Most retirement calculators begin with an assumption.
Stocks return 7%. Bonds return 4%. Inflation runs at 2.5%.
Put those numbers into a formula, compound them for 30 years, and out comes a retirement projection.
It's clean.
It's also not how retirement actually happens.
Markets don't deliver their long-term average every year. They crash, recover, stagnate and rally. Inflation changes. Bonds don't always behave the same way. And once you're taking money out of a portfolio, the order in which those things happen can matter as much as the average return itself.
That's why Ask Linc's approach to AI retirement planning doesn't start by asking a language model to guess what the market will return.
Our retirement modeling is built on actual historical market data—including datasets maintained by the Kenneth R. French Data Library and Robert Shiller.
The goal isn't to predict the future from the past.
It's to answer a more useful question:
How would a retirement plan like yours have held up across very different markets we've actually experienced?
Why average-return retirement calculators miss something important
Imagine two retirees with identical portfolios, identical spending and identical average investment returns over retirement.
One gets strong returns during the first five years and experiences a bear market much later.
The other retires directly into a major market decline.
They can end up with dramatically different outcomes.
The second retiree is withdrawing money while the portfolio is down. That means selling a larger share of the remaining assets to fund the same lifestyle—and those assets are no longer there to participate in the eventual recovery.
That's sequence of returns risk.
It's one of the fundamental problems with using a smooth average return for retirement planning. You aren't just accumulating assets anymore. You're simultaneously investing, withdrawing and paying for a cost of living that changes over time.
We've written separately about how Ask Linc stress-tests retirement portfolios across historical market sequences. This article goes one layer deeper: where that history comes from, how we combine it, and why the underlying data matters.
Building a month-by-month picture of market history
Testing a retirement plan requires more than downloading the historical price of the S&P 500.
A real portfolio may contain U.S. stocks, international stocks, bonds and cash. At the same time, the retiree's cost of living is changing with inflation.
Those things interact.
So Ask Linc combines multiple long-running research datasets into one synchronized, month-by-month market history.
From the Kenneth R. French Data Library, we use data representing:
- the broad U.S. stock market
- international developed-market equities
- one-month U.S. Treasury bills as a proxy for cash
French's U.S. data gives us monthly market history beginning in July 1926.
For international equities, we use French's international index data, with history beginning in January 1975.
We combine those with Robert Shiller's historical datasets, from which our model derives:
- long-term U.S. government-bond returns
- month-to-month U.S. inflation
After aligning those sources, Ask Linc currently has 1,200 synchronized monthly observations from July 1926 through June 2026 for the core U.S. series.
That gives the retirement engine one consistent historical environment containing:
U.S. stocks + international stocks + government bonds + cash + inflation.
Why keeping market returns and inflation together matters
It would be easy to take average stock returns from one source, assume a bond return from another, add 2.5% inflation and call it a retirement model.
But we'd be constructing economic environments that never actually happened.
Consider the 1970s.
A retiree didn't experience "stock returns" independently from everything else. Markets, interest rates and unusually high inflation were occurring at the same time.
Our historical retirement modeling preserves that relationship.
When a historical month had weak equity returns, the simulation uses the bond returns and inflation from that historical period too.
When inflation surged, retirement spending in that scenario feels the surge.
When bonds helped diversify a falling stock portfolio, the simulated portfolio receives that benefit.
We don't shuffle everything into an imaginary average year.
History stays connected to history.
That becomes especially important when you're trying to understand sequence of returns risk in retirement. The danger isn't simply that stocks occasionally fall. It's that investment returns, withdrawals and inflation can interact badly at exactly the wrong time.
Then we put your portfolio into that history
The datasets alone aren't the point.
What matters is connecting them to your financial situation.
When Ask Linc analyzes a retirement scenario, it starts with the investments in your actual portfolio and determines what economic exposures they represent.
Holdings can map into historical sleeves such as:
- U.S. equities
- international equities
- nominal U.S. government bonds
- cash
The simulation then applies the historical returns of those asset classes according to your portfolio's modeled allocation.
This solves an important problem with long-term retirement analysis.
Suppose you own an ETF launched in 2018.
It obviously doesn't have market prices going back to 1926.
Pretending otherwise would create fake precision.
But the economic exposure represented by that ETF may have a much longer history.
So rather than manufacture an 80-year price history for a modern fund, Ask Linc models the historical behavior of the asset exposure underneath it.
And when an investment can't be responsibly mapped to one of the historical return series we support, we don't quietly invent a return for it. The limitation is surfaced in the analysis.
One retirement plan, many starting points
Now suppose you're asking whether your portfolio can support $80,000 a year in retirement spending.
A traditional retirement planning calculator might assume:
7% investment return
3% inflation
30 years of retirement
And draw one smooth line.
Ask Linc can ask something different:
What happens if this same plan begins at many different points in market history?
One starting date might lead into a long bull market.
Another might encounter severe inflation.
Another might put a major market decline near the beginning of retirement.
Each starting point creates a different month-by-month sequence of:
- U.S. stock returns
- international stock returns
- bond returns
- cash returns
- inflation
We can then see how the portfolio behaves through each sequence.
Did the requested retirement withdrawals last?
How deep was the drawdown?
How long did recovery take?
If the portfolio was depleted, when did that happen?
This turns retirement planning from a single forecast into a historical stress test of the assumptions behind the plan.
