scenario-modeler

Business

Monte Carlo simulations for exit scenarios, return distributions

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Scenario Modeler

Overview

The Scenario Modeler skill provides advanced scenario analysis and Monte Carlo simulations for venture capital return modeling. It enables probabilistic analysis of exit outcomes and return distributions to inform investment decisions and portfolio construction.

Capabilities

Exit Scenario Modeling

Model multiple exit scenarios (IPO, M&A, secondary)
Assign probabilities to scenarios
Calculate expected returns across outcomes
Account for timing variations

Monte Carlo Simulation

Run thousands of probabilistic scenarios
Model parameter distributions
Generate return distributions
Calculate confidence intervals

Sensitivity Analysis

Identify key value drivers
Model driver interactions
Create tornado charts
Determine break-even assumptions

Return Distribution Analysis

Calculate expected IRR and MOIC
Generate return percentiles
Model loss probability
Analyze portfolio-level returns

Usage

Model Exit Scenarios

Input: Company data, exit assumptions
Process: Build scenarios, assign probabilities
Output: Scenario matrix, expected value

Run Monte Carlo

Input: Base assumptions, parameter distributions
Process: Run simulation iterations
Output: Return distribution, percentile analysis

Analyze Sensitivities

Input: Base case, key drivers
Process: Calculate driver sensitivities
Output: Sensitivity analysis, tornado chart

Model Portfolio Returns

Input: Portfolio of investments, scenarios
Process: Aggregate portfolio outcomes
Output: Portfolio return distribution

Scenario Framework

Scenario	Probability Range	Typical Multiple
Home Run	5-15%	10x+
Strong Exit	15-25%	3-10x
Moderate Exit	20-30%	1-3x
Flat/Write-off	30-50%	0-1x

Integration Points

VC Method Valuation: Scenario-based valuation
Cap Table Modeling: Ownership under scenarios
DCF Analysis: Probability-weighted DCF
Sensitivity Analyst (Agent): Support scenario analysis

Simulation Parameters

Parameter	Distribution Type
Exit Multiple	Log-normal
Exit Timing	Normal/Triangular
Revenue Growth	Normal
Market Multiple	Log-normal
Dilution	Triangular

Best Practices

Ground scenarios in historical data
Validate probability assumptions
Include tail scenarios (both positive and negative)
Consider correlation between assumptions
Use simulations for insight, not precision