Modeling Sequence-of-Returns Risk in Retirement Portfolios

Introduction

Sequence-of-returns risk describes how the timing of investment gains and losses affects the longevity of a retirement portfolio. In plain terms: two retirees can earn the same average return over 30 years, yet the retiree who suffers losses early in retirement may run out of money long before the other. In my practice over the past 15 years, I’ve seen this dynamic change recommended withdrawal rates, asset allocation, and the decision to add annuities or a cash cushion.

This article explains how professionals model sequence-of-returns risk, shows a simple numerical example, compares common modeling techniques, and lists practical strategies retirees can use to reduce the chance that an early market downturn will derail income plans.

Why sequence-of-returns matters for retirement planning

Most retirees withdraw money from their portfolios while the assets are still invested. When withdrawals occur during market downturns, those distributions lock in losses and reduce the base available to benefit from later recoveries. This is fundamentally different from the accumulation phase, where negative returns early in a long career can be averaged out by later gains.

Key consequences:

• Higher early withdrawals during a market slump magnify losses and accelerate portfolio depletion.
• Fixed withdrawal rules (for example, taking the same dollar amount each year) do not adapt to market stress and can drain assets faster.
• Sequence-of-returns risk is most acute in the first 5–10 years of retirement, when portfolios face the highest probability of permanent damage if markets decline.

How professionals model sequence-of-returns risk

Modeling is the process of testing how different patterns of returns affect a portfolio under a chosen withdrawal strategy. Financial planners typically use two main approaches:

1. Historical sequence testing (HST)

• Method: Run a retirement withdrawal plan against historical annual returns for a set of asset mixes (for example, 60% stocks / 40% bonds), shifting the start year through decades of market history.
• Strength: Shows outcomes grounded in real markets (examples include retirees who began in 1929, 1966, or 2000).
• Limitation: Past history is not a perfect predictor of the future and may omit extreme events.

1. Monte Carlo simulation

• Method: Generate thousands of plausible forward-looking return sequences that reflect expected means, volatilities, and correlations. Then simulate withdrawals across those sequences to estimate the probability of failure and distribution of outcomes.
• Strength: Captures many potential future paths and tail risk better than a few historical samples.
• Limitation: Results depend on the input assumptions (expected returns, volatility, and distribution shape). Monte Carlo models can understate risk if assumptions are too optimistic.

Both methods are complementary. In practice, I run both HST to see real-world examples and Monte Carlo to quantify failure probability across many scenarios.

Simple numeric example (illustrative)

Two retirees, A and B, each start with $500,000 and plan 30 years of withdrawals. They both expect 6% annualized average returns and plan to withdraw 4% of the initial balance in year one, adjusted for inflation each subsequent year. The difference is the order of returns in the first five years:

• Sequence X (bad early returns): -10%, -8%, +5%, +7%, +6% in years 1–5.
• Sequence Y (good early returns): +10%, +8%, +5%, +7%, +6% in years 1–5.

Because retiree A (Sequence X) is withdrawing from a falling balance in the first years, more shares are sold at depressed prices. Retiree B benefits from early gains and sells fewer shares to fund the same spending. Over 30 years, retiree A may exhaust the portfolio decades earlier, even though both sequences average similarly.

This numerical gap is why planners stress the “order” of returns in addition to the long-term average.

Common modeling inputs and how to choose them

• Asset allocation: The stock/bond mix strongly influences volatility and recovery potential. Higher equity allocations improve long-term growth but increase early retirement drawdown risk.
• Expected returns and volatility: Use conservative return assumptions and realistic volatility—avoid optimistic equity returns if they drive false comfort.
• Withdrawal rule: Fixed percentage (e.g., the 4% Rule) versus dynamic or guardrail-based rules. Modeling should reflect the actual withdrawal policy a retiree intends to follow.
• Time horizon and mortality: Longer horizons (or uncertain longevity) increase exposure to sequence risk. Incorporate life expectancy ranges or joint-life probabilities.
• Fees and taxes: Net-of-fees and after-tax returns materially change outcomes—include realistic expense and tax assumptions.

