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Behavioral finance

The sunk-cost fallacy: throwing good money after bad

Money already lost shouldn't influence the next decision — but it dominates it. A rule that ignores your cost basis is the fix.

Autopilot Options Research · April 11, 2026 · 4 min read

"I've already put this much into it — I can't bail now." If you've ever thought that about a trade, you've met the sunk-cost fallacy, and it's quietly one of the most expensive instincts in markets.

The core mistake

A sunk cost is money already spent or lost that you can't get back. Rationally, it should have zero bearing on what you do next — the only question is whether the trade is worth holding from here. But humans don't work that way. The more we've already lost, the more committed we feel, and the harder it becomes to walk away.

So a small loss we'd happily cut becomes a large loss we cling to, purely because we've already paid for the privilege of being in it.

Why trading makes it worse

The sunk-cost fallacy teams up with loss aversion and confirmation bias to form a near-perfect trap. Loss aversion makes booking the loss painful; confirmation bias supplies reasons to hold; sunk cost adds "but I've come this far." Together they keep you in losing trades exactly as long as it takes to turn a manageable mistake into a serious one.

Breaking it

The cure is the same as for its cousins: take the decision away from the moment.

  • Judge a position on its merits from now forward, never on what you've already lost.
  • Pre-commit to an exit that doesn't know or care about your cost basis.
  • Automate it, so "but I've come this far" never gets a vote.

A rule that closes a trade at a pre-set limit is immune to sunk cost — it has no past to honor. That indifference is exactly what you want when your own brain is busy justifying one more day.


This article is educational and does not constitute investment advice or a recommendation. Options trading involves substantial risk and is not suitable for every investor. Autopilot Options does not guarantee profits or prevent losses. Past performance and historical data do not guarantee future results.

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