N.Barberis et al./Journal of Financial Economics 49 (1998)307-343 319 of the previous section.Model 1 generates effects identical to those predicted by conservatism.An investor using Model 1 to forecast earnings reacts too little to an individual earnings announcement,as would an investor exhibiting conser- vatism.From the perspective of Griffin and Tversky (1992),there is insufficient reaction to individual earnings announcements because they are low in strength. In fact,these announcements have extremely high weight when earnings follow a random walk,but investors are insensitive to this aspect of the evidence. In contrast,the investor who believes in Model 2 behaves as if he is subject to the representativeness heuristic.After a string of positive or negative earnings changes,the investor uses Model 2 to forecast future earnings,extrapolating past performance too far into the future.This captures the way that representa- tiveness might lead investors to associate past earnings growth too strongly with future earnings growth.In the language of Griffin and Tversky,investors overreact to the information in a string of positive or negative earnings changes since it is of high strength;they ignore the fact that it has low weight when earnings simply follow a random walk. The investor also believes that there is an underlying regime-switching pro- cess that determines which regime the world is in at any time.We specify this underlying process as a Markov process as well,so that whether the current regime is Model 1 or Model 2 depends only on what the regime was last period. We focus attention on cases in which regime switches are relatively rare.That is, if Model 1 determines the change in earnings in period t,it is likely that it determines earnings in period t+1 also.The same applies to Model 2.With some small probability,though,the regime changes,and the other model begins generating earnings.For reasons that will become apparent,we often require the regime-switching probabilities to be such that the investor thinks that the world is in the mean-reverting regime of Model 1 more often than he believes it to be in the trending regime of Model 2. The transition probabilities associated with Models 1 and 2 and with the underlying regime-switching process are fixed in the investor's mind.In order to value the security,the investor needs to forecast future earnings.To do this,he uses the earnings stream he has observed to update his beliefs about which regime is generating earnings.Once this is done,he uses the regime-switching model to forecast future earnings.The investor updates in a Bayesian fashion even though his model of earnings is incorrect.For instance,if he observes two consecutive earnings shocks of the same sign,he believes more strongly that he is in the trending earnings regime of Model 2.If the earnings shock this period is of the opposite sign to last period's earnings shock,he puts more weight on Model 1,the mean-reverting regime. Our model differs from more typical models of learning.In our framework, the investor never changes the model he is using to forecast earnings,but rather uses the same regime-switching model,with the same regimes and transition probabilities throughout.Even after observing a very long stream of earningsof the previous section. Model 1 generates effects identical to those predicted by conservatism. An investor using Model 1 to forecast earnings reacts too little to an individual earnings announcement, as would an investor exhibiting conser￾vatism. From the perspective of Griffin and Tversky (1992), there is insufficient reaction to individual earnings announcements because they are low in strength. In fact, these announcements have extremely high weight when earnings follow a random walk, but investors are insensitive to this aspect of the evidence. In contrast, the investor who believes in Model 2 behaves as if he is subject to the representativeness heuristic. After a string of positive or negative earnings changes, the investor uses Model 2 to forecast future earnings, extrapolating past performance too far into the future. This captures the way that representa￾tiveness might lead investors to associate past earnings growth too strongly with future earnings growth. In the language of Griffin and Tversky, investors overreact to the information in a string of positive or negative earnings changes since it is of high strength; they ignore the fact that it has low weight when earnings simply follow a random walk. The investor also believes that there is an underlying regime-switching pro￾cess that determines which regime the world is in at any time. We specify this underlying process as a Markov process as well, so that whether the current regime is Model 1 or Model 2 depends only on what the regime was last period. We focus attention on cases in which regime switches are relatively rare. That is, if Model 1 determines the change in earnings in period t, it is likely that it determines earnings in period t#1 also. The same applies to Model 2. With some small probability, though, the regime changes, and the other model begins generating earnings. For reasons that will become apparent, we often require the regime-switching probabilities to be such that the investor thinks that the world is in the mean-reverting regime of Model 1 more often than he believes it to be in the trending regime of Model 2. The transition probabilities associated with Models 1 and 2 and with the underlying regime-switching process are fixed in the investor’s mind. In order to value the security, the investor needs to forecast future earnings. To do this, he uses the earnings stream he has observed to update his beliefs about which regime is generating earnings. Once this is done, he uses the regime-switching model to forecast future earnings. The investor updates in a Bayesian fashion even though his model of earnings is incorrect. For instance, if he observes two consecutive earnings shocks of the same sign, he believes more strongly that he is in the trending earnings regime of Model 2. If the earnings shock this period is of the opposite sign to last period’s earnings shock, he puts more weight on Model 1, the mean-reverting regime. Our model differs from more typical models of learning. In our framework, the investor never changes the model he is using to forecast earnings, but rather uses the same regime-switching model, with the same regimes and transition probabilities throughout. Even after observing a very long stream of earnings N. Barberis et al./Journal of Financial Economics 49 (1998) 307—343 319