity waterfall with carry tiers
• Management promote calculations

Debt Types Supported

• Senior secured
• Term Loan A/B
• Second lien / Subordinated
• Mezzanine
• PIK (payment-in-kind)
• Revolving credit

Example Usage

from lbo_model import LBOModel, DebtType, quick_lbo

# Full model
lbo = LBOModel("Acme Corp")
lbo.set_transaction(entry_multiple=8.0, ltm_ebitda=50)
lbo.add_debt_tranche("Term Loan B", DebtType.TERM_LOAN_B, multiple=4.0, interest_rate=0.065)
lbo.add_debt_tranche("Second Lien", DebtType.SUBORDINATED, multiple=1.5, interest_rate=0.095)
lbo.calculate_sources_uses()
lbo.set_operating_assumptions(base_revenue=250, revenue_growth=[0.08, 0.07, 0.06, 0.05, 0.04])
lbo.set_exit(exit_multiple=8.5, exit_year=5)
lbo.build_debt_schedule()
returns = lbo.calculate_returns()

# Waterfall
lbo.add_waterfall_tier("8% Preferred", 0.08, 0.80, 0.20)
lbo.add_waterfall_tier("20%+ Carry", 0.20, 0.50, 0.50)
waterfall = lbo.calculate_waterfall()

# Quick analysis
model = quick_lbo(
    purchase_price=400, ltm_ebitda=50, revenue=250,
    revenue_growth=0.06, ebitda_margin=0.20
)

6. Real Estate DCF

Module: real_estate_dcf.py

Property-level DCF analysis specialized for multifamily and commercial real estate investments.

Key Features

• Unit mix modeling (studio, 1BR, 2BR, etc.)
• Detailed operating proforma
• GPR → Vacancy → EGI → NOI waterfall
• Debt service with I/O periods
• CapEx reserves by component
• Exit cap rate valuation
• Refinancing analysis
• Cap rate sensitivity tables

Property Types Supported

• Multifamily
• Office
• Retail
• Industrial
• Self-storage
• Mixed-use

Key Metrics Calculated

• IRR (levered)
• Equity Multiple
• Cash-on-Cash Return
• Going-in Cap Rate
• DSCR (Debt Service Coverage Ratio)
• LTV (Loan-to-Value)

Example Usage

from real_estate_dcf import RealEstateDCF, UnitMix, PropertyType, quick_multifamily_analysis

# Full model
model = RealEstateDCF("Brooklyn Center Apartments", PropertyType.MULTIFAMILY)
model.set_property_details(
    units=90, total_sqft=72000,
    unit_mix=[
        UnitMix("Studio", 10, 500, 950),
        UnitMix("1BR", 45, 750, 1150),
        UnitMix("2BR", 30, 950, 1400),
        UnitMix("3BR", 5, 1200, 1700),
    ]
)
model.set_acquisition(purchase_price=8_100_000, capex_at_acquisition=200_000)
model.set_financing(ltv=0.75, interest_rate=0.0575, io_period_years=2)
model.set_operating_assumptions(hold_period=5, rent_growth=[0.04, 0.035, 0.03, 0.03, 0.025])
model.set_exit(exit_cap_rate=0.06)
model.build_proforma()
returns = model.calculate_returns()
print(model.generate_summary())

# Quick analysis
model = quick_multifamily_analysis(
    purchase_price=8_100_000, units=90, avg_monthly_rent=1200,
    ltv=0.75, exit_cap_rate=0.06
)

Input Requirements Summary

For DCF Analysis

• Historical financial statements (3-5 years)
• Revenue growth assumptions
• Operating margin projections
• Capital expenditure forecasts
• Working capital requirements
• Terminal growth rate or exit multiple
• Discount rate components (risk-free rate, beta, market premium)

For Monte Carlo Simulation

• Probability distributions for uncertain variables
• Correlation assumptions between variables
• Number of iterations (typically 1,000-10,000)

For LBO Analysis

• LTM EBITDA and purchase multiple
• Debt structure (tranches, rates, amortization)
• Operating projections
• Exit assumptions

For Real Estate DCF

• Property details (units, SF, unit mix)
• Acquisition price and financing terms
• Rent roll / market rents
• Operating expenses
• CapEx reserves
• Exit cap rate

Best Practices Applied

Modeling Standards

• Consistent formatting and structure
• Clear assumption documentation
• Separation of inputs, calculations, outputs
• Error checking and validation

Valuation Principles

• Use multiple valuation methods for triangulation
• Apply appropriate risk adjustments
• Consider market comparables
• Validate against trading multiples
• Document key assumptions clearly

Risk Management

• Identify and quantify key risks
• Use probability-weighted scenarios
• Stress test extreme cases
• Consider correlation effects
• Provide confidence intervals

Limitations and Disclaimers

• Models are only as good as their assumptions
• Past performance doesn't guarantee future results
• Market conditions can change rapidly
• Regulatory and tax changes may impact results
• Professional judgment required for interpretation
• Not a substitute for professional financial advice