We find that conditional means and variances of consumption growth vary through time, and this variation appears to be associated with the business cycle. A pricing model with fluctuating means and variances of consumption growth provides implications about conditional moments of returns for both short and long investment horizons, and these implications are explored empirically. The U-shaped pattern of first-order autocorrelations of returns, as well as business cycle patterns in the price of risk, appears to be consistent with the model, but our exploration suggests that other implications about conditional return moments are at odds with the data.

This article presents a mean-variance framework for likelihood-ratio tests of asset pricing models. A pricing model is tested by examining the position of one or more reference portfolios in sample mean-standard-deviation space. Included are tests of both single-beta and multiple-beta relations, with or without a riskless asset, using either a general or a specific alternative hypothesis. Tests with a factor that is not a portfolio return are also included. The mean-variance framework is illustrated by testing the zero-beta CAPM, a two-beta pricing model, and the consumption-beta model.

[This article presents a mean-variance framework for likelihood-ratio tests of asset pricing models. A pricing model is tested by examining the position of one or more reference portfolios in sample mean-standard-deviation space. Included are tests of both single-beta and multiple-beta relations, with or without a riskless asset, using either a general or a specific alternative hypothesis. Tests with a factor that is not a portfolio return are also included. The mean-variance framework is illustrated by testing the zero-beta CAPM, a two-beta pricing model, and the consumption-beta model.]

[We analyze a unique dataset that includes the full demand schedules of 27 Israeli IPOs that were conducted as nondiscriminatory (uniform price) auctions. To the best of our knowledge, this is the first time the whole demand schedule for any asset is described. The demand schedules are relatively flat around the auction clearing price: The average elasticity is 37. The elasticity is low when the return distribution contains a large unique component. We also find a significant average abnormal return of 4.5% on the first trading day and a positive correlation between the abnormal return and the elasticity of demand.]

We analyze a unique dataset that includes the full demand schedules of 27 Israeli IPOs that were conducted as nondiscriminatory (uniform price) auctions. To the best of our knowledge, this is the first time the whole demand schedule for any asset is described. The demand schedules are relatively flat around the auction clearing price: The average elasticity is 27. The elasticity is low when the return distribution contains a large unique component. We also find a significant average abnormal return of 4.5% on the first trading day and a positive correlation between the abnormal return and the elasticity of demand.

Sample evidence about the predictability of monthly stock returns is considered from the perspective of a risk-averse Bayesian investor who must allocate funds between stocks and cash. The investor uses the sample evidence to update prior beliefs about the parameters in a regression of stock returns on a set of predictive variables. The regression relation can seem weak when described by usual statistical measures but the current values of the predictive variables can exert a substantial influence on the investor's portfolio decision, even when the investor's prior beliefs are weighted against predictability.

Sample evidence about the predictability of monthly stock returns is considered from the perspective of a risk‐averse Bayesian investor who must allocate funds between stocks and cash. The investor uses the sample evidence to update prior beliefs about the parameters in a regression of stock returns on a set of predictive variables. The regression relation can seem weak when described by usual statistical measures, but the current values of the predictive variables can exert a substantial influence on the investor's portfolio decision, even when the investor's prior beliefs are weighted against predictability.

Sample evidence about the predictability of monthly stock returns is considered from the perspective of a risk-averse Bayesian investor who must allocate funds between stocks and cash. The investor uses the sample evidence to update prior beliefs about the parameters in a regression of stock returns on a set of predictive variables. The regression relation can seem weak when described by usual statistical measures, but the current values of the predictive variables can exert a substantial influence on the investor's portfolio decision, even when the investor's prior beliefs are weighted against predictability.

The Capital Asset Pricing Model implies that (i) the market portfolio is efficient and (ii) expected returns are linearly related to betas. Many do not view these implications as separate, since either implies the other, but we demonstrate that either can hold nearly perfectly while the other fails grossly. If the index portfolio is inefficient, then the coefficients and from an ordinary least squares regression of expected returns on betas can equal essentially any values and bear no relation to the index portfolio's mean‐variance location. That location does determine the outcome of a mean‐beta regression fitted by generalized least squares.