Real Market History vs. Average Retirement Forecasts

“Stocks return 7%, bonds return 4%, and inflation runs at 2.5%” is tidy enough for a spreadsheet.

It is also a market that nobody has ever lived through.

Real markets arrive in sequences. Stocks fall while inflation is high. Bonds sometimes cushion the damage and sometimes fall too. Recoveries can begin quickly or take years. Once a retiree is selling investments to fund spending, those relationships matter more than the long-run average.

Ask Linc tests your plan against those uneven returns. It uses synchronized monthly market and inflation data to replay the plan through historical sequences instead of drawing one smooth forecast.

Run the free retirement calculator against real market history.

An average return deletes the part that can break a retirement

Suppose two portfolios both earn a 6% annualized return over 20 years. The first gets strong returns early and a crash late. The second gets the crash immediately, while the retiree is withdrawing money.

The averages may match. The retirement outcomes may not.

Early withdrawals sell more shares after a decline and leave less invested for the recovery. Inflation can deepen the problem by forcing the retiree to withdraw more dollars at exactly the wrong time. This is sequence-of-returns risk, and averaging is precisely what hides it.

A historical model preserves the order: the return, inflation, contribution, or withdrawal that occurred in one month leads into the next month from the same period.

The data behind Ask Linc’s historical record

Ask Linc combines long-running research datasets into one monthly history for the asset exposures used by the retirement engine.

The Kenneth R. French Data Library supplies 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. market history begins in July 1926. International developed-market data begins later, in January 1975.

Ask Linc also uses Robert Shiller’s historical data to derive:

Long-term U.S. government-bond returns
Month-to-month U.S. inflation

After alignment, the core U.S. record currently contains 1,200 synchronized monthly observations from July 1926 through June 2026.

The word synchronized matters. A month of weak stock returns stays attached to the bond return, cash return, and inflation from that same month.

Why returns and inflation stay together

It would be easy to take average stock returns from one source, assume an average bond return, add constant inflation, and call the result a retirement scenario.

That would create a history that never happened.

When the model replays a historical month:

Stocks receive that month’s return
Bonds receive the return derived for the same period
Cash receives the period’s Treasury-bill return
Retirement spending changes with the inflation from that sequence

This preserves the combinations that made certain periods difficult. High inflation is not paired with conveniently frozen spending. A stock-market decline is not automatically paired with an average bond return. History remains connected to history.

A modern fund does not need an invented 1926 price

Your ETF may have launched five years ago. The economic exposure underneath it can have a much longer record.

Ask Linc does not fabricate an 80-year price history for a new fund. When accounts are connected, it identifies the holding’s economic exposure—such as U.S. equities, international equities, nominal government bonds, or cash—and maps that exposure to the historical series the model can support.

This distinction prevents a recent ticker from limiting the analysis to recent markets. It also creates a responsibility: the model should disclose what it could not map cleanly.

If a holding is ambiguous, concentrated, or represents an asset without a suitable history, guessing would make the result look more complete than it is. Ask Linc can instead leave that money out of the historical test and show the missing amount and mapping confidence.

The international-data gap is visible, not hidden

International developed-market history begins in 1975, decades after the U.S. series. A retirement test beginning in 1926 cannot observe international returns that were not recorded in the selected dataset.

For the months without that international series, Ask Linc uses the U.S. market return as a fallback and identifies the limitation. On the current record, the affected periods are July 1926 through December 1974 and January through June 2026.

That is not the same as claiming international stocks behaved like U.S. stocks. It is an explicit modeling gap. The result can still use the complete U.S. record, but those fallback months cannot teach you about the diversification benefit—or cost—of investing overseas.

A transparent model tells you where the history is strong, where it is substituted, and how much of the portfolio it could actually test.

The years before retirement use history too

Some calculators begin at retirement with a portfolio value that was grown at one assumed rate. That smooths away the accumulation risk before withdrawals even begin.

Ask Linc can simulate annual contributions between the current age and the proposed retirement date through the same historical sequence. A person eight years from retirement does not receive eight identical returns; contributions buy into the markets that occurred in those months.

This matters when you compare retirement dates. Working two more years changes more than the number of withdrawals:

More contributions enter the portfolio
The existing assets experience two additional years of returns
The retirement horizon becomes shorter
The bridge to Social Security and Medicare becomes shorter

Test retirement at 60, then move only the date. The difference between the runs shows what the additional working years did in the same historical framework.

One plan, many historical starting points

The model moves the same plan across every overlapping historical window long enough to cover it.

A 30-year retirement can begin at more historical dates than a 50-year retirement because every tested window needs a complete horizon. Each new start month creates a different path of returns and inflation.

Across those windows, the model can examine:

How many plans funded spending for the full horizon
Which starting periods were especially difficult
When unsuccessful plans depleted
How severe portfolio drawdowns became
How much spending the history supported
How a later retirement age or lower spending changed the outcome

The free calculator shows the result for a preset asset mix. Connecting accounts lets the same engine use the holdings it can identify instead of assuming the entire portfolio.

This is historical replay, not Monte Carlo

A Monte Carlo model draws returns from an assumed probability distribution and creates many synthetic futures. Ask Linc’s historical engine does something different: it replays month-by-month sequences that occurred.

Both approaches have limits.

Historical replay preserves real relationships among returns and inflation, but it is constrained to one recorded past. Monte Carlo can create paths outside that record, but its results depend on the distribution and relationships assumed.

Ask Linc’s result should be read as: “How did this plan behave across the complete historical windows available?” It should not be read as a prediction that the next retirement will repeat one of them.

The language model does not invent the returns

Ask Linc uses AI to understand the question, gather the relevant financial facts, explain the result, and identify tradeoffs. The financial engine performs the retirement calculation from explicit inputs and historical series.

The language model is not asked to guess next year’s return or improvise a survival rate. That separation matters because a polished explanation is not evidence that the arithmetic was reproducible.

A trustworthy result should make it possible to inspect:

The balances and spending used
The retirement and Social Security ages
The asset mix or holdings mapped
The historical range tested
The money or exposures left out
The calculation behind the conclusion

For the broader design principle, see why deterministic calculations matter in AI financial analysis.

What historical modeling still cannot promise

A long record is better than a smooth average, but it is not complete knowledge.

The future can be different. Historical replay cannot contain a market structure or inflation path that has never occurred.
The windows overlap. They share most of their observations and are not independent probability trials.
The data is U.S.-centered. Results should not be treated as universal evidence for every country or portfolio.
Asset mapping is an approximation. A modern investment may not behave exactly like its broad historical exposure.
The free model omits important planning details. Taxes, account types, Roth conversions, required distributions, healthcare quotes, fees, home equity, and scheduled one-time expenses require separate work.

If a plan lasted in 90% of the historical windows, the careful conclusion is that it lasted in 90% of the windows tested. It is not that the household has a 90% personal probability of success.

Use the history to compare decisions

Historical modeling is most useful when the comparison is controlled.

Run the retirement age and spending you actually want.
Keep the date fixed and test floor, planned, and high-cost spending.
Return to planned spending and work one additional year.
Test the nearest more-conservative asset mix you could genuinely hold.
Write down which change protected the weak windows.

The goal is not to find the input combination with the prettiest result. It is to see which real decision—spending, timing, contributions, Social Security, or allocation—creates enough margin.

Run the free retirement calculator with your own numbers.

Why Real Market History Beats an Average Retirement Forecast