LifecycleFinance

Monte Carlo Simulation

Monte Carlo simulation is a sophisticated financial planning tool that runs thousands of potential future scenarios to assess the probability that your retirement plan will succeed. It helps answer the critical question: "Will my money last?"

Table of Contents

What Is Monte Carlo Simulation?

The Concept

  • Named after the Monte Carlo casino (randomness)
  • Uses random sampling to model thousands of possible outcomes
  • Accounts for market volatility and uncertainty
  • Provides probability-based results, not guarantees

How It Works

  1. Input your financial data (savings, spending, time horizon)
  2. Model uses historical return distributions (stocks, bonds)
  3. Runs thousands of simulations with random market returns
  4. Tracks success/failure in each scenario
  5. Reports probability of success (e.g., "85% chance of success")

Why Use Monte Carlo?

Beyond Simple Calculations

  • 4% Rule - Assumes average returns (doesn't account for sequence risk)
  • Monte Carlo - Models actual volatility and sequence of returns
  • Shows range of possible outcomes, not just average
  • Accounts for worst-case and best-case scenarios

Sequence of Returns Risk

  • Order of returns matters greatly
  • Poor early returns can devastate portfolio
  • Good early returns can boost success
  • Monte Carlo models this critical factor

Real-World Planning

  • Accounts for market uncertainty
  • Shows probability, not certainty
  • Helps make informed decisions
  • More realistic than linear projections

Key Inputs

Financial Data

  • Current Savings - Starting portfolio value
  • Monthly Contributions - Ongoing savings (if applicable)
  • Withdrawal Rate - Annual spending needs
  • Time Horizon - Years in retirement
  • Asset Allocation - Stock/bond mix

Market Assumptions

  • Expected Returns - Average returns for stocks/bonds
  • Volatility - Standard deviation of returns
  • Correlations - How assets move together

Based on historical data or forward-looking estimates.

Understanding Results

Success Rate

  • 95%+ Success - Very high confidence
  • 75-95% Success - Good confidence, some adjustments may help
  • <75% Success - Lower confidence, likely need bigger adjustments

What Success Means

  • Portfolio doesn't run out of money
  • Can maintain spending throughout retirement
  • Accounts for inflation
  • Handles market downturns

Failure Scenarios

  • Portfolio depleted before end of retirement
  • Need to reduce spending
  • May need to return to work
  • Could deplete assets

Using Results to Adjust

If Success Rate Is Too Low

  • Increase Savings - Save more before retirement
  • Reduce Spending - Lower withdrawal rate
  • Work Longer - Extend accumulation phase
  • Adjust Allocation - More stocks (if appropriate) for growth
  • Delay Social Security - Increase guaranteed income

If Success Rate Is High

  • Consider Earlier Retirement - If desired
  • Increase Spending - Enjoy retirement more
  • More Conservative Allocation - Reduce risk if desired
  • Gift to Family - If goals are secure

Limitations

Not a Guarantee

  • Probability, not certainty
  • Based on historical patterns
  • Future may differ from past
  • Extreme events (black swans) possible

Input Sensitivity

  • Results depend on assumptions
  • Return estimates may be wrong
  • Spending may change
  • Life circumstances evolve

Model Assumptions

  • Assumes normal distribution (may not hold in crises)
  • Doesn't account for all risks (health, long-term care)
  • May not model all income sources perfectly
  • Simplifies complex reality

When to Use Monte Carlo

Retirement Planning

  • 5-10 years before retirement
  • Assessing retirement readiness
  • Testing different scenarios
  • Making go/no-go decisions

Ongoing Planning

  • Annual plan reviews
  • After major life changes
  • When market conditions change
  • Adjusting strategy

Major Decisions

  • Early retirement consideration
  • Large purchase decisions
  • Career change impacts
  • Relocation planning

Monte Carlo vs. Other Methods

vs. 4% Rule

  • 4% Rule - Simple, assumes average returns
  • Monte Carlo - Complex, models volatility
  • 4% Rule - Single answer
  • Monte Carlo - Probability distribution

vs. Linear Projections

  • Linear - Assumes steady returns
  • Monte Carlo - Models ups and downs
  • Linear - One outcome
  • Monte Carlo - Range of outcomes

Best Practices

Use Realistic Assumptions

  • Don't be overly optimistic about returns
  • Account for fees and taxes
  • Use conservative estimates
  • Consider lower return environments

Run Multiple Scenarios

  • Test different spending levels
  • Try different retirement dates
  • Model various asset allocations
  • Consider different life expectancies

Update Regularly

  • Markets change
  • Your situation evolves
  • Assumptions may need adjustment
  • Re-run as you approach retirement

Don't Over-Optimize

  • 100% success may mean you're saving too much
  • Some risk is acceptable
  • Balance confidence with lifestyle
  • Perfect is the enemy of good

Tools and Resources

Financial Planning Software

  • Many advisors use Monte Carlo
  • The LCF Planner uses Monte Carlo to quantify uncertainty

Monte Carlo simulation is a powerful tool for retirement planning that goes beyond simple calculations to model the uncertainty and volatility of real markets. It helps you understand not just whether your plan might work on average, but the probability it will work given thousands of possible future scenarios.

While it's not a guarantee, Monte Carlo provides valuable insight into retirement readiness and helps you make informed decisions about savings, spending, and retirement timing. Use it as one tool in your planning arsenal, alongside other methods and professional advice.