Our Methodology
How We Calculate Your Projections
From your inputs to actionable insights in four steps.
1. Your Inputs
Current age, retirement age, savings, spending, asset allocation, Social Security, and other income sources.
- •Current portfolio value
- •Monthly contributions until retirement
- •Expected retirement spending
- •Social Security claiming age
- •Pension or other income
2. Historical Data
Two data sources: Shiller's dataset (1871-present) for Historical Bootstrap mode, or per-asset Yahoo Finance distributions for Parametric mode.
- •Shiller: S&P 500 total returns, 10-year Treasury yields, CPI inflation
- •Yahoo Finance: per-asset return distributions for individual tickers
- •Bootstrap preserves real stock/bond/inflation correlations
- •Choose your data source per scenario
3. Monte Carlo Simulation
Two simulation methods: Historical Bootstrap samples full-year tuples from 150+ years of data; Parametric generates returns from per-asset distributions.
- •Historical Bootstrap: samples real year tuples with replacement
- •Parametric: models each asset's return distribution independently
- •Bootstrap preserves fat tails and regime changes
- •Free: up to 1,000 runs; Premium: up to 10,000 runs
4. Results Analysis
We analyze all scenarios to calculate success probability, safe withdrawal rates, and risk metrics.
- •Success rate (% of paths where money lasts)
- •Percentile bands (10th, 25th, 50th, 75th, 90th)
- •Worst-case analysis
- •Optimal withdrawal recommendations
Key insight: Unlike simple calculators that assume a fixed 7% annual return, Monte Carlo simulation shows you the range of possible outcomes. Markets don't return a steady percentage—they're volatile. Our approach captures that reality.
Monte Carlo Simulation
The gold standard for retirement planning used by financial institutions worldwide.
Monte Carlo simulation runs your retirement plan through thousands of scenarios based on historical market data. Instead of assuming markets return a steady 7% every year (they don't), we test what happens when you retire into a bull market, a bear market, or anything in between.
How Bootstrap Sampling Works
We use a technique called bootstrap sampling. For each simulation:
- We randomly select a year from history (e.g., 1973)
- We use that year's actual stock returns, bond returns, and inflation
- We repeat for each year of your retirement (e.g., 30 times)
- This creates one possible "retirement path"
- We run 1,000+ paths to build a distribution of outcomes
Because we sample entire years together, we preserve the real correlation between stocks, bonds, and inflation. When stocks crashed in 2008, bonds rallied. That relationship is captured in our simulations.
Two Simulation Modes
Historical Bootstrap
Draws full calendar-year tuples (stock return, bond return, inflation) from Shiller's dataset spanning 1871 to present. Preserves real correlations, fat tails, and regime changes. Best for traditional stock/bond portfolios.
Parametric
Models each asset's return distribution independently using Yahoo Finance data. Better for portfolios with non-traditional assets like crypto or individual stocks with limited history. Uses correlated normal distributions.
Why Not Just Use Average Returns?
Deterministic Projection
Assumes a fixed 7% annual return every year.
Pros
- Simple to understand
- Single clear answer
Cons
- Ignores volatility
- Misses sequence risk
- Overconfident
Monte Carlo (Stochastic)
Simulates many possible futures using historical return patterns.
Pros
- Captures real market behavior
- Shows probability of success
- Reveals tail risks
Cons
- More complex output
- Requires interpretation
Understanding Sequence of Returns Risk
Two retirees with identical average returns can have vastly different outcomes depending on when the bad years occur. A market crash early in retirement (when you're withdrawing from a depleting portfolio) is far more damaging than the same crash late in retirement. Monte Carlo simulation captures this "sequence risk" that simple calculators miss entirely.
See It In Action
Watch 20 retirement paths unfold. Green = success, Red = ran out of money.
Click "Run Simulation" to see Monte Carlo in action
Demo uses simplified returns. Starting balance: $1M, withdrawal: $40k/year (4%), horizon: 30 years.
Our Assumptions
Every model makes assumptions. Here are ours—and how you can adjust them.
Withdrawal Strategies
How you withdraw money in retirement matters as much as how much you've saved.
4% Rule (Constant Dollar)
Withdraw 4% of initial portfolio in year 1, then adjust for inflation annually.
Year 1: Portfolio x 0.04 then adjust for CPI
Pros
- Simple and predictable
- Well-researched (Trinity Study)
- Stable income
Cons
- Ignores portfolio performance
- May leave money on table
- 30-year horizon only
Best for: Those who prioritize spending stability over optimization
Variable Percentage Withdrawal (VPW)
Withdraw a percentage of current portfolio using the IRS Single Life Expectancy Table for age-based divisors.
