Monte Carlo Simulator

💡 What This Tool Does

The Monte Carlo Simulator runs thousands of random market scenarios to show the probability that the retirement plan will succeed. It accounts for market volatility, sequence of returns risk, and uncertainty to produce a realistic success rate.

Validation: Our Monte Carlo engine has been stress-tested across 100,000+ market scenarios, including extreme, low-probability "Black Swan"–style outcomes, to ensure stable and reliable results. View public stress test report

Key Questions It Answers:

• What's the probability my retirement plan will succeed?
• How likely am I to run out of money — vs. fail to fund spending in a given year?
• What's my plan's success rate across different market conditions?
• How sensitive is my plan to market volatility?

📐 Two Ways We Measure "Success" (and Depletion)

Praxion reports two related metrics. They answer different questions and can diverge sharply — a plan may show 100% balance solvency while funding success is much lower.

Full funding (strict): Positive balance at life expectancy, no plan insolvency, no material withdrawal shortfall, and all retirement years fully funded.

Balance solvency bands

• 95%+ — Very conservative: May indicate oversaving or very low spending
• 90%+ — Strong: Common high-confidence planning range
• 85%+ — Acceptable: CFP-style benchmark for many planners
• 75%+ — Borderline: Review savings, spending, or retirement timing
• 0%+ — At risk: Meaningful adjustments likely needed

Funding success bands (stricter)

• 90%+ — Strong: Spending funded across nearly all scenarios
• 75%+ — Acceptable: Most paths fund spending; monitor weak sequences
• 60%+ — Borderline: Many scenarios hit unfunded spending years
• 0%+ — At risk: Plan frequently fails to fund spending despite assets

Numeric depletion floor: assets below $1,000 count as depleted for balance solvency. Funding success uses the engine's planInsolvent flag when withdrawals cannot cover required spending.

📋 How to Use This Tool

Before Starting

Ensure profile data is complete:

• All retirement account balances
• Annual savings contributions
• Retirement expenses and income
• Asset allocation

Step-by-Step Instructions

1. Review the Plan: Ensure all data is accurate
2. Set Simulation Parameters: Number of iterations, time horizon
3. Run Simulation: Generate thousands of scenarios
4. Review both metrics: Balance solvency and funding success (Enhanced Success Metrics)
5. Analyze Results: Review highest-, lowest-, and average-modeled outcomes
6. Adjust if Needed: Modify plan if success rate is low

🔍 What to Look For

Key Metrics

1. Balance solvency: % of scenarios with positive ending balance (headline rate)
   • 85%+ acceptable for many planners; 90%+ strong
   • Below 75% — balance depletion likely in many paths
2. Funding success: % of scenarios with no unfunded spending year
   • 75%+ acceptable; 90%+ strong
   • Can be much lower than balance solvency — review both
3. Probability Distribution: Range of possible outcomes
4. Worst Case (10th percentile): Bottom 10% of outcomes
5. Strong Case (90th percentile): Top 10% of outcomes
6. Median Outcome: Middle scenario

📊 How to Interpret Results

Understanding the Output

Monte Carlo shows the probability distribution of outcomes, not a single prediction. It accounts for market uncertainty and volatility.

Next Steps

1. If success rate is good: Review annually
2. If success rate is low: Make adjustments and re-run
3. Compare to Stress Test results
4. Use results to inform withdrawal strategy

💼 Example Scenarios

Scenario 1: Strong Plan (Age 60, Retiring at 65)

• Savings: $1,500,000
• Annual Savings: $30,000
• Retirement Expenses: $60,000/year
• Success Rate: 92%
• Result: ✅ Very strong plan

Scenario 2: Risky Plan (Age 55, Retiring at 60)

• Savings: $600,000
• Annual Savings: $25,000
• Retirement Expenses: $80,000/year
• Success Rate: 65%
• Result: ⚠️ Need to save more or delay retirement

🎓 Understanding Monte Carlo Simulations

What Makes Monte Carlo Different?

Unlike simple projections that assume average returns every year, Monte Carlo simulations model real-world market volatility. Returns vary year-to-year, and the order matters—a market crash early in retirement is more damaging than one later.

Sequence of Returns Risk

The order of investment returns significantly impacts retirement success. Poor market returns in early retirement years force larger portfolio withdrawals (selling more shares when prices are low), which can deplete savings faster than the same average return with better early years.

Distribution of Outcomes

Monte Carlo provides a range of possible outcomes: strong case, weak case, and everything in between. This helps illustrate not just what is likely, but what is possible under different market conditions.

Historical vs. Forward-Looking Analysis

Monte Carlo uses statistical properties of historical returns to project future possibilities. While past performance does not imply future results, it provides a reasonable framework for modeling uncertainty.

Adjusting the Plan Based on Results

If the success rate is below 85%, consider: increasing savings, reducing expenses, delaying retirement, or adjusting asset allocation. Small changes can significantly improve the modeled success rate.

📚 Related Tools & Resources

Monte Carlo simulation is most informative when combined with other retirement planning tools:

• Social Security Analyzer — Model claiming strategy to improve Monte Carlo success rates
• Historical Stress Test — See how the plan performs against actual market crashes
• Tax Optimizer — May reduce lifetime taxes and retain more of modeled savings
• Withdrawal Strategy Comparison — Compare 4% rule vs. dynamic strategies
• Earliest Retirement Calculator — Find the soonest modeled retirement age at the target success rate
• Monte Carlo Simulation Guide — Deep dive into how Monte Carlo works

❓ Frequently Asked Questions

Q1: What's a good Monte Carlo success rate?

Check both metrics. For balance solvency (headline rate), many planners target 85–90%+. For funding success (no unfunded spending years), 75%+ is often acceptable and 90%+ is strong. A 100% balance solvency rate with low funding success means assets remain but spending is not always fully funded.

Q1b: When does Praxion count a plan as "depleted"?

Balance solvency fails when ending balance hits $0 or total assets fall below $1,000. Funding success fails when any year is flagged for unfunded required spending — even if the portfolio still holds substantial assets.

Q2: How many simulations should I run?

Consider running at least 1,000 simulations for statistical accuracy. More simulations (5,000-10,000) provide slightly more precision but generally don't change results significantly. Our default setting runs 10,000 simulations.

Q3: Is Monte Carlo analysis better than simple projections?

Yes, for retirement planning. Simple projections assume the same return every year, which never happens in reality. Monte Carlo accounts for market volatility and sequence of returns risk, providing a more realistic assessment of the plan's robustness.

Q4: Why does the success rate change each time it runs?

Monte Carlo uses random sampling to generate different scenarios each time. While there may be slight variations between runs (±1-2%), the overall success rate should be similar. Larger changes suggest reviewing input data.

Q5: What if the success rate is low?

If balance solvency is below 75% or funding success is below 60%, the plan likely needs adjustment. Options include saving more, reducing retirement spending, working longer, or revisiting withdrawal timing. If only funding success is low while balance solvency is high, focus on cash-flow timing, tax drag, and expense spikes — not just portfolio size.