b2KIT

Retirement Withdrawal Calculator

Model sustainable withdrawal rates from retirement portfolios using the 4% rule, dynamic spending, and Monte Carlo simulations.

Tested tool guide Tested browser tools Checked August 16, 2026

What Retirement Withdrawal Calculator does, with a checked example

This calculator turns a retirement nest egg into a withdrawal plan: it applies the 4% rule (4% of the balance in year one, then the same dollar amount raised with inflation each year), and it can stress-test the plan with Monte Carlo simulation, which runs thousands of random return sequences to estimate the probability your money outlives you. It also models dynamic spending strategies that cut or raise withdrawals as the market moves. The thing people most often get wrong: a 4% withdrawal does not mean 4% of the current balance every year.

Worked example

A concrete input and expected output from the current implementation.

Input

Portfolio: $1,000,000. Method: 4% rule. Inflation: 3% per year.

Expected output

Year 1: $40,000 ($3,333 per month). Year 2: $41,200. Year 3: $42,436. The withdrawal dollar amount is set in year 1 and then rises only with inflation.

The 4% rule fixes year-one withdrawals at 4% of the starting balance ($1,000,000 x 0.04 = $40,000), then escalates that fixed dollar amount by 3% inflation each year ($40,000 x 1.03 = $41,200, then x 1.03 again = $42,436). It never re-derives a percentage from the current balance.

How the result is produced

1

The 4% rule baseline

The rule, from the 1998 Trinity Study, sets year-one withdrawals at 4% of the initial balance, then increases that dollar amount annually by inflation. It was built for 30-year retirements from a stock-heavy portfolio of stocks and bonds; historically about 95% of 30-year periods supported it. The tool applies this math directly when you choose the 4% rule.

2

Monte Carlo and dynamic spending

Instead of one historical path, Monte Carlo simulation generates thousands of random return sequences from the return and volatility assumptions you enter, and counts what fraction keep the balance above zero for the horizon - that is the success probability. Dynamic spending rules instead modify withdrawals along the way, such as cutting them 10% when the rate drifts too far above its target, trading lower worst-case income for higher success odds.

Good uses

  • Estimating a safe starting withdrawal from a known nest egg before giving notice: $1,000,000 at 4% means $40,000 in year one, plus inflation each year after.
  • Testing whether a target income is affordable: enter a $1,500,000 portfolio and a $60,000 annual withdrawal and see the implied rate (4%) and the survival odds the simulation assigns to it.
  • Comparing strategies on the same portfolio - fixed 4% versus dynamic spending - to see how much success probability variable income buys you, and whether the income swings are acceptable.

Limits and checks

  • Monte Carlo output is only as good as the return and volatility assumptions entered: two calculators with different assumptions report different success probabilities for the same portfolio. Treat the result as a conditional estimate and test how much it moves when you change assumptions.
  • The 4% rule assumes a 30-year horizon, annual inflation adjustments, and a stock-heavy portfolio. Retiring at 50 (a 40-plus-year horizon), one-off large expenses, and taxes or fees each reduce the real success rate; results hold only for the modeled conditions.
  • A success probability averages over thousands of simulated futures, but you live one of them. Sequence-of-returns risk means two portfolios with identical average returns can fail or thrive depending on whether poor years arrive early; a 90% probability does not tell you which outcome you get.

Common questions

Does the 4% rule guarantee I will never run out of money?

No. The Trinity Study found that a 4% inflation-adjusted withdrawal from a stock-heavy portfolio lasted 30 years in about 95% of historical periods - historically robust, but not a guarantee, and it failed in roughly one in twenty of those windows. A Monte Carlo success probability is a model estimate, not a promise.

What success probability should I aim for?

There is no objectively correct target; 80% to 95% are common choices, and a higher target usually means withdrawing less, holding more stocks, or accepting variable income through dynamic spending. Remember the probability inherits your assumptions - entering optimistic returns inflates it, so test how sensitive the number is before you rely on it.

References and verification

The example and behavioral notes were checked against the browser implementation. Standards and primary references below define the relevant format, formula, or platform behavior.

Related Tools