We explore the impact of fake news on asset price dynamics within the asset-pricing model of Brock and Hommes (1998). By polluting the information landscape, fake news interferes with agents' perception of the dividend process of the risky asset. Our analysis reveals that fake news decreases the...

Prior literature demonstrates that an increased trading activity of a fi rm's stock is associated with abnormal future stock returns (the high-volume return premium) and interprets this phenomenon as evidence that increased visibility generates reductions in cost of capital. Motivated by this...

In this paper we investigate the predictive power of cross-sectional volatility, skewness and kurtosis for future stock returns. Adding to the work of Maio (2016), who finds cross-sectional volatility to forecast a decline in the equity premium with high predictive power in-sample as well as...

Machine learning methods for big data trade off bias for precision in prediction. To understand the implications for financial markets, I formulate an asymmetric information model where agents optimally choose a biased estimator. The model identifies a novel cost of complexity that arises...

Machine learning (ML) is a novel method that has applications in asset pricing and that fits well within the problem of measurement in economics. Unlike econometrics, ML models are not designed for parameter estimation and inference, but similar to econometrics, they address, and may be better...