1
Q
How is the equity risk premium puzzle an application of prospect theory?
A
- Equity risk premium defined as gap between expected return on aggregate stock market & portfolio of gov. bonds –> —> v high level of risk aversion would have to be assumed to rationalise this equity risk premium (assuming expected utility theory).
- Benartzi & Thaler (1995) use estimated parameters of prospect theory to determine frequency w which investors would have to evaluate their portfolios of stocks & bonds to explain equity premium puzzle.
- Propose ‘myopic loss aversion’, which combines loss aversion & mental accounting in prospect theory.
- Money nominally placed in diff. ‘mental accounts’, which is not easily substitutable –> investors may react more -vely to losses within an annual mental account compared to same losses spread over longer evaluation period due to greater myopic focus on short-term accounts.
- When people are loss averse, more willing to take risks if they evaluate their performance infrequently -> e.g. attractiveness of risky asset depends on time horizon of investor –> longer the investor intends to hold the asset (evaluation period), the more attractive the risky asset appears as implied equity premium decreases.
2
Q
What are the 3 applications of prospect theory?
A
1) Equity Risk Premium Puzzle
2) Disposition Fffect
3) Skewness Premium
3
Q
How is the disposition effect an application of prospect theory?
A
- Disposition effect is tendency to sell winners & hold losers –> explained by Shefrin & Statman (1985).
- Found that when investors in loss domain i.e. stock decreases in value, investors are risk seeking & so choose to hold stock for one more period rather than realise loss & sell.
- Similarly investors in gain domain are risk averse & so sell winning stock to realise gains rather than risk potential losses down the line.
4
Q
How is the skewness premium an application of prospect theory?
A
- Barberies & Huang (2008) show skewness in distribution of security’s returns can be priced.
- Probability weighting function in prospect theory non-linear, overweighting tails (low probabilities) of a security’s return distribution –> +vely skewed security therefore overpriced as investors willing to pay more (higher premium) for securities w potential for extreme but unlikely +ve performance –> earns lower/-ve avg excess return compared to traditional pricing models –> +vely skewed security overvalued relative to expected utility model.
