Week 7 - Theories, stock-level returns Flashcards
Four Types of Models
- Preference-based models
- Belief-based models
- Models of cognitive limits
- Models with frictions
Preference-based models
Barberis and Huang (2008) - Stocks as Lotteries
- Based on prospect theory
- Single period model -> risk-free asset and J risky assets with multivariate Normal Payoffs
Investors:
- Identical expectations about security payoffs
- Identical CPT preferences -> defined over gains/losses in wealth
- reference point is initial wealth scaled up by the risk-free rate
Utility defined over: W = W1 -W0*Rf
Barberis and Huang (2008) Model
- CAPM Holds - prospect theory gives the same prediction as the Expected Utility model
- To make more interesting predictions, remove assumption of multivariate normal assumption of returns -> introduce small, independent, positively skewed security into economy
- Prediction -> the new security earns negative excess return -> skeweness is priced, in contrast to concave EU model where only coskewness with market matters
Barberis and Huang Model Equilibrium
- Equilibrium involves heterogenous holdings - some investors hold large, undiversified positions in new security whilst others hold no positions in new security
- Heterogenous holdings aris from non-unique global optima - not from heterogenous preferences
- Since new security contributes skewness to the portfolios of some investors, it is valuable and so earns a low average return
- Only works if security is highly skewed - otherwise would need too undiversified a position to add skewness to the porfolio
Applications of Barberis and Huang (2008)
- Low average return on IPOs - IPO returns are highly positively skewed
- Low average return of distressed stocks, bankrupt stocks, OTC stocks
- Overpricing of Out-of-the-money options on individual stocks
- Low average return on stocks with high idiosyncratic volatility
Belief-Based Models
- Daniel, Hirshleifer, Subrahmanyam -> overconfidence, self-attribution bias
- Barberis, Shleifer, Vishny -> Representativeness, conservatism
Overconfidence
- 1980s -> models where rational investors observe different information may not generate much trading volume
- Each investor infers other’s signals from prices, or from willingness to trade -> reduces own willingness to trade
- Overconfidence -> overestimation of the precision of one’s own information signals
- Dismissiveness -> underestimation of precision of others’ signals
- overconfidence and dismissiveness can generate significant trading volume
Empirical tests of Overconfidence and Dismissiveness
Grinblatt and Keloharju (2009):
- Data from Finland shows that overconfident people trade more
- Overconfidence is self-reported confidence minus how confident individual should be based on performance on aptitude tests
Glaser and Weber (2007)
- Use data from German online brokerage to measure two types of overconfidence: Overplacement and overprecision
- Found that overplacement predicts trading, but overprecision does not
Barber and Odean (2001)
- Argue that, since men tend to be more overconfident than women, they will trade more and have worse returns
-confirm this using brokerage data
DHS Model
- DHS present a model of misvaluation based on overconfidence
- A risk-neutral, representative investor is overconfident about the private information he gathers - stress biases in the interpretation of private, rather than public information
- If the private information is positive, overconfidence means that investors will push prices up too far relative to fundamentals
- This leads to long-term reversals and value premium
Self Attribution bias in DHS
- When public information confirms the private signal, the investor becomes even more confident in the private signal
- When public information disconfirms the private signal, he does not lose much confidence in the private signal
- This leads to momentum in addition to a value premium
Representativeness
- individuals focus more on first terms and often neglect the base rates
Law of small numbers
- Kahneman and Tversky (1971) propose that individuals have an incorrect belief in law of small numbers - they think that even a small sample will reflect characteristics of the population
- Gambler’s fallacy - When we dont know the model generating the data, LSN leads us to over-infer from small sample - generating over-extrapolation of recent trends
BSV Model
- Representative, risk-neutral investor
- earnings on all assets follow a random walk
- investor thinks that earnings at any time are driven by:
mean-reverting regime: earnings are more mean reverting that in reality
trending regime: earnings trend more than in reality
BSV Biases
Conservatism - tendency to underweight new information relative to priors
Representativeness - Motivated by LSN - people expect even short samples to reflect properties of parent population
BSV example
When a company announces surprisingly good earnings, conservatism means that investors react insufficiently, pushing the price up too little
- since the prices is too low, subsequent returns will be higher on average -> PEAD and momentum will be higher
After a series of good earnings announcements, representativeness causes people to overreact and push the price up too high
- Law of samll numbers leads investors to believe this is a firm with high earnings growth, so forecast higher earnings in the future
- since the price is now too high, subsequent returns are too low on average -> long term reversals + value
Model with cognitive limits
Hong and Stein (1999)
Hong and Stein Model
- Two boundedly rational groups of investors interact, where bounded rationality means that investors are only able to process a subset of available information
- newswatchers: make forecasts based on private information, but do not condition on past prices
momentum traders: condition only on the most recent price change - private information gradually difusses through newswatchers -> slow diffusion generates momentum
Momentum traders condition only on prices so optimal strategy is to trade on good/bad signals -> by buying, momentum traders hope to profit from continued diffusion of information
- Behaviour preserves momentum but also generates price reversals -> momentum traders keep buying at fundamental value, generating an overreaction that is only later reversed
Differences between DHS, BSV and HS
Momentum:
- In BSV and HS - Momentum is due to an initial underreaction followed by a correction
- In DHS, it is due to zn initial overreaction followed by even more overreaction
Conceptual difference between DHS, BSV and HS
DHS and BSV are psychology-based
HS is bounded-rationality-based
Model with Frictions
Overconfidence + short-sale constraints
Model with frictions advantages
Allows us to think about overpricing and bubbles in a better way
How do overconfidence and short sale constraints generate overpricing?
Argument 1: Static argument (Miller, 1977)
- If investors disagree about an asset’s future prospects, the optimists buy the asset wile the pessimist stays out of the market
Dynamic Argument (Harrison and Kreps, 1978)
- If investors disagree, each is willing to pay more than her estimate of the present value of future cash flows - when information is released, there is a chance that she will be able to sell to someone more optimistic
Extension of Overconfidence + SSC dynamic argument
Scheinkman and Xiong
- Put in an explicit source of disagreement, such as overconfidence
- make predictions not only about prices but about volume and volatility as well
Sheinkman and Xiong
- Single risky asset in finite supply, paying a dividend with unobserved drift
dDt = ftdt + sigmadZt
df = -lambda(ft - f)*dt ….
