PASS

Monte Carlo Stress Test Report

Validation of our retirement planning engine under 100,000+ extreme market scenarios

Our claim: The logic was run through 100,000+ market scenarios to ensure the Monte Carlo outputs remained stable. These scenarios stress-test the engine against extreme, low-probability ("Black Swan") outcomes by exploring the extreme tails of probability distributions (e.g. severe drawdowns, elevated volatility, and adverse inflation/return regimes), consistent with the types of events used in institutional risk and stress-testing practice.

Report date: February 12, 2026 Scenarios run: 100,000 Runtime: 5.7 minutes Errors: 0

What was tested

This stress test ran the same Monte Carlo engine used in production through 100,000 scenarios. "Black Swan" scenarios are defined as extreme, low-probability market outcomes whose financial impact we capture through stressed return and volatility assumptions—consistent with macroeconomic shocks, financial market crises (e.g. 2008- or COVID-style volatility and drawdowns), and other tail events. We explore the extreme tails of probability distributions; we do not model specific geopolitical or operational events. The goal is to verify that the engine remains stable (no crashes, valid numeric outputs) under these severe conditions.

Results

Metric	Value
Success rate (under stress)	0.7%
Simulation errors	0
10th percentile (final balance)	$276,511
50th percentile (median)	$3,022,213
90th percentile	$10,691,499

Under Black Swan assumptions, most paths correctly show portfolio stress (low success rate); the important result is that the engine completed all 100,000 runs with zero errors and produced valid, finite outputs.

Methodology

Same engine as production; no live user data; run offline (local or CI). Stress test parameters: mean return -3%, volatility 35%, inflation mean 6%, inflation volatility 3% (extreme tail assumptions).