We study how heterogeneous beliefs affect returns and examine whether they are a priced factor in traditional asset pricing models. To accomplish this task, we suggest new empirical measures based on the disagreement among analysts about expected earnings (short-term and long-term) and show they are good proxies. We first establish that the heterogeneity of beliefs matters for asset pricing and then turn our attention to estimating a structural model in which we use the forecasts of financial analysts to proxy for agents' beliefs. Finally, we investigate whether the amount of heterogeneity in analysts' forecasts can help explain asset pricing puzzles.

Because a money manager learns more about her skill from her management experience than outsiders can learn from her realized returns, she expects inefficiency in future contracts that condition exclusively on realized returns. A fund family that learns what the manager learns can reduce this inefficiency cost if the family is large enough. The family's incentive is to retain any given manager regardless of her skill but, when the family has enough managers, it adds value by boosting the credibility of its retentions through the firing of others. As the number of managers grows, the efficiency loss goes to zero.

We present a simulation-based method for solving discrete-time portfolio choice problems involving non-standard preferences, a large number of assets with arbitrary return distribution, and, most importantly, a large number of state variables with potentially path-dependent or non-stationary dynamics. The method is flexible enough to accommodate intermediate consumption, portfolio constraints, parameter and model uncertainty, and learning. We first establish the properties of the method for the portfolio choice between a stock index and cash when the stock returns are either iid or predictable by the dividend yield. We then explore the problem of an investor who takes into account the predictability of returns but is uncertain about the parameters of the data generating process. The investor chooses the portfolio anticipating that future data realizations will contain useful information to learn about the true parameter values.

We study how heterogeneous beliefs affect returns and examine whether they are a priced factor in traditional asset pricing models. To accomplish this task, we suggest new empirical measures based on the disagreement among analysts about expected earnings (short-term and long-term) and show they are good proxies. We first establish that the heterogeneity of beliefs matters for asset pricing and then turn our attention to estimating a structural model in which we use the forecasts of financial analysts to proxy for agents’ beliefs. Finally, we investigate whether the amount of heterogeneity in analysts’ forecasts can help explain asset pricing puzzles.

We present a simulation-based method for solving discrete-time portfolio choice problems involving non-standard preferences, a large number of assets with arbitrary return distribution, and, most importantly, a large number of state variables with potentially path-dependent or non-stationary dynamics. The method is flexible enough to accommodate intermediate consumption, portfolio constraints, parameter and model uncertainty, and learning. We first establish the properties of the method for the portfolio choice between a stock index and cash when the stock returns are either iid or predictable by the dividend yield. We then explore the problem of an investor who takes into account the predictability of returns but is uncertain about the parameters of the data generating process. The investor chooses the portfolio anticipating that future data realizations will contain useful information to learn about the true parameter values. Copyright 2005, Oxford University Press.

Because a money manager learns more about her skill from her management experience than outsiders can learn from her realized returns, she expects inefficiency in future contracts that condition exclusively on realized returns. A fund family that learns what the manager learns can reduce this inefficiency cost if the family is large enough. The family's incentive is to retain any given manager regardless of her skill but, when the family has enough managers, it adds value by boosting the credibility of its retentions through the firing of others. As the number of managers grows, the efficiency loss goes to zero. Copyright 2005, Oxford University Press.