N.Barberis et al./Journal of Financial Economics 49 (1998)307-343 317 In the context at hand,Griffin and Tversky's theory suggests that individuals might underweight the information contained in isolated quarterly earnings announcements,since a single earnings number seems like a weakly informative blip exhibiting no particular pattern or strength on its own.In doing so,they ignore the substantial weight that the latest earnings news has for forecasting the level of earnings,particularly when earnings are close to a random walk.At the same time,individuals might overweight consistent multiyear patterns of notice- ably high or low earnings growth.Such data can be very salient,or have high strength,yet their weight in forecasting earnings growth rates can be quite low. Unfortunately,the psychological evidence does not tell us quantitatively what kind of information is strong and salient(and hence is overreacted to)and what kind of information is low in weight(and hence is underreacted to).For example, it does not tell us how long a sequence of earnings increases is required for its strength to cause significant overpricing.Nor does the evidence tell us the magnitude of the reaction(relative to a true Bayesian)to information that has high strength and weight,or low strength and weight.For these reasons,it would be inappropriate for us to say that our model is derived from the psychological evidence,as opposed to just being motivated by it. There are also some stock trading experiments that are consistent with the psychological evidence as well as with the model presented below.Andreassen and Kraus(1990)show subjects(who are university undergraduates untrained in finance)a time series of stock prices and ask them to trade at the prevailing price.After subjects trade,the next realization of price appears,and they can trade again.Trades do not affect prices:subjects trade with a time series rather than with each other.Stock prices are rescaled real stock prices taken from the financial press,and sometimes modified by the introduction of trends Andreassen and Kraus's basic findings are as follows.Subjects generally 'track prices',i.e.,sell when prices rise and buy when prices fall,even when the series they are offered is a random walk.This is the fairly universal mode of behavior,which is consistent with underreaction to news in markets.However, when subjects are given a series of data with an ostensible trend,they reduce tracking,i.e.,they trade less in response to price movements.It is not clear from Andreassen and Kraus's results whether subjects actually switch from bucking trends to chasing them,although their findings certainly suggest it. De Bondt(1993)nicely complements Andreassen and Kraus's findings.Using a combination of classroom experiments and investor surveys,De Bondt finds strong evidence that people extrapolate past trends.In one case,he asks subjects to forecast future stock price levels after showing them past stock prices over unnamed periods.He also analyzes a sample of regular forecasts of the Dow Jones Index from a survey of members of the American Association of Indi- vidual Investors.In both cases,the forecasted change in price level is higher following a series of previous price increases than following price decreases, suggesting that investors indeed chase trends once they think they see them.In the context at hand, Griffin and Tversky’s theory suggests that individuals might underweight the information contained in isolated quarterly earnings announcements, since a single earnings number seems like a weakly informative blip exhibiting no particular pattern or strength on its own. In doing so, they ignore the substantial weight that the latest earnings news has for forecasting the level of earnings, particularly when earnings are close to a random walk. At the same time, individuals might overweight consistent multiyear patterns of notice￾ably high or low earnings growth. Such data can be very salient, or have high strength, yet their weight in forecasting earnings growth rates can be quite low. Unfortunately, the psychological evidence does not tell us quantitatively what kind of information is strong and salient (and hence is overreacted to) and what kind of information is low in weight (and hence is underreacted to). For example, it does not tell us how long a sequence of earnings increases is required for its strength to cause significant overpricing. Nor does the evidence tell us the magnitude of the reaction (relative to a true Bayesian) to information that has high strength and weight, or low strength and weight. For these reasons, it would be inappropriate for us to say that our model is derived from the psychological evidence, as opposed to just being motivated by it. There are also some stock trading experiments that are consistent with the psychological evidence as well as with the model presented below. Andreassen and Kraus (1990) show subjects (who are university undergraduates untrained in finance) a time series of stock prices and ask them to trade at the prevailing price. After subjects trade, the next realization of price appears, and they can trade again. Trades do not affect prices: subjects trade with a time series rather than with each other. Stock prices are rescaled real stock prices taken from the financial press, and sometimes modified by the introduction of trends. Andreassen and Kraus’s basic findings are as follows. Subjects generally ‘track prices’, i.e., sell when prices rise and buy when prices fall, even when the series they are offered is a random walk. This is the fairly universal mode of behavior, which is consistent with underreaction to news in markets. However, when subjects are given a series of data with an ostensible trend, they reduce tracking, i.e., they trade less in response to price movements. It is not clear from Andreassen and Kraus’s results whether subjects actually switch from bucking trends to chasing them, although their findings certainly suggest it. De Bondt (1993) nicely complements Andreassen and Kraus’s findings. Using a combination of classroom experiments and investor surveys, De Bondt finds strong evidence that people extrapolate past trends. In one case, he asks subjects to forecast future stock price levels after showing them past stock prices over unnamed periods. He also analyzes a sample of regular forecasts of the Dow Jones Index from a survey of members of the American Association of Indi￾vidual Investors. In both cases, the forecasted change in price level is higher following a series of previous price increases than following price decreases, suggesting that investors indeed chase trends once they think they see them. N. Barberis et al./Journal of Financial Economics 49 (1998) 307—343 317