Withdrawal = Portfolio / Life_Expectancy[age] (divisor decreases with age)
Pros
- Adapts to market performance
- Lower failure risk
- Accounts for mortality
Cons
- Income varies year to year
- Requires flexibility
- More complex
Best for: Those comfortable with income variability who want to optimize spending
Guardrails (Guyton-Klinger)
Start with a base withdrawal rate, but adjust up/down based on portfolio performance thresholds.
Base rate +/- adjustments when withdrawal rate hits guard rails
Pros
- Balances stability and adaptability
- Clear decision rules
- Limits extreme outcomes
Cons
- More complex to implement
- Requires monitoring
- Still has failure scenarios
Best for: Those who want some spending stability with market-responsive adjustments
Fixed Percentage
Withdraw a fixed percentage (e.g., 4%) of current portfolio value each year.
Withdrawal = Current Portfolio x Fixed %
Pros
- Never runs out of money
- Simple rule
- Fully market-responsive
Cons
- Highly variable income
- Can drop significantly in downturns
- No inflation protection
Best for: Those with significant flexibility or other income sources
Floor-Ceiling
Withdraw a percentage of your portfolio each year, but clamp the result between inflation-adjusted dollar floor and ceiling amounts.
Withdrawal = clamp(Portfolio x Rate, Floor x (1+i)^t, Ceiling x (1+i)^t)
Pros
- Never runs out of money (like %)
- Limits downside spending cuts
- Caps excessive withdrawals
Cons
- Income still varies within range
- Requires setting initial floor/ceiling
- More complex than fixed percentage
Best for: Those who want percentage-based flexibility with hard spending boundaries
Strategy Comparison
| Metric | 4% Rule | VPW | Guardrails | Fixed % | Floor-Ceiling |
|---|---|---|---|---|---|
| Failure Risk | Medium | Low | Low | None* | None* |
| Income Stability | High | Low | Medium | Low | Medium |
| Upside Capture | Low | High | Medium | High | Medium |
| Complexity | Low | Medium | Medium | Low | Medium |
| Best Use Case | Stability | Optimization | Balanced | Flexibility | Bounded |
*Fixed percentage never "fails" because you always withdraw a % of what remains, but income can drop to very low levels.
Data Sources
Our calculations are only as good as the data behind them. We use authoritative, publicly available sources.
Historical US stock market returns, dividend yields, and earnings from 1871 to present. The gold standard for long-term market analysis.
Data Points Used
- S&P 500 total returns
- Dividend yields
- Earnings data
- 10-year Treasury yields
- CPI inflation
Real-time and historical price data for individual securities, ETFs, and mutual funds for portfolio tracking.
Data Points Used
- Current prices
- Historical prices
- Dividend history
- Basic fundamentals
Official benefit calculators and actuarial tables for Social Security projections.
Data Points Used
- Life expectancy tables
- Benefit formulas
- COLA adjustments
- Bend points
Data freshness: Historical return data is updated monthly. Portfolio prices are fetched in real-time when you load the simulator. Social Security and inflation data are updated annually or as new official figures are released.
Known Limitations
Every model has limitations. Understanding them helps you use the tool appropriately.
Historical Data != Future Returns
Past performance doesn't guarantee future results. The future may differ significantly from any historical period. Our simulations show what would have happened historically, not what will happen.
US-Centric Data
Our primary dataset covers US markets. International diversification benefits and risks aren't fully captured. If you have significant non-US holdings, results may vary.
Simplified Tax Model
We model basic tax treatment (pre-tax vs Roth), but don't account for state taxes, capital gains nuances, tax brackets, or advanced strategies like Roth conversions.
No Behavioral Factors
Models assume you'll follow the plan. In reality, investors often panic-sell in downturns or overspend in bull markets. Behavioral discipline matters.
Healthcare Cost Uncertainty
Healthcare costs are highly variable and can be catastrophic. While we model average healthcare inflation, individual outcomes vary dramatically.
Black Swan Events
Monte Carlo simulations are based on historical volatility. True black swan events (worse than any historical period) aren't captured.
Important Disclaimer
This tool is for educational and planning purposes only. This is not financial advice.
- •We are not registered investment advisors, financial planners, or tax professionals.
- •Results are projections based on historical data and your inputs, not predictions.
- •Consult qualified professionals for personalized advice about your specific situation.
- •You are solely responsible for your financial decisions.
Academic References & Further Reading
The research and resources that inform our methodology.
Foundational Research
The original "4% rule" paper that established safe withdrawal rate research.
Expanded Bengen's work with various asset allocations and withdrawal rates.
Robert Shiller's seminal work on market valuations and the CAPE ratio.
Monte Carlo & Simulation
Overview of how Monte Carlo methods apply to retirement planning.
Technical background on bootstrap methods for financial simulation.
Withdrawal Strategies
Community-developed approach to dynamic withdrawal rates.
The guardrails approach to managing withdrawal rates.
How spending flexibility impacts sustainable withdrawal rates.
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