We use a Panel Smooth Transition Regression (STR) model to study nonlinearities in the expectation-formation process in the U.S. stock market. To this end, we use data from the Livingston survey to investigate how the importance of regressive and extrapolative expectations fluctuates over time...

We use a Panel Smooth Transition Regression (STR) model to study nonlinearities in the expectation-formation process in the U.S. stock market. To this end, we use data from the Livingston survey to investigate how the importance of regressive and extrapolative expectations fluctuates over time...

We use a Panel Smooth Transition Regression (STR) model to study nonlinearities in the expectationformation process in the US stock market. To this end, we use data from the Livingston survey to investigate how the importance of regressive and extrapolative expectations fluctuates over time as...

This paper examines return predictability when the investor is uncertain about the right state variables. A novel feature of the model averaging approach used in this paper is to account for finite-sample bias of the coefficients in the predictive regressions. Drawing on an extensive...

Using a random sample of 60% of our cross-sectional data on U.S. stocks from 1964 to 2012, we trained four machine learning algorithms to forecast debt paydown over a one-year horizon. An evaluation of these candidate models on half of the hold-out sample (20% of the original dataset) showed...

Using 719,830 analyst recommendations from 1994 to 2017, we construct various portfolios based on levels and changes in analyst recommendations and examine how the value of those recommendations in predicting the abnormal stock returns has changed over time. We find that the predictive value of...