Behavioural Finance and Decision-Making
What CB1 tests in behavioural finance and decision-making
This domain tests how real decision-makers deviate from rational expected-utility models, and how those deviations affect markets and corporate decisions. Expect biases, heuristics, prospect theory and limits to arbitrage.
Core rules and concepts
- Bounded rationality recognises cognitive limits; satisficing replaces optimisation when search is costly.
- Prospect theory uses a value function defined over gains and losses relative to a reference point. It is concave for gains, convex for losses and steeper for losses, producing loss aversion.
- Common biases include overconfidence, anchoring, availability, representativeness, confirmation, herd behaviour and the disposition effect.
- Market anomalies such as momentum and long-run reversal challenge strong efficiency, but limits to arbitrage-short-sale constraints, noise-trader risk and costs-can let mispricing persist.
- Decision architecture, checklists and pre-mortems can reduce bias, but debiasing is difficult and context-dependent.
Worked example
An investor buys shares at 100. They fall to 70 and the investor refuses to sell, hoping to break even, while selling a winner at 120 too early. Under prospect theory the loss region is convex, so a gamble to recover the loss can seem attractive; the disposition effect predicts selling winners and holding losers. A pre-set rebalancing rule or loss-review trigger reduces this behaviour.
Traps that cost marks
Do not label every deviation a bias without identifying the reference point or heuristic. Do not claim arbitrage always eliminates mispricing. Remember that overconfidence can coexist with risk aversion. Distinguish individual bias from aggregate market outcomes.