Sequence of Returns Risk: Why the Order of Gains Changes Everything in Retirement

The Same Average, Two Opposite Outcomes

Roberto and Carla retire on the same day. Both have €400,000 in a diversified 70/30 equity-bond portfolio. Both withdraw €16,000 per year, adjusted for inflation. Over the following twenty years, both achieve an average annual return of 6.5%.

At the end of that period, Roberto still has €680,000 in his portfolio. Carla has nothing. Her portfolio ran out in year fourteen.

Same starting point. Same average return. Incomparable outcomes.

The difference lies entirely in the order in which those returns arrived. Roberto lived through a declining market in his final working years, but his early retirement years coincided with strong market performance. Carla experienced the reverse: her first years of retirement fell during a market crash. Her portfolio lost value while she continued to withdraw, selling units at depressed prices. Her capital base eroded permanently, never recovering enough to last twenty years.

This is sequence of returns risk.

What Sequence of Returns Risk Is

Sequence of returns risk describes the danger that the timing of a portfolio’s returns produces far worse outcomes than expected, even when the average return stays the same.

The risk is not in the aggregate numbers; it is in the path. A portfolio that loses 30% in the first year of retirement, then recovers and grows, ends up in a much weaker position than one that first grows and then suffers the same loss, even though both may show identical arithmetic averages over the full period.

The reason is mathematical. When you make regular withdrawals from a declining portfolio, you sell more units than you would at higher prices. This permanently reduces the number of units available to benefit from the eventual recovery. The capital base erodes in a way that is structurally difficult to reverse.

Why It Does Not Matter During Accumulation

In the accumulation phase, sequence risk is essentially irrelevant. An investor who contributes €500 per month for thirty years ends up with a result that depends almost entirely on the annualised average return, not on the order of good and bad years.

In fact, in a regular investment plan, a falling market in early years is advantageous: you buy more shares at lower prices, reducing your average cost basis.

That symmetry breaks the moment you make your first withdrawal.

As soon as you begin selling units rather than buying them, the sequence of returns becomes the dominant factor in your final outcome. An accumulating portfolio and a withdrawing portfolio respond in opposite ways to the same volatility.

The Asymmetry in Detail: A Worked Example

Consider two investors, both with €400,000 and annual withdrawals of €16,000. Over five years, both experience the same set of returns, but in reverse order.

The arithmetic average is identical for both: +5% per year.

Investor A (good years first):

$$V_1 = (400{,}000 \times 1.30) - 16{,}000 = 504{,}000$$

$$V_2 = (504{,}000 \times 1.20) - 16{,}000 = 588{,}800$$

$$V_3 = (588{,}800 \times 1.05) - 16{,}000 = 602{,}240$$

$$V_4 = (602{,}240 \times 0.90) - 16{,}000 = 526{,}016$$

$$V_5 = (526{,}016 \times 0.80) - 16{,}000 = 404{,}813$$

Investor B (bad years first):

$$V_1 = (400{,}000 \times 0.80) - 16{,}000 = 304{,}000$$

$$V_2 = (304{,}000 \times 0.90) - 16{,}000 = 257{,}600$$

$$V_3 = (257{,}600 \times 1.05) - 16{,}000 = 254{,}480$$

$$V_4 = (254{,}480 \times 1.20) - 16{,}000 = 289{,}376$$

$$V_5 = (289{,}376 \times 1.30) - 16{,}000 = 360{,}189$$

After five years, same average return, same withdrawals: Investor A has €404,813 while Investor B has €360,189. A gap of more than €44,000 created without either investor making a single different choice.

Over a thirty-year horizon, this divergence compounds into the difference between a healthy portfolio and an empty one.

Why Standard Models Miss It

Many financial planning tools, and nearly all the spreadsheets that circulate among individual investors, use average returns to project the future. Enter 6% per year for thirty years and the spreadsheet returns a tidy, reassuring final figure.

That calculation is misleading for anyone withdrawing from a portfolio.

An average return is not the return of a real portfolio. The real return depends on the path: on the order of years and on the interaction between returns and withdrawals. A constant-return model cannot simulate this because it removes the temporal variability that is precisely the source of the risk.

