cFIREsim Walkthrough

While FIRECalc grounds retirement planning in actual historical data, cFIREsim approaches the problem differently: it generates thousands of random, probable future scenarios based on statistical assumptions about returns and volatility. This probabilistic Monte Carlo method reveals not just whether your plan worked in specific past periods, but across a vast distribution of plausible futures. cFIREsim asks the question: given what we know about market behavior and volatility, what percentage of randomly generated return sequences would allow my portfolio to survive retirement?

Quick definition

cFIREsim is a free, web-based Monte Carlo retirement simulator that generates thousands of random future return scenarios based on specified mean returns, volatility, and correlation assumptions. Rather than backtest against historical sequences, it models your retirement across a probabilistic distribution of potential futures, revealing the percentage of simulated scenarios where your portfolio survives and examining the range of likely outcomes.

Key takeaways

• Monte Carlo simulation generates thousands of random return sequences, each statistically consistent with your asset allocation's expected risk and return profile.
• Success rate in cFIREsim shows the percentage of simulated scenarios (often 5,000–10,000 runs) where your portfolio never depletes during your retirement window.
• Unlike historical backtesting, Monte Carlo can model custom asset allocations, time-varying returns, or return assumptions that differ from historical averages.
• The distribution of outcomes reveals not just success probability, but also the range of possible portfolio values at retirement end and worst-case drawdown scenarios.
• Comparing cFIREsim results to FIRECalc results reveals whether current market conditions suggest more or less risk than historical averages would predict.

Monte Carlo Simulation: Generating Probable Futures

Flowchart

Monte Carlo methodology originated in physics and gambling analysis—domains where precise calculation of all outcomes is impossible. The insight is elegant: if you generate many random samples from a known probability distribution and run your system through each sample, the distribution of outcomes approximates what could happen.

In retirement planning, you specify assumptions: S&P 500 annual returns average 10% with 18% volatility; bonds average 5% with 6% volatility; they correlate at -0.2 (meaning when stocks fall, bonds tend to rise). cFIREsim then generates 5,000 independent random return sequences, each of 30+ years, that are statistically consistent with these assumptions. One scenario might generate a strong bull market in years 1–5, a crash in years 6–8, and recovery afterward. Another might show the opposite sequence. Yet both emerge from the same underlying probability distribution.

By running your retirement plan through all 5,000 scenarios—withdrawing your planned amounts and monitoring portfolio balance—cFIREsim reveals how many succeed and fail. If 4,750 scenarios end with portfolio balance > 0, your success rate is 95%. If 3,900 scenarios succeed, your success rate is 78%. This distribution-based approach captures the full range of probable futures, not just historical ones.

Setting Up Your Scenario in cFIREsim

Using cFIREsim begins with inputting core parameters mirroring your financial situation. You enter starting portfolio value (in today's dollars), annual spending, number of years in retirement, and your asset allocation by percentage.

The calculator defaults to standard return assumptions: US large-cap stocks at roughly 10% mean return and 18% standard deviation, bonds at 5% return and 6% standard deviation, and cash at 2% with near-zero volatility. Many users accept these defaults, but cFIREsim permits modification: you can input custom expected returns and volatilities if you believe the market environment differs from historical norms.

Asset allocation in cFIREsim typically includes stocks, bonds, and cash. You specify percentages: perhaps 70% stocks, 25% bonds, 5% cash. The calculator then blends the return distributions of each asset class based on their correlation structure to produce a combined portfolio distribution. A higher stock percentage increases expected return but also portfolio volatility, while higher bond allocation reduces both.

Inflation handling works similarly to FIRECalc: you typically assume 3% annual inflation, and your spending amount increases accordingly year-to-year. Some users instead input spending in today's dollars and let the calculator handle inflation adjustment automatically.

