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bias library

Herding / FOMO

You follow the crowd because evolution taught you safety in numbers.

In markets, the crowd is wrong at the extremes more often than it is right. Banerjee (1992) formalised the mathematical model — informational cascades — that shows how individually rational agents can collectively drive irrational outcomes. In crypto, the social-media amplification of this effect is the single most expensive emotion.

What it looks like in crypto

  • ●You buy because a coin is trending on X. "If everyone is talking about it, there must be something." That logic peaks at the top.
  • ●You mirror a tracked trader's position — same coin, same time, no understanding of why they entered or how they'd exit. You bought the trader's confidence, not the asset.
  • ●You capitulate at the bottom because peers are. The narrative shifts to "this is structural", "this is different", "it's the end". It rarely is.
  • ●Your reasoning is, in the end, "everyone is buying it" or "everyone is selling it". This is herd behaviour, named.
Counter-moves
  1. When you spot herd behaviour — yours or others' — that's the signal to slow down, not speed up.
  2. If your only argument for a trade is "others are buying", skip the trade. Build a thesis that survives without the crowd.
  3. Cap your time on X to a fixed window. Crypto Twitter creates urgency that almost never matches reality.
  4. Run the contrarian thought experiment: if no one I follow had this position, would I still want it? If the answer is no, the position is on borrowed conviction.

Worth knowing

Herding feels like prudence ("I'm not the only one") and is actually outsourced thinking. The fix is not to be a contrarian for the sake of it — it is to be able to defend the trade alone, in a quiet room, without your feed. If you can, the crowd's later agreement is bonus. If you can't, the crowd's later disagreement is catastrophe.

Academic foundation

Banerjee (1992). A Simple Model of Herd Behavior. Quarterly Journal of Economics, 107(3), 797–817.

Related biases
Recency bias

You weight recent events much more than their statistical significance warrants.

Overconfidence

You systematically overestimate how accurate your forecasts are.

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