Behavioral Biases: Why Mispricings Exist and Persist

Every edge in this course needs an answer to one question: who is on the other side, and why do they keep doing it? Behavioral finance supplies the most durable answers — systematic human regularities that generate mispricings — and interviews use the same material twice: to test whether you understand markets, and to watch whether the biases operate in you at the table.

The core catalog

• Loss aversion & prospect theory: losses hurt ~2× as much as equal gains please, and people are risk-averse over gains but risk-seeking over losses. Its trading fingerprint is the disposition effect — selling winners too early, riding losers too long ("it'll come back") — visible in retail brokerage data and, uncomfortably, on professional desks; the risk-management lesson's hard stop-losses exist largely as a commitment device against it.
• Anchoring: estimates gravitate to whatever number was mentioned first, relevant or not. In markets: 52-week highs and round numbers act as reference points with measurable price behavior around them. In interviews: the market-making game's opening quote is often an anchor trap — re-derive your fair value, don't negotiate around theirs.
• Overconfidence: the best-documented bias — intervals too narrow, self-assessed skill too high, trading too frequent (with measured underperformance from the churn). The Fermi lesson's calibration training is the direct antidote, and "how do you know you're right?" questions are overconfidence probes.
• Recency & availability: overweighting what's vivid and recent — buying after rallies, dumping after crashes, pricing yesterday's disaster as tomorrow's base rate (the log-odds lesson's LR discipline is the corrective: what's the likelihood ratio of this news, not its emotional volume?).
• Herding: imitation is individually rational when others may know more (the microstructure course's information logic!) — which is exactly what makes bubbles and crashes collectively possible.

From bias to factor

The behavioral reading of familiar premia: momentum — underreaction to news (anchoring on old prices) followed by herded overreaction; value — overextrapolation of growth stories glamorizes expensive stocks and abandons boring cheap ones; low-beta — lottery preference plus leverage constraints overprices exciting high-beta names (the CAPM lesson's anomaly, motivated). Whether these premia are "risk" or "behavior" remains contested — the honest interview answer is "probably both, and the behavioral half persists only where arbitrage is hard," which leads to the load-bearing concept:

Limits of arbitrage

Biases alone don't create tradable edges — someone should correct them. They persist because correction is costly and dangerous: mispricings can widen before they close ("markets can stay irrational longer than you can stay solvent"), shorting is constrained and expensive, capital is impatient (clients redeem at the bottom — forced selling exactly when the edge is largest), and noise-trader risk is systematic, not diversifiable. The formal irony worth quoting: arbitrageurs' funding fragility is what lets behavioral premia survive — and it links straight into the next lesson on liquidity spirals.

The interview version

"Why does momentum work if everyone knows about it?" — Behavioral origin (under/overreaction) plus limits of arbitrage (crash risk: momentum's occasional violent reversals scare away the capital that would erase it). "Tell me about a time you were wrong." — This is a disposition-effect and updating probe wearing HR clothing: the winning answer shows fast recognition, a rule-based exit, and a changed process. And at the market-making table, the meta-game is explicit: the interviewer will anchor you, push your quotes when you're down (loss-aversion pressure), and watch whether your spread widens with your uncertainty or your emotions. Knowing the catalog is table stakes; not exhibiting it under pressure is the differentiator this entire prep exists to build.