Running Simulations and Interpreting Success Rates

Once inputs are set, cFIREsim generates thousands of return sequences and runs your retirement plan through each. The output aggregates results, showing you:

• Success rate: percentage of simulations where portfolio balance never hits zero through your full retirement window
• Median outcome: the middle portfolio balance at retirement end (when ranked across all simulations)
• 10th percentile outcome: the value below which only 10% of simulations fell (worst-case region)
• 90th percentile outcome: the value above which only 10% of simulations ended

This distribution reveals something FIRECalc cannot: the full range of probable outcomes, not just which historical periods worked. Where FIRECalc shows "the worst historical case was a 35% portfolio depletion in 1929," cFIREsim shows "in 5% of probable futures, your portfolio drops below 20% of starting value; in 50% of cases, it's at 140% or higher by retirement end."

A 95% success rate in cFIREsim carries different implications depending on the distribution. A 95% rate where the 10th percentile outcome is 50% portfolio value (portfolio still intact) suggests more comfort than a 95% rate where the 10th percentile is 5% value (barely survives). cFIREsim forces you to examine this distribution explicitly.

The Advantage: Testing Non-Historical Scenarios

cFIREsim's primary advantage over FIRECalc is flexibility in scenario design. You can test allocations that haven't existed for a full century. For instance, if you plan a 40% US stocks / 40% international stocks / 20% bonds allocation, historical S&P 500-based backtesting doesn't fully capture international performance patterns. cFIREsim, by contrast, can model custom return assumptions for international equities, generating scenarios specific to your actual portfolio structure.

You can also explore scenarios that differ from historical norms. If you believe future equity returns will be lower than historical 10% averages—say, 8%—you can modify cFIREsim's assumptions accordingly and rerun. This flexibility lets you stress-test your plan against different market regime assumptions: higher inflation, lower bond returns, higher equity volatility.

Similarly, if you expect significant life changes, cFIREsim permits variable spending: you might spend heavily in early retirement (travel, leisure) and less in later years (healthcare costs stabilize), or vice versa. You can model one-time large expenses (wedding, home renovation) at specific years and see how they affect portfolio survival.

This flexibility transforms cFIREsim from a retirement calculator into a scenario-testing laboratory where you can interrogate your plan against a wider range of assumptions.

Customizing Return Assumptions

Many users accept cFIREsim's default return expectations, which align with historical averages. But informed investors might question these assumptions. After a decade of historically high equity returns and low bond yields, some planners expect mean reversion—future returns reverting toward historical averages, but potentially below recent conditions.

cFIREsim permits adjusting expected annual returns by asset class. You might lower stock expectations from 10% to 8%, bond expectations from 5% to 3%, or increase volatility assumptions if you believe markets are more unstable. These adjustments rerun the simulation, generating new success rates under your custom assumptions.

Volatility (standard deviation) settings also matter substantially. The default 18% stock volatility reflects roughly historical ranges, but extreme market conditions—2008-style crashes or 2020-style volatility spikes—suggest occasional periods exceed 25–30% volatility. Increasing volatility assumption reduces success rates because larger swings mean greater sequence-of-returns risk.

The correlation between asset classes (how much stocks and bonds move together) also influences outcomes. The default -0.2 correlation assumes bonds and stocks tend opposite: bonds gain when stocks crash. But during stagflation periods (1970s), bonds and stocks fell together, breaking this assumption. cFIREsim lets you model different correlations, testing scenarios where traditional diversification breaks down.

Asset Allocation Sensitivity and Outcomes

Like FIRECalc, cFIREsim reveals that asset allocation is the primary lever affecting success rates. But because cFIREsim can test custom allocations, the analysis becomes more granular.

A user might test:

• 100/0/0 (100% stocks): High expected return (10% average), high volatility. Success rates depend heavily on luck: if early years contain a crash, portfolio faces severe stress. Monte Carlo typically shows 85–92% success for moderate withdrawal rates, but worst-case scenarios can be catastrophic.
• 70/25/5 (balanced growth): Moderate return (~8.5% average), reduced volatility. Success rates typically 93–95% for standard withdrawal rates. Less dramatic worst-case scenarios.
• 60/40/0 (traditional balanced): Conservative return (~8% average), volatility cushioned by bonds. Success rates often 95%+ but higher capital consumption means less final portfolio wealth.
• 50/40/10 (income-focused): Lower return (~7% average), bond income provides spending buffer. Success rates very high (96%+) but lower growth means portfolio may not beat inflation long-term.

