Module 12: Risk Management and Trading Psychology
Cognitive Biases in Crypto
Chapter 8 of 14 · about 5 minutes
8.1 A smart person's mistakes
Node had a long career, a good education, and years of investing experience. Yet Node had made several crypto decisions that, looking back, seemed obviously irrational: holding a losing token too long, buying after a huge rally, ignoring warning signs about a project Node liked.
"I'm not a careless person," said Node. "Why do I keep doing this?"
"Because you're a person," said Hash. "Human brains use mental shortcuts that work well in everyday life and badly in markets. Psychologists call them cognitive biases. Knowing their names won't make you immune. But it helps you catch them."
8.2 The most common biases in crypto
| Bias | What it is | How it shows up in crypto |
|---|---|---|
| Confirmation bias | Seeking information that supports what you already believe | Following only accounts that agree with your positions |
| Recency bias | Overweighting recent events | Assuming a rally or crash will continue forever |
| Anchoring | Fixating on a specific number | "It was $10 last month, so $5 is cheap" |
| Loss aversion | Feeling losses more strongly than equal gains | Refusing to sell losers to avoid "making it real" |
| Disposition effect | Selling winners too early and holding losers too long | Taking a 20% profit quickly but holding a 60% loss for months |
| Overconfidence | Overestimating your skill or knowledge | Increasing size after a few wins (Chapter 11) |
| Sunk cost fallacy | Continuing because of what you've already invested | "I've lost so much, I can't sell now" |
| Herd behaviour | Following what others are doing | Buying because everyone online is buying |
| FOMO (fear of missing out) | Fear of being left behind | Chasing tokens that have already surged |
| Hindsight bias | Believing past events were predictable | "I knew it would crash," leading to overconfidence |
| Survivorship bias | Seeing only the winners | Memecoin success screenshots (Module 11, Chapter 10) |
| Illusion of control | Believing you control outcomes you don't | Over-trading, constant checking and tinkering |
8.3 Anchoring in practice
Anchoring is especially common in crypto, because of the dramatic price histories.
Example. A token peaked at $40 and now trades at $8.
| Anchored thinking | Better thinking |
|---|---|
| "It's down 80%, so it's cheap" | "What's it worth today, based on fundamentals?" (Module 5) |
| "I'll sell when it gets back to my $30 entry" | "Would I buy it today at $8, knowing what I know now?" |
| "It will definitely return to $40" | "Many tokens never recover their peaks" (Module 1, Chapter 13) |
The price you paid, or a past high, has no effect on what the asset will do next.
8.4 Loss aversion and the disposition effect
Research in behavioural economics suggests that losses tend to feel roughly twice as painful as equivalent gains feel good. This drives the disposition effect.
| Position | Typical emotional response | Typical (biased) action |
|---|---|---|
| Up 20% | "Lock it in before it disappears" | Sell too early |
| Down 40% | "Selling would make the loss real" | Hold too long, hoping |
This pattern produces exactly the opposite of good risk management: small wins and large losses (Module 4, Chapter 16).
Mint recognised it immediately. "That's my whole portfolio. I sold my winners and kept my losers."
"Almost everyone does it at first," said Hash. "Stops and written exit plans (Chapter 4) are the cure, because they decide before the emotions arrive."
8.5 Recency bias and cycles
In bull markets, recent gains make further gains feel inevitable. In bear markets, recent losses make recovery feel impossible. Both feelings peak at the worst times: euphoria near tops, despair near bottoms (Chapter 9).
On-chain cycle metrics (Module 6, Chapter 6) and long-term charts on log scale (Module 4, Chapter 2) help counter recency bias by showing the bigger picture.
8.6 Confirmation bias and your information diet
Crypto social media is very good at feeding confirmation bias. Algorithms show you more of what you engage with, and communities often reward agreement and punish doubt (Module 5, Chapter 12).
Countermeasures:
- Follow thoughtful people who disagree with your positions.
- Before buying, write down the strongest argument against it (Module 11, Chapter 13).
- Ask, "What would change my mind?" and look for that evidence.
- Be wary of sources with financial interests in your decision (Module 11, Chapter 2).
8.7 Debiasing tools
| Tool | Which biases it counters |
|---|---|
| Written trading plans and exit rules | Loss aversion, disposition effect, sunk cost |
| Pre-trade checklist (Module 4, Chapter 16) | FOMO, herd behaviour, overconfidence |
| Thesis with invalidation criteria | Confirmation bias, anchoring |
| Trading journal (Chapter 13) | Hindsight bias, overconfidence, illusion of control |
| "Would I buy it today?" test | Anchoring, sunk cost |
| Waiting period before impulsive trades | FOMO, herd behaviour |
| Fixed position sizing rules | Overconfidence after wins, revenge after losses |
8.8 Try it yourself
Review your last ten crypto decisions, or imagine ten you might make. For each, identify which biases from section 8.2 might have influenced it. Then apply the "would I buy it today?" test to every position you currently hold. For any that fail, write down why you're still holding.
Key takeaways from this chapter
- Cognitive biases are mental shortcuts that work in everyday life but mislead in markets, affecting everyone.
- Key crypto biases include confirmation, recency, anchoring, loss aversion, disposition effect, overconfidence, sunk cost, herding, FOMO, hindsight, survivorship, and illusion of control.
- Anchoring to past prices or entry prices distracts from what an asset is worth now.
- Loss aversion drives the disposition effect: selling winners too early and holding losers too long.
- Recency bias peaks at the worst times, making euphoria and despair feel permanent.
- Social media amplifies confirmation bias; seek out disagreement deliberately.
- Written plans, checklists, theses, journals, waiting periods, and fixed sizing rules counter biases.