We study a newly designed survey administered to a large panel of wealthy retail investors.
The survey elicits beliefs that are important for macroeconomics and finance, and matches …

We study the rationality of individual and consensus forecasts of macroeconomic and
financial variables using the methodology of Coibion and Gorodnichenko (2015), who …

We offer a systematic analysis of the use of deep learning networks for stock market analysis
and prediction. Its ability to extract features from a large set of raw data without relying on …

Commonly used instruments for the identification of monetary policy disturbances are likely
to combine the true policy shock with information about the state of the economy due to the …

We develop a large-scale deep learning model to predict price movements from limit order
book (LOB) data of cash equities. The architecture utilizes convolutional filters to capture the …

It surprised investors, who dumped stocks and brought the market index down by 500 points
on Monday. It surprised policymakers, who rushed to rescue other financial institutions after …

We show that mutual fund investors rely on simple signals and likely do not engage in
sophisticated learning about managers' alpha as widely believed. Simplistic performance …

Behavioral finance tries to make sense of financial data using models that are based on
psychologically accurate assumptions about people's beliefs, preferences, and cognitive …

We analyze time series of investor expectations of future stock market returns from six data
sources between 1963 and 2011. The six measures of expectations are highly positively …

Survey evidence suggests that many investors form beliefs about future stock market returns
by extrapolating past returns. Such beliefs are hard to reconcile with existing models of the …