The non-linear relationship matters: moving from 50/50 to 60/40 might improve success by 2 percentage points, while moving from 70/30 to 100/0 might reduce it by 8 points. cFIREsim's rapid recalculation lets you explore these tradeoffs interactively.

Withdrawal Rate Sensitivity

cFIREsim also reveals how sensitive success rates are to withdrawal amount. Testing a plan with $50K annual spending and then $55K spending shows the cost of each additional dollar withdrawn. Often, raising spending from $50K to $55K (10% increase) reduces success rate from 96% to 89% (7-point drop)—a non-linear penalty that accelerates at higher withdrawal rates.

This sensitivity analysis helps determine your true safe withdrawal amount. You might find that $48K annual spending yields 97% success (very conservative), while $52K yields 93% (acceptably safe), and $60K yields 78% (risky). This granular view lets you optimize between early retirement comfort and portfolio security.

The sequence of withdrawals also matters. cFIREsim typically assumes withdrawals increase with inflation each year. But you can instead model declining spending in early retirement (assuming you'll slow down and spend less as you age), which improves success rates because your portfolio faces less strain in later years.

Portfolio Distribution and Probability Density

Beyond simple success percentages, cFIREsim provides value in revealing the distribution of outcomes. A 95% success rate alone tells you little about the width of the confidence interval.

Imagine two plans, each with 95% success:

• Plan A: 10th percentile outcome is 80% portfolio value; 90th percentile is 250%
• Plan B: 10th percentile outcome is 5% portfolio value; 90th percentile is 350%

Both succeed 95% of the time, but Plan A offers far less portfolio volatility. In 90% of scenarios, your balance stays between 80–250% of starting value (tight range). Plan B's 5–350% range means you face serious portfolio risk in worst cases, even though the success percentage matches. cFIREsim's distribution view forces this distinction.

Some users focus on the median outcome (50th percentile)—the balance you'd expect to have at retirement end if the average future occurs. A median of 180% means, on balance, your portfolio grows substantially. A median of 95% means it roughly tracks inflation. Comparing median outcomes across allocation scenarios reveals which strategies offer better growth without sacrificing safety.

Comparing cFIREsim to FIRECalc Insights

When you run the same scenario through both tools, interesting patterns emerge. FIRECalc shows success rates grounded in specific historical periods, while cFIREsim shows probabilistic outcomes. Sometimes they align closely (both show ~94% success); other times they diverge.

High divergence often signals information: if FIRECalc shows 92% success but cFIREsim shows 87%, it suggests current market conditions (higher valuations, lower yields, higher volatility) make the probabilistic future less favorable than history. This might indicate you should raise your risk tolerance or lower spending expectations.

Conversely, if cFIREsim shows 96% success while FIRECalc shows 90%, current conditions appear better than historical norms, offering you safety margin. You might be able to withdraw slightly more or retire slightly sooner.

Running both tools systematically provides information neither delivers alone. FIRECalc grounds you in empirical reality; cFIREsim explores probabilistic futures. Together, they create a robust retirement confidence framework.

Real-World Walkthrough: A Specific Example

Let's trace through a concrete scenario using cFIREsim:

Inputs:

• Starting portfolio: $1.2M
• Annual spending: $45K
• Retirement window: 35 years
• Allocation: 70% stocks, 25% bonds, 5% cash
• Inflation: 3% annually

Default assumptions:

• Stocks: 10% return, 18% volatility
• Bonds: 5% return, 6% volatility
• Cash: 2% return, 0% volatility
• Stock-bond correlation: -0.2

Output after 5,000 simulations:

• Success rate: 94%
• Median outcome: 142% portfolio value (portfolio grows substantially)
• 10th percentile: 65% (portfolio reduced but intact)
• 90th percentile: 268% (strong growth scenario)

This result suggests the plan is sound. In 94 of 100 probable futures, the portfolio never depletes. In the median case, you end retirement with $1.7M. Even in worst-case regions (10th percentile), your portfolio drops only to $780K—still substantial.

