Time in market — what missing the best months costs

For each horizon the tool generates a thousand independent market paths, compounds each one in full, then recomputes it with the largest monthly returns removed and compares the endings. The headline figure is the median across all those paths rather than the result on one particular history, which matters because this statistic is notoriously sensitive to the window it is taken over — shift the start by a single year on real data and the number moves dramatically. A median across independent paths is a central estimate; one history is one draw presented as a law.

Removing the best months is a thought experiment, not a strategy anyone could execute or avoid: nobody sells before the best month and buys after it, and the same exercise run on the worst months produces an equally dramatic result in the opposite direction. The paths come from your return and volatility assumptions, so the magnitude scales with the volatility you set — a higher figure concentrates more of the return into fewer months and makes missing them look worse. Nothing is deducted for cost or tax.

Is the best-days statistic actually misleading?

The number is usually correct and the implication usually is not. The best months cluster tightly against the worst ones, in the middle of falling markets, so anyone positioned to miss the best months was almost certainly positioned to miss the worst ones too. Removing only the upside while keeping all of the downside is not a scenario any investor could experience. What survives the criticism is the narrower point: returns are concentrated in short bursts, so being absent is expensive.

Why simulate instead of using the real index history?

Because the real-history version of this statistic is unusually fragile. It depends heavily on the exact window chosen, and the windows that get published are the ones with the most striking numbers. Taking the median across many independent paths removes the ability to pick a flattering window and gives a central estimate instead of one dramatic draw.

What is the practical conclusion?

That the cost of being out is asymmetric and large enough that staying invested beats most attempts to step aside and return. It is not an argument that timing is theoretically impossible — it is an argument that the penalty for getting it slightly wrong is severe, and that the burden of proof sits with the person proposing to try.

• Sequence of returns risk — the same returns in a different order
• Monte Carlo simulation — the range of outcomes, not a single line
• Loss tolerance — what each allocation lost in three real crashes