The practical consequence is that retirement planning based purely on averages tends to systematically overstate the probability of success. Sequence risk is nearly invisible in simplified models but shows up forcefully in real markets.

Strategies to Manage Sequence Risk

Sequence risk cannot be eliminated, but it can be managed. Effective strategies work on two fronts: reducing the impact of a declining market in the early retirement years and preserving the flexibility to adapt withdrawals to actual conditions.

The Bucket Approach

Divide the portfolio into three buckets with different time horizons. The first bucket holds one to two years of living expenses in cash or cash equivalents. It is not exposed to market volatility and ensures that early withdrawals do not require selling assets at depressed prices. The second bucket holds bonds and low-volatility assets for a three-to-eight-year horizon. The third bucket is the equity portfolio for long-term growth.

The logic is straightforward: even if equities fall 40% in year one, withdrawals come from the cash bucket, not from depressed stocks. The portfolio has time to recover before it needs to be touched.

Flexible Withdrawals with Guardrails

Instead of rigidly following the inflation-adjusted initial withdrawal, define two adjustment thresholds. If the portfolio grows significantly, the next year’s withdrawal can increase up to a set ceiling. If it falls below a critical level, the withdrawal drops by 10-15% until conditions normalise.

The cost of this approach is a less predictable income. The benefit is a lower risk of portfolio exhaustion during periods of stress, giving the portfolio time to recover.

The Bond Tent: An Inverted Glidepath

One of the most counterintuitive yet well-researched strategies involves increasing bond exposure in the five years before retirement, then gradually reducing it over the ten years that follow, migrating back towards higher equity exposure.

The aim is to protect the portfolio during its window of maximum vulnerability: the five years straddling the retirement date, when sequence risk is most acute. Once that critical phase has passed, the portfolio can afford to shift back towards equities for long-term sustainability.

Partial Annuitisation

Converting a portion of capital into a lifetime annuity guarantees a fixed income independent of market performance. This eliminates sequence risk on the annuitised portion, at the cost of reduced flexibility and the ability to pass capital to heirs.

For investors in countries with a public pension, that state benefit already partly serves this function: it is a guaranteed income floor that reduces dependence on the investment portfolio in the critical early years.

Sequence Risk and Monte Carlo Simulation

Monte Carlo simulation is the most powerful tool for making sequence risk visible during the planning stage.

Rather than using a constant average return, a Monte Carlo simulation generates thousands of possible portfolio paths, each with a different sequence of returns drawn from the historical distribution. The result is not a single number but a probability distribution: how many simulations end with positive capital, how many are exhausted, and in which year.

This approach explicitly captures what an average-return model cannot see. Two portfolios with the same expected average but different volatility levels will produce very different outcome distributions: the more volatile portfolio faces worse potential sequences and will have a wider tail of negative scenarios.

With Wallible, you can run this simulation on your actual portfolio, with your specific allocation, planned withdrawals, and time horizon. The success probability the simulator calculates already incorporates sequence risk through the variability of the simulated paths.

The Connection to the 4% Rule

The article on the 4% rule explains how that withdrawal rate was calibrated on US historical market data over a thirty-year horizon. The rate implicitly accounts for sequence risk: it was chosen precisely because it held up even in the worst historical scenarios, including the major crises of the twentieth century.

But sequence risk also explains why the 4% rule is not a universal guarantee. A retiree who begins withdrawals at the start of a prolonged bear market faces the worst possible conditions: withdrawing during negative-return years at exactly the critical phase. The Trinity Study’s success probabilities become lower when you strip out the good fortune of starting with a favourable market.

For many investors outside the US, where historical equity returns have been lower than the American experience, sequence risk makes a more conservative withdrawal rate of 3 to 3.5% and a flexible withdrawal strategy a more defensible choice than mechanically applying the 4% figure.

Next Step

Sequence risk is invisible in average-return spreadsheets but real in living markets. Retirement planning that ignores it is built on foundations that appear solid only because they have never been stress-tested.

With Wallible you can:

• Model your withdrawal plan using Monte Carlo simulation to see the full distribution of outcomes, not just the average
• Read the guide to the 4% rule to understand where that number comes from and why a more conservative rate often makes sense
• Explore the Monte Carlo simulation guide to see how the simulator captures path variability rather than just expected returns