Now modify one variable—increase annual spending to $50K:

Revised output:

• Success rate: 89%
• Median outcome: 98% portfolio value (portfolio roughly stable)
• 10th percentile: 35% (portfolio depleted nearly halfway)
• 90th percentile: 195% (still strong but reduced)

The additional $5K annual withdrawal reduces success rate by 5 percentage points and median outcome from 142% to 98%. The worst-case scenario becomes substantially riskier (35% vs 65%).

Now test a more conservative allocation—60/35/5:

Output (spending back to $45K):

• Success rate: 95.5%
• Median outcome: 128% portfolio value
• 10th percentile: 72% (better than 70/25/5)
• 90th percentile: 205% (lower growth but acceptable)

The bond allocation improves worst-case outcomes and success rates, though at the cost of median growth. This comparison lets you consciously choose your risk tolerance.

Interpreting the Confidence Interval

cFIREsim's distribution of outcomes naturally creates a confidence interval. If you trust the underlying return assumptions, the 90% confidence interval (the range between 10th and 90th percentiles) represents where you'd expect to land in 9 out of 10 probable futures.

A wide confidence interval (e.g., 30–200% portfolio value at retirement end) indicates high uncertainty—outcomes vary widely depending on which random sequence of returns occurs. A narrow interval indicates stable outcomes across scenarios, which is reassuring.

Narrow intervals typically come from conservative allocations with long time horizons (more return smoothing) and low withdrawal rates (less portfolio stress). Wide intervals emerge from aggressive allocations, short retirement windows, or high withdrawal rates, where sequence-of-returns risk dominates.

The interval width is information: if your plan produces wide intervals, you're relying on good luck. If intervals are narrow, your plan is robust to variations in future returns.

One-Time Expenses and Variable Spending

Real retirement rarely involves fixed annual spending. cFIREsim permits modeling variable spending: you might plan to spend heavily in years 1–10 (travel, leisure), moderate spending in years 11–25 (routine expenses), and lower spending in years 26+ (less mobility).

You can also model one-time major expenses. Perhaps you plan a $100K round-the-world trip in year 5, or expect to need $200K for a child's wedding in year 8. cFIREsim lets you enter these as one-time draws, which impacts portfolio balance and potentially success rates.

Some users model required spending (housing, utilities, healthcare) as base amount, and discretionary spending (travel, hobbies) as variable. If markets crash and discretionary spending is flexible, success rates improve. cFIREsim can model this flexibility by varying spending based on portfolio performance.

Limitations of cFIREsim and Monte Carlo

Monte Carlo simulation's primary limitation is its dependence on assumptions. If historical volatility of 18% doesn't hold in the future—if markets become far more unstable—all outputs become unreliable. The simulation is only as good as its return and volatility assumptions.

Additionally, cFIREsim's default assumptions may not match your specific portfolio. If you hold significant alternatives (REITs, commodities, private equity), whose returns and correlations differ from stocks and bonds, the simulation may misrepresent your actual risk profile.

cFIREsim also assumes your withdrawal rate is deterministic—you withdraw your planned amount each year. In reality, most retirees exhibit spending flexibility: they reduce spending during crashes and increase during booms. This real behavior improves actual success rates beyond what cFIREsim predicts (assuming fixed spending). Conversely, some retirees lock in spending inflexibly, accepting the risk that cFIREsim models.

The tool also cannot predict catastrophic tail risks: pandemics, wars, major policy changes. These events exceed the probability space cFIREsim explores, operating outside the statistical models.

Connection to Modern Portfolio Theory and Risk Management

cFIREsim embodies principles from Modern Portfolio Theory: portfolio risk is measured by volatility of returns, and diversification reduces this volatility. By modeling return distributions and correlations, cFIREsim quantifies the portfolio-level risk that emerges from combining asset classes.

The tool also reflects insights from behavioral finance: it reveals that success depends largely on luck (which random return sequence you experience), not just on your numbers. A disciplined saver with a solid withdrawal rate might still face portfolio exhaustion if unlucky with early market crashes.

Common Mistakes Using cFIREsim

Many users set unrealistically high return expectations or low volatility assumptions, leading to overly optimistic success rates. If you assume 12% stock returns and 3% volatility (far better than historical), your success rate will be artificially high.

