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 ….
Scheinkman and Xiong Model
Model prediction:
Price = Fundamental value + resale value
Predicts overpricing and high volume
- Price and volume move together as we vary exogenous parameters
Bubble is largest when trading cost c = 0 -> as c increases, volume drops quickly, price also drop, but less quickly
Why are models of disagreement with SSC popular
They explain overpricing and the coincidence of high valuations with heavy trading
Evidence on the coincidence between high valuations and heavy trading
- Value stocks v growth stocks
- Tech stocks in late 1990s
