Success rate

Explanation

After a Monte Carlo run, each trial either made it to the end of your plan with money left, or it did not. The success rate is the share that made it. The app can also count other events as failures, such as a margin call that forces sales, depending on how the scenario is set up.

How to interpret it:

• It is not a probability of the real future. It reflects your assumptions about returns, inflation and behavior. Change the inputs, and the number moves.
• Higher is not always better. Pushing toward 100% usually means working longer or spending less than you could. Many planners consider something like 75% to 90% reasonable if you are willing to adjust spending along the way, because real retirees respond to bad markets.
• Failure has degrees. Running out at 94 with Social Security still arriving is very different from running out at 70. Look at when failures happen and at the low percentiles.
• Success has degrees too. A 95% success rate often comes with a median outcome of leaving a very large balance unspent.

Pairing a success rate with flexible spending rules, such as guardrails, often gives a more realistic picture: the plan may “succeed” more often in exchange for occasional spending cuts.

In scenarios, the success rate appears alongside percentile bands. Comparing it across scenarios (different retirement ages, withdrawal rates, allocations or loan sizes) is one of the clearest ways to see trade-offs.

Example

You run 10,000 trials of a 35-year retirement.

• 9,150 trials end with money left; 850 run out.
• Success rate: 9,150 ÷ 10,000 = 91.5%

Looking closer at the 850 failures: if their median depletion age is 92, most of the shortfall occurs late, when Social Security still covers part of spending. Lowering the withdrawal rate from 4% to 3.5% might raise success to around 97%, at the cost of $5,000 less spending a year per $1,000,000 saved.