Monte Carlo Simulation for Retirement Planning: What It Is and Why It Matters#

Imagine you're about to retire with $1,000,000 saved and a plan to withdraw $50,000 per year for 30 years. Will your money last? A simple calculation might say "yes" based on average returns — but retirement doesn't deliver average returns every year. Markets crash. Inflation spikes. Some retirees get 20 years of exceptional growth; others face a brutal first decade that permanently damages their portfolio.

Monte Carlo simulation was developed to address this uncertainty. It doesn't tell you what will happen — it tells you what could happen across hundreds of possible futures, and how likely each outcome is. Used properly, it's one of the most honest and useful tools in retirement planning.

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What Is Monte Carlo Simulation?#

Monte Carlo simulation is a computational technique that generates a large number of random scenarios — typically 500–10,000 — and measures how many result in success (not running out of money) versus failure (running out of money before death).

Each simulated scenario randomly varies market returns year by year, based on the historical distribution of returns (average return, standard deviation, and correlation between assets). Some runs experience great early returns; others face immediate market crashes. All of this is based on historical patterns.

The output tells you something like: "Under 1,000 simulations, your plan was successful in 847 of them — an 84.7% success rate."

This is far more useful than "your plan assumes 6% returns and should be fine" — because it captures the range of possible experiences, not just the average.

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How Monte Carlo Simulation Works#

Step 1: Input Your Data#

A Monte Carlo tool typically needs:

• Starting portfolio value
• Annual spending (withdrawals)
• Asset allocation (stock/bond/cash split)
• Retirement age and planning horizon (how many years)
• Additional income sources (CPP, OAS, pension, part-time work)
• Inflation rate assumption

Step 2: Generate Random Return Sequences#

The simulation draws randomly from a distribution of annual returns for each asset class. Based on historical data, Canadian equities might have an average real return of 5–6% with a standard deviation of 15–18%. The simulation randomly selects a return for each year from this distribution, for thousands of trials.

Each trial generates a different return sequence — some start well, some start with a crash, some are volatile throughout.

Step 3: Run the Plan Through Each Sequence#

For each simulated return sequence, the tool runs your retirement plan: starting with your portfolio, subtracting annual withdrawals (adjusted for inflation), adding portfolio growth, factoring in CPP/OAS income starting at your chosen ages, and checking whether the portfolio reaches zero before the end of the planning horizon.

Step 4: Count Successes and Failures#

At the end of all simulations, the tool counts how many reached the end of the planning horizon with money remaining (success) and how many depleted the portfolio (failure). The success rate is reported as a percentage.

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Interpreting the Success Rate#

A 90% success rate means: in 90 out of 100 simulated retirements, your plan worked (didn't run out of money). In 10 out of 100, it failed.

But what does "90% confidence" feel like in practice?

A common target for retirement planning is 85–90% success, not 100%. Planning for 100% confidence usually requires either enormous savings or very low spending — neither of which may be practical or desirable.

What happens in the 10% failure scenarios? It depends. Many simulations "fail" in the final few years of a long horizon — meaning you run out at age 92 in a plan designed to age 95. You might still be fine in practice if you can adjust spending. Others fail earlier and require more drastic action.

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Monte Carlo vs Historical Sequence Analysis#

An alternative to Monte Carlo is historical sequence analysis — running your plan through every actual historical return sequence (e.g., every rolling 30-year period from 1900 to today). The famous "4% rule" research used this method.

The two methods are complementary. If your plan succeeds in both Monte Carlo (90%+ success) and historical sequence analysis (surviving all historical periods), it has robust confidence across different methodologies.

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The Sequence of Returns Problem Visualized#

Monte Carlo simulation powerfully illustrates sequence-of-returns risk. Compare two simulated retirees with identical 7%/year average returns over 20 years but different sequences:

Retiree A: Good early returns, bad late returns

• Years 1–5: +20% average
• Years 6–10: +8% average
• Years 11–15: −5% average
• Years 16–20: +6% average
• Portfolio survives well — early growth protected withdrawals

Retiree B: Bad early returns, good late returns

• Years 1–5: −8% average
• Years 6–10: +12% average
• Years 11–15: +15% average
• Years 16–20: +8% average
• Portfolio may fail — early losses combined with withdrawals created a "hole" that even later gains couldn't fill

Both averaged 7%/year. Only the sequence differed. Monte Carlo captures this risk by testing across thousands of possible sequences.

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What Improves Your Monte Carlo Success Rate?#

If your success rate is lower than you'd like, here are the levers:

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Limitations of Monte Carlo Simulation#

Monte Carlo is powerful but not perfect. Know its limitations:

1. Historical Data May Not Predict the Future#

Monte Carlo typically assumes future returns will be drawn from a distribution based on historical data. But structural changes — prolonged low-growth periods, high-valuation starting points, demographic shifts — may mean future returns are systematically lower than historical averages.

Some Monte Carlo tools allow you to reduce the expected return assumption (e.g., apply a 1% "valuation adjustment") to address this concern.

2. Returns Are Often Assumed Independent Year to Year#

Many basic Monte Carlo models assume each year's return is random and independent of prior years. In reality, markets have momentum, mean reversion, and correlation effects. More sophisticated models (block bootstrapping, correlated return models) address this but are less common in consumer tools.

3. Constant Spending Assumption May Be Unrealistic#

Many models assume constant inflation-adjusted spending throughout retirement. In reality, spending often follows a "smile" pattern — higher in early active years, lower in mid-retirement, higher again for healthcare in late life. Modelling spending realistically affects results.

4. Behavioural Response Is Not Modelled#

In a real market crash, many retirees would reduce spending, return to part-time work, or adjust strategy. Monte Carlo models usually don't capture this adaptive response. A plan with an 80% success rate in simulation might have a much higher real-world success rate because retirees would adapt to adversity.

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Using Monte Carlo Practically#

For Canadian retirees, Monte Carlo is most useful for:

1. Stress-testing a withdrawal rate: Is 4% safe for your situation, or should you aim for 3.5%?
2. Evaluating CPP/OAS delay strategies: Does delaying CPP to 70 significantly improve your success rate?
3. Testing asset allocation: How much does moving from 60/40 to 70/30 affect your risk and success rate?
4. Sizing an annuity: How much guaranteed income do you need to eliminate tail risk?
5. Evaluating phased retirement: Does retiring at 60 vs 62 meaningfully change the probability of success?