Does the 4% rule survive forward-looking forecasts?

The question

William Bengen's 1994 paper showed that a 4% inflation-adjusted withdrawal from a 50/50 stock-bond portfolio survived every rolling 30-year window in US history from 1926 to 1976. The "4% rule" became shorthand for safe retirement spending.

That study used historical US returns. Major asset managers — J.P. Morgan, BlackRock, Vanguard, GMO, Charles Schwab, Invesco — now publish forward-looking Capital Market Expectations: their best estimates of returns over the next 7 to 20 years. These forecasts often differ materially from historical averages, especially at current valuation levels.

So the question is concrete: when you swap historical returns for forward-looking expectations, does the 4% rule still hold?

This piece runs the same retirement plan through 10,000 Monte Carlo simulations under each of the six publicly-available forecasts and reports the result.

The plan

One canonical scenario, identical across all six forecasts:

Holding volatility and correlations constant across runs is deliberate: it isolates the effect of each firm's return expectation. Some firms publish their own volatility and correlation estimates; others do not. Using one consistent set lets the comparison be apples-to-apples on the input that varies most across forecasts.

The forecasts

One sentence per source. Each links to its publicly available original. We use only data that has been reported publicly by the firm itself or in widely-circulated financial press; we do not source from paywalled or registration-gated portals.

Of the six, GMO is the outlier — a deliberate one. GMO's framework is mean-reverting: it adjusts current asset prices toward historical fair value over a 7-year horizon. With US large-cap equities trading at elevated cyclically-adjusted earnings multiples (Shiller P/E above 30 in late 2025), GMO's model produces negative real return forecasts for US stocks. The other five firms use building-block approaches that pencil in more modest valuation adjustments.

Result 1: success rate

Probability the portfolio survives 30 years without depletion, by forecast:

Five of six forecasts produce a 100% success rate (or 99.99% in J.P. Morgan's case — one path of 10,000 failed). GMO produces 93.2%, meaning 683 of 10,000 simulated paths exhausted the portfolio before year 30.

This may be the surprising result. The forward-looking forecasts are lower than the long-run historical 1926–2025 averages (~10.2% nominal for US equity) — yet the 4% rule still holds for five of six. Why?

Because the 4% rule's safety margin against historical returns is wide. Bengen 1994's 100% historical success rate at 4% on a 50/50 portfolio held with margin to spare; even a 5–6% withdrawal would have survived most rolling windows. Reducing the expected return from 10% to 4–7% nominal shrinks that margin without erasing it.

GMO breaks rank because its US equity forecast goes negative. A portfolio whose largest single holding has a negative real expected return is a different mathematical object: not a smaller margin, but one that drift in the wrong direction.

Result 2: terminal balance

The success-rate chart conceals most of what's interesting. Below: the distribution of terminal balances (after 30 years, in nominal US dollars) under each forecast. The dot is the median; the dark band is the 25th–75th percentile; the light band is the 10th–90th percentile.

The medians:

The headline number: median terminal balance varies 11.2× across forecasts, from $370k (GMO) to $4.14M (J.P. Morgan), for an identical plan.

Even setting GMO aside, the five remaining forecasts span 1.47× — Vanguard's $2.82M to J.P. Morgan's $4.14M, a $1.32M difference in expected legacy balance. The same plan, the same allocation, the same withdrawal, and yet a $1.3M swing depending on whose forecast you anchor to.

Why GMO is different

GMO is not pessimistic for its own sake. The firm's framework is explicit and reproducible: forecast US equity returns by reverting current valuations (P/E, Shiller CAPE, dividend yield) to a long-run mean over seven years.

At late-2025 valuation levels — Shiller CAPE above 30, well above the 1926–2024 historical mean near 17 — that mean-reversion implies several years of below-average price returns. Combined with mediocre starting dividend yields, the model produces a 7-year US equity total return forecast that GMO publishes as a negative real number. After conversion to nominal at a 2.5% inflation assumption, US equity is still roughly −3.5% per year.

For a portfolio that's 45% US equity, that is the difference between marginally building the portfolio and slowly bleeding it. The 4% rule does not survive a sustained negative real return on the largest equity holding, because the inflation-adjusted withdrawal grows at 2.5% per year while the asset shrinks. Sequence-of-returns risk amplifies it.

GMO's model has been historically right and historically wrong. They were notably correct that the late-1990s tech bubble would mean-revert, and notably early — and therefore wrong for years — about the 2010s bull market. We make no claim about whether their current forecast will turn out to be right. The point is that their model produces these numbers, the model is publicly documented, and a 4% retirement plan needs to address what happens if their forecast is correct.

What this implies for retirees

Three takeaways. None are advice — they're observations from the data.

1. The 4% rule's robustness is broader than its critics often claim.

It survives across five of six current published forecasts despite the forecasts being substantially below historical averages. If your prior was that "lower future returns kill the 4% rule," this data says: not exactly. Lower returns shrink the margin and the bequest balance, but they don't break the survival outcome under most forecasts.

2. But it can break if a forecast is right that current US equity valuations are unsustainable.

GMO's forecast is the only one that does. If you believe equity returns over the next decade will reflect mean reversion from elevated valuations rather than continued historical-average growth, the 4% rule has real failure risk and you may want a lower withdrawal rate or a more conservative allocation than 60/40.