For the more detailed walkthrough of that process, see Stress Testing Your Retirement Portfolio Using Historical Market Data.
Inflation affects both your investments and your spending
Inflation deserves special attention because it affects retirement from two directions.
It influences the economic environment your portfolio is navigating.
And it changes how much money you need to maintain the same lifestyle.
If you're spending $80,000 today, maintaining that purchasing power over a long retirement probably won't mean withdrawing exactly $80,000 every year forever.
By default, Ask Linc's historical retirement scenarios allow spending to rise with the CPI that actually occurred during each historical sequence.
So when a scenario encounters high inflation, the retiree doesn't get historical investment returns paired with conveniently frozen expenses.
The portfolio must support the higher cost of living too.
That lets us test a much more meaningful question:
How did this portfolio behave when both markets and retirement spending were changing at the same time?
What about the years before you retire?
Sequence risk doesn't suddenly appear on your retirement date.
Imagine you're 55 and want to retire at 65.
A major market decline at 64 has a very different effect on your plan than the same decline at 56 followed by eight years of recovery.
That's why Ask Linc can model the entire path from today through the retirement scenario rather than beginning with an assumed portfolio value on your retirement date.
If you're still saving, the analysis can include contributions during those years.
Those contributions experience the same historical returns and inflation environment as the existing portfolio.
Only then do retirement withdrawals begin.
This gets much closer to the question people actually have:
"Given what I have today, what I'm contributing, when I want to retire and what I want to spend, how has a plan like mine held up?"
Our Retirement Answers hub is built around the same idea: start with the straightforward math, then test the assumptions—spending, income, withdrawal rate, retirement age, inflation and investment risk—that can change the answer.
Historical market data also changes how we think about withdrawal rates
The familiar 4% rule is itself based on studying how retirement withdrawals survived historical market periods.
But a single withdrawal-rate rule can't account for every portfolio, retirement length or market sequence.
Ask Linc instead calculates retirement outcomes using the allocation and timeline being analyzed.
That means we can look at a range of withdrawal rates and ask how they actually behaved through the historical sequences available for that portfolio.
It's a more useful way to think about a safe withdrawal rate than treating one percentage as universally safe.
We cover the rule itself—and why longer retirements make its assumptions more important—in The 4% Rule for Early Retirement: Does It Still Work in 2026?.
And if you want to go deeper into how Ask Linc derives withdrawal-rate ranges from historical scenarios, see Historical Withdrawal Rate Analysis for Smarter Retirement Planning.
Historical scenarios, deterministic calculations
There's another reason we built the system this way.
The calculations are reproducible.
Ask Linc doesn't ask an AI model to look at your portfolio and improvise an opinion about whether your retirement plan seems safe.
And our historical scenario engine isn't asking the AI to invent future market returns.
The financial engine performs the calculation using explicit portfolio mappings, historical return sequences, inflation data, contributions and withdrawal assumptions.
The AI's job comes afterward: helping explain the result, the tradeoffs and the assumptions in plain English.
Same portfolio.
Same spending.
Same historical dataset.
Same calculation.
That separation between AI reasoning and fixed financial calculations is a broader principle throughout Ask Linc. We explain it in more detail in Why Determinism Matters in AI Financial Analysis.
What the retirement model can tell us
After running a retirement scenario through historical starting points, Ask Linc can examine questions such as:
- How often did the requested withdrawals last for the full retirement period?
- Which historical starting periods were especially difficult?
- How severe did portfolio drawdowns become?
- When did unsuccessful scenarios run out of money?
- How did different retirement withdrawal rates behave?
- What was the portfolio worth when retirement actually began?
- How much did changing retirement age or contributions alter the result?
That's considerably different from:
"If you earn 7% per year, you'll have $2.4 million."
Instead, the conversation can become:
"Here's how this plan behaved across very different historical markets. Here's where it struggled. And here are the assumptions that matter most."
Does historical success predict retirement success?
No.
That's an important limitation.
If a retirement plan survived 90% of the historical starting windows available for a particular scenario, that does not mean there is a 90% probability it will succeed in the future.
The historical windows overlap. They aren't hundreds of completely independent experiments.
More importantly, the future isn't required to repeat anything we've seen before.
That's why we think of this analysis as a historical stress test, not a prediction.
It answers:
"How would this retirement plan have behaved across the market environments for which we have reliable history?"
It does not answer:
"What will markets return for the next 30 years?"
Nobody has a dataset that can answer that.
Why Kenneth French and Robert Shiller matter to retirement planning
The value of the Kenneth French and Robert Shiller datasets isn't simply that they're widely used in financial research.
It's the depth and consistency of the history they make available.
The period captured by our retirement model includes:
- depressions and recessions
- wars and geopolitical shocks
- high inflation
- low inflation
- very high interest rates
- near-zero interest rates
- severe stock-market declines
- extraordinary bull markets
- long periods when stocks and bonds behaved very differently
A single expected-return assumption compresses all of that into one number.
We'd rather keep the messy parts.
Because when you're deciding whether you can retire, the messy parts may be exactly what determines whether the plan holds up.
That's what historical data adds to AI retirement planning.
Not a crystal ball.
A tougher test.
And when the question is something as important as Can I retire?, we think that's a much better place to start.
Explore the calculations and assumptions behind common retirement questions in the Ask Linc Retirement Answers library, or use Ask Linc with your own accounts, spending, income and retirement goals.