Authoritative resources: for tax and retirement-benefit rules, reference IRS guidance (https://www.irs.gov/) and Social Security timing considerations (https://www.ssa.gov/) when modeling income sources.

Mitigation strategies that modeling can test

1. Cash or short-term bucket (liquidity buffer)

• Keep 1–3 years (or more, depending on risk tolerance) of spending in cash or short-duration bonds to avoid forced portfolio sales after market drops.

1. Bond ladders and guaranteed income

• Laddering bonds or buying immediate/short-deferral annuities can replace part of the withdrawal sequence with predictable income.
• Partial annuitization (converting a portion of savings to lifetime income) greatly reduces sequence risk for the annuitized portion.

1. Dynamic withdrawal rules and guardrails

• Adjust withdrawals up or down based on portfolio performance and a pre-set rule (for example, reduce withdrawals temporarily when portfolio value falls below a guardrail). Methods such as the Guyton-Klinger rules or more modern dynamic spending rules are common in planning practice.

1. Lower initial safe withdrawal rate

• The classic 4% Rule is a starting point; recent research and high-valuation environments often suggest starting lower (e.g., 3–3.5%) to reduce failure probability.
• See FinHelp’s coverage on The 4% Rule of Retirement Withdrawal for background and variations.

1. Asset allocation glide-paths and rebalancing

• Reduce equity exposure around retirement (a conservative glide-path) or use time-based rebalancing to preserve downside protection while retaining growth potential.

1. Working longer or delaying Social Security

• Delaying full retirement and claiming Social Security later increases guaranteed income and shortens the withdrawal horizon, alleviating sequence risk (see Social Security information at https://www.ssa.gov/).

Tools and practical steps to model sequence risk yourself

• Use planning software or Monte Carlo calculators that allow custom assumptions for returns, volatility, withdrawals, and correlations.
• Run historical sequence tests by starting retirement in different past years to see how actual markets would have impacted outcomes.
• Stress-test with large negative shocks in early years (for example, a 20–30% drop) and see the impact on failure probability and years remaining.
• Consider combining modeling methods: historical tests to understand plausible worst-case sequences, Monte Carlo to quantify probabilities.

For practical strategies on withdrawals and designing resilient income, see FinHelp articles on Managing Sequence of Returns Risk in Withdrawal Years and Retirement Income Strategies: Sequencing Withdrawals and Payout Options.

Common misconceptions

• Myth: “If average returns are X%, sequence order doesn’t matter.” Reality: Order matters a great deal when withdrawals occur during a down market.
• Myth: “More bonds = no sequence risk.” Reality: Lower-volatility portfolios reduce risk but can also lower expected returns, increasing the chance of outliving assets if withdrawals remain high.
• Myth: “An initial good decade guarantees success.” Reality: Late-career bear markets or extended longevity still create failure risk.

Actionable checklist for retirees and planners

1. Run both historical sequence tests and Monte Carlo simulations with conservative assumptions.
2. Build a 2–5 year cash reserve to fund withdrawals after retirement.
3. Consider partial annuitization or a bond ladder to cover essential expenses.
4. Choose a flexible withdrawal rule and predefine guardrails for spending cuts and raises.
5. Reassess asset allocation and withdrawal plans annually or at major life events.

Professional disclaimer

This content is educational and does not constitute individualized financial, tax, or investment advice. For personalized planning, consult a fiduciary financial planner or tax professional. For tax-specific rules and distributions, consult the IRS (https://www.irs.gov/) and for retirement consumer guidance, see the Consumer Financial Protection Bureau (https://www.consumerfinance.gov/).

References and authoritative sources

• IRS—official guidance and tax rules: https://www.irs.gov/.
• Social Security Administration—timing and benefits information: https://www.ssa.gov/.
• Consumer Financial Protection Bureau—retirement planning resources: https://www.consumerfinance.gov/.

Modeling sequence-of-returns risk is not about eliminating uncertainty; it’s about making that uncertainty explicit and actionable. Using a combination of historical tests, Monte Carlo analysis, and practical mitigations (cash buffers, partial annuities, and dynamic withdrawals) can materially reduce the odds that an early downturn turns into a retirement crisis.