Others ignore the distribution of outcomes and focus only on success percentage. A plan with 95% success but a 10th-percentile portfolio of 5% is riskier than a plan with 93% success and 50% 10th-percentile outcome.

Some fail to adjust assumptions as market conditions change. After equity valuations rise significantly, you might reasonably lower future return expectations. Rerunning cFIREsim annually with updated assumptions helps you track whether your plan remains sound.

Finally, many users treat 95% success as a hard threshold rather than a decision point. A 90% success rate with flexible spending might be more practical than a 95% rate that requires excessive spending discipline. The number should inform your decision, not dictate it.

FAQ

How many Monte Carlo simulations does cFIREsim run?

Typically 5,000–10,000 simulations per scenario. More simulations increase accuracy but add computational time. For most purposes, 5,000 simulations provides adequate precision (the standard error in success rate calculation is roughly 0.7% at 5,000 runs).

Can I adjust correlation between assets in cFIREsim?

Most versions of cFIREsim allow adjusting correlation assumptions. Default is typically stock-bond correlation of -0.2 to -0.3. You can test scenarios where correlation is higher (say, 0.1) to model periods where stocks and bonds move together, reducing diversification benefit.

How do I compare cFIREsim results to FIRECalc results?

Run the same core scenario (portfolio size, spending, allocation, years) in both tools. cFIREsim will show a probabilistic success rate; FIRECalc will show historical success rate. If they differ meaningfully (cFIREsim shows 88%, FIRECalc shows 93%), it suggests current market conditions differ from historical norms—investigate why.

What if I expect my spending to change over time?

cFIREsim permits variable spending: you can model lower spending in later years, higher in early years, or specific one-time expenses at particular years. This creates more realistic projections than fixed-spending assumptions.

Should I use cFIREsim or FIRECalc?

Use both. FIRECalc grounds you in historical data and worst-case periods (Great Depression, 2008). cFIREsim explores probabilistic futures and custom scenarios. Together, they provide comprehensive retirement planning assessment.

How sensitive are results to inflation assumptions?

Very sensitive. Increasing inflation assumption from 3% to 4% typically reduces success rates by 3–8 percentage points because your withdrawals must increase faster, straining the portfolio. Ensure your inflation assumption is realistic for your expected expenses.

What confidence level should I target?

Most advisors recommend 90%–95% success rate. Below 80% requires either high spending flexibility, side income, or willingness to return to work. Above 95% may represent overly conservative spending that reduces retirement quality.

Related Concepts

Monte Carlo Methods in Finance — The theoretical foundation underlying cFIREsim's approach.

Volatility and Standard Deviation — Core inputs to cFIREsim's return distribution modeling.

Asset Allocation and Diversification — The primary mechanism through which cFIREsim improves success rates.

FIRECalc Explained — Historical backtesting as complement to Monte Carlo simulation.

Sequence-of-Returns Risk — The core risk cFIREsim quantifies through distribution of outcomes.

Summary

cFIREsim brings Monte Carlo simulation—a powerful probabilistic tool—into the hands of individual retirement planners. By generating thousands of random return sequences consistent with your asset allocation's risk and return profile, the tool reveals what percentage of probable futures would support your retirement plan.

Unlike FIRECalc's historical grounding, cFIREsim's flexibility permits custom scenarios: non-historical allocations, variable spending, inflation adjustments, and return assumptions tailored to current market conditions. This flexibility transforms it into a scenario-testing laboratory where you can interrogate your plan against a broader range of assumptions.

The key insight cFIREsim provides is distributional: success doesn't mean a single success percentage, but rather a distribution of probable outcomes. A 95% success rate where worst cases still leave you with 60% portfolio value is fundamentally different from one where worst cases drop to 5%. Understanding this distribution—through quantiles and confidence intervals—is central to sound retirement planning.

Used alongside FIRECalc, cFIREsim creates a comprehensive assessment: historical backtesting ensures your plan would have survived past catastrophes; probabilistic modeling reveals your exposure to future variations in returns and volatility.

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