3. The bequest dimension is the more useful framing.

"Will my plan survive?" is binary and not very informative when the answer is 100% under most forecasts. "What is the distribution of my legacy balance?" is the question with substantive variance: an 11× range under different forecasts, and a 1.5× range even excluding the outlier. Retirees with bequest motives, or with longer-than-30-year horizons, get more decision-relevant signal from this view than from the headline success rate.

Methodology

Engine

QuantCalc's Monte Carlo simulation engine, written in C, with correlated lognormal asset-return modeling via Cholesky decomposition of the published correlation matrix. Withdrawals are inflation-adjusted at a 2.5% deterministic CPI rate consistent with the post-1990 anchored inflation regime; further methodology on inflation available. Each simulation runs the full month-by-month portfolio path for 360 months (30 years).

Sampling

Pseudo-random (Mersenne Twister) draws were used as the canonical run rather than the engine's faster default Sobol quasi-Monte Carlo, because QMC is known to converge faster on means but to bias estimates of tail probabilities. The article numbers come from pseudo-random sampling. We re-ran the same scenario under both QMC variants as a robustness check; the comparison appears below.

The success rates are stable across all three sampling regimes. The maximum cross-regime spread is 1.52 percentage points (GMO under scrambled QMC versus default QMC). For tail-sensitive outcomes, pseudo-random is the appropriate default.

Volatility & correlation matrix

Annual volatility per asset class: US equity 15.2%, international equity 18.0%, bonds 5.0%, real estate 15.0%, cash 1.0% (J.P. Morgan published values). Correlation matrix: J.P. Morgan published values, full 5×5. Held constant across all six return-forecast runs so that only the return assumption varies. The five other firms either don't publish a complete correlation matrix or use modestly different volatility estimates within typical empirical ranges (US equity 15–19%, bonds 5–7%); using one consistent set isolates the return-forecast variable.

Inflation

Deterministic 2.5% CPI applied uniformly to withdrawals across all runs. Stochastic inflation models (AR(1), regime-switching) are available in the QuantCalc engine and produce qualitatively similar conclusions, with slightly higher tail-failure rates under regime-switching. We chose deterministic for this piece to keep the comparison clean and the methodology reproducible by other researchers.

What this analysis does not model

• Sequence-of-returns risk beyond what i.i.d. monthly returns naturally produce. The engine does not impose macro regimes or autocorrelation.
• Tax drag on withdrawals (no Roth/Traditional/taxable distinction at this layer).
• Healthcare cost shocks, long-term care events, or other lumpy out-of-distribution spending.
• Behavioral changes — actual retirees often dynamically adjust withdrawals in response to portfolio performance.
• Time-varying volatility (GARCH-style), fat-tailed return distributions (Student-t), or regime-shift correlation increases during crises. The QuantCalc engine supports these; this piece does not enable them.

Reproducibility

The full simulation outputs are available below as machine-readable downloads. Anyone with the QuantCalc free CLI or the public API can re-run the exact same scenario and get the same numbers (modulo random-seed differences in the pseudo-random regime).

To reproduce in QuantCalc, run a Monte Carlo simulation with these parameters:

{
  "currentAge": 65,
  "retirementAge": 65,
  "endAge": 95,
  "initialSavings": 1000000,
  "monthlyWithdrawal": 3333.33,
  "allocations": [0.45, 0.15, 0.40, 0.0, 0.0],
  "numSimulations": 10000,
  "adjustForInflation": true,
  "useQMC": false,
  "cme_source": "vanguard",                  // change per run
  "correlations_source": "jpmorgan",
  "volatility_source": "jpmorgan"
}

Set cme_source to one of: jpmorgan, blackrock, vanguard, gmo, schwab, invesco. The resulting successRate and percentiles match the figures in this article within sampling noise.

FAQ

Why use 60/40 instead of Bengen's 50/50?

60/40 is the more common modern reference allocation for retirement and what most contemporary retirement calculators default to. Re-running the analysis at 50/50 (or 70/30) shifts the absolute numbers but not the ranking of forecasts or the qualitative conclusions. The terminal-balance spread tightens slightly at 50/50 because of the lower equity weight, but the GMO outlier persists.

Does the answer change at a 4.5% or 3.5% withdrawal rate?

Yes, and this is a useful direction for further study. At 3.5% inflation-adjusted withdrawal, success approaches 100% even under GMO. At 4.5%, GMO's success drops sharply (we estimate to ~78–82% based on a smaller-sample re-run). The other forecasts hold above 99% even at 4.5%. The 4% level is where GMO and the rest visibly diverge.

Why isn't there a "Historical 1926–2025" comparison?

Because adding it would distract from the main question — how do forward-looking forecasts treat the 4% rule? The historical-data answer is well-known (effectively 100% success on US data; near-100% on international rolling windows). For completeness: under the same 60/40 plan with QuantCalc's historical 1926–2025 inputs, success rate is 100% with a median terminal balance of approximately $8.6M nominal — higher than any of the published forward-looking forecasts.

Found this useful? Run your own retirement plan against multiple forecasts at quantcalc.app. Or read more research: ACA cliff Monte Carlo (2026) · Three problems with the 4% rule · 2026 safe-withdrawal-rate research · Full simulation methodology.