The future is uncertain. Monte Carlo simulation embraces that uncertainty by running thousands of possible scenarios to estimate the probability of achieving your financial goals. It's the most sophisticated tool for retirement planning—when used correctly.
What Is Monte Carlo Simulation?
The Basic Idea
Instead of assuming one set of returns (like 7% annually), Monte Carlo runs thousands of scenarios with different return sequences, drawn from historical distributions.
Each scenario is a possible "path" your portfolio could take. The aggregate of all paths gives you probability distributions.
Example output:
- 10,000 scenarios simulated
- 8,500 scenarios: Portfolio lasts through retirement
- 1,500 scenarios: Portfolio depleted early
- Success rate: 85%
Why Not Just Use Averages?
Averages hide variability. A plan that works with average returns might fail 30% of the time due to sequence-of-returns risk|sequence-of-returns-risk.
Monte Carlo captures that variability.
How Monte Carlo Works
Step 1: Define Your Plan
Input your assumptions:
- Current portfolio value
- Asset allocation
- Annual spending (or withdrawal rate)
- Retirement length (or life expectancy)
- Inflation assumptions
- Additional income (Social Security, pension)
Step 2: Model Possible Returns
The simulator generates possible return sequences based on:
- Historical return distributions
- Assumed expected returns
- Standard deviations (volatility)
- Correlations between asset classes
Step 3: Run Thousands of Simulations
Each simulation:
- Generates a random return sequence
- Applies your withdrawal strategy
- Tracks portfolio value year-by-year
- Records whether portfolio survives
Step 4: Aggregate Results
- Success rate: % of scenarios where portfolio lasts
- Median outcome: Middle of the distribution
- Worst-case scenarios: Bottom 5-10%
- Best-case scenarios: Top 5-10%
Interpreting Results
Success Rate
The headline number. Most people target 80-90%.
90%+ success: Very conservative. May be under-spending. 80-90%: Reasonable safety margin. 70-80%: Higher risk. Flexibility needed. Below 70%: Plan needs adjustment.
Median vs. Average Outcome
- Median: Half of scenarios do better, half do worse
- Average: Skewed by extreme outcomes
Focus on median for typical expectations.
The Tail Outcomes
Most valuable insights are in the extremes:
- Bottom 10%: What happens if things go badly?
- Bottom 5%: Worst realistic case
- Top 10%: What if things go great?
Can you survive the bottom 10%? Would you adjust in the top 10%?
Key Assumptions That Drive Results
Expected Returns
Lower expected returns → Lower success rates
Historical US stock returns: ~10% nominal Many planners now assume: 6-8% nominal (lower going forward)
A 2% difference in assumed returns dramatically changes success rates.
Inflation Assumption
Historical average: ~3% Recent experience: Variable Impact: Higher inflation = lower real returns = lower success
Withdrawal Strategy
- Fixed percentage: Most common
- Flexible: Dramatically improves success rates
- Variable percentage: Adjusts based on portfolio value
Monte Carlo should test your actual intended strategy.
Time Horizon
Longer retirement = more scenarios that fail
- 20-year retirement: Higher success
- 40-year retirement: Lower success
Be realistic about planning horizon.
Limitations of Monte Carlo
1. Garbage In, Garbage Out
Results are only as good as assumptions. Wrong expected returns or inflation assumptions invalidate results.
2. Historical Data May Not Repeat
Simulations often draw from historical return distributions. Future may be different (better or worse).
3. Doesn't Model Behavior
Simulations assume you follow the plan exactly. In real life, you'd probably adjust spending in bad scenarios.
4. Precision ≠ Accuracy
Getting "85.7% success rate" implies false precision. Think in ranges: 80-90% is safer than 70-80%.
5. Point-in-Time Snapshot
Results change as markets move. An 90% plan today might be 75% after a market crash.
Using Monte Carlo Effectively
1. Run Multiple Scenarios
Test different assumptions:
- Base case
- Lower returns
- Higher inflation
- Early retirement
- Health shock (increased spending)
2. Focus on What You Control
Can't control returns. Can control:
- Savings rate
- Retirement age
- Spending level
- Asset allocation
- Flexibility
3. Build In Flexibility
Plans with adjustment mechanisms (spending cuts in bad years) dramatically improve success rates.
4. Update Regularly
Re-run simulation:
- Annually
- After major market moves
- When circumstances change
5. Don't Over-Optimize
A 92% vs. 88% success rate isn't meaningfully different. Don't make major life decisions based on small differences.
Monte Carlo Tools
Free/Low-Cost Options
- Portfolio Visualizer: Free, robust monte carlo
- cFIREsim: Popular for early retirement
- FIRECalc: Simple historical sequence testing
- Personal Capital: Free with account
Professional Tools
- Financial planning software (used by advisors): MoneyGuidePro, eMoney
- Actuarial tools: More sophisticated mortality modeling
What to Look For
- Customizable assumptions
- Flexible withdrawal strategies
- Clear visualization of results
- Sensitivity analysis
Beyond Monte Carlo: Historical Sequence Testing
What It Is
Instead of random returns, test your plan against every historical 30-year period.
Example: How would your plan have done starting in:
- 1929 (Great Depression)?
- 1966 (stagflation ahead)?
- 2000 (dot-com crash)?
Advantages
- Uses real historical sequences
- No assumptions about return distributions
- Shows worst actual historical outcome
Disadvantages
- Limited data (maybe 100 overlapping 30-year periods)
- Future may be worse than past
- US-centric bias
Use Both
Monte Carlo + historical testing together give fuller picture.
A Sample Analysis
The Situation
- Age 60, retiring now
- Portfolio: $2,000,000
- Annual spending: $80,000 (4% withdrawal rate)
- Asset allocation: 60% stocks / 40% bonds
- Social Security at 67: $30,000/year
Monte Carlo Results
- Success rate: 87%
- Median ending balance: $1,500,000
- 10th percentile: $200,000
- 5th percentile: Depleted at age 88
Interpretation
Plan works in most scenarios. Bottom 5-10% are concerning—runs out late in life.
Possible Adjustments
- Delay Social Security to 70 → +3% success
- Reduce spending by 10% → +5% success
- Add flexible spending rule → +8% success
The Bottom Line
Monte Carlo simulation is the best tool for understanding retirement plan robustness, but it requires realistic assumptions and careful interpretation. Run multiple scenarios, focus on tail risks, and build flexibility into your plan. Remember: 85% success isn't a guarantee—it means 15% of scenarios fail. Plan accordingly.