We study the effects of belief dispersion on stock trading volume. Unlike most of the existing work on the subject, our paper focuses on how household investors' disagreements on macroeconomic variables influence market-wide trading volume. We show that greater belief dispersion among household...

We study the dynamics of a Lucas-tree model with finitely lived individuals who "learn from experience." Individuals update expectations by Bayesian learning based on observations from their own lifetimes. In this model, the stock price exhibits stochastic fluctuations around the rational...

We study the asset pricing implications of a general equilibrium Lucas endowment economy inhabited by two agents with habit formation preferences. Preferences are modeled either as internal or external habits. We allow for agents' heterogeneity in relative risk aversion and habit strength. We...

Beaudry and Portier (American Economoc Review, 2006) propose an identification scheme to study the effects of news shocks about future productivity in Vector Error Correction Models (VECM). This comment shows that their methodology does not have a unique solution, when applied to their VECMs...

Many theories of asset prices assume time-varying uncertainty in order to generate time-varying risk premia. This paper generates time-varying uncertainty endogenously, through precautionary saving dynamics. Precautionary motives prescribe that, in bad times, next period's consumption should be...

We document a robust positive relationship between the belief dispersion about macroeconomic conditions among household investors and the stock market trading volume, using more than 30 years of household survey data and a novel approach to measuring belief dispersions. Notably, such a...

We propose a method to decompose stock returns period by period. First, we argue that one can directly estimate expected stock returns from securities available in modern financial markets (using the real yield curve and the Martin (2017) equity risk premium). Second, we derive a return...

While many studies find that the tail distribution of high frequency stock returns follow a power law, there are only a few explanations for this finding. This study presents evidence that time-varying volatility can account for the power law property of high frequency stock returns. The power...

This paper uses a dataset of more than 900,000 news stories to test whether news can predict stock returns. We measure sentiment with a proprietary Thomson-Reuters neural network. We find that daily news predicts stock returns for only 1 to 2 days, confirming previous research. Weekly news,...

Among growing concerns about potential financial stability risks posed by the asset management industry, herding has been considered as an important risk amplification channel. In this paper, we examine the extent to which institutional investors herd in their trading of U.S. corporate bonds and...