Firm-level variables that predict cross-sectional stock returns, such as price-to-earnings and short interest, are often averaged and used to predict the time series of market returns. We extend this literature and limit the data-snooping bias by using a large population of the literature's...

We study the out-of-sample and post-publication return-predictability of 97 variables that academic studies show to predict cross-sectional stock returns. Portfolio returns are 26% lower out-of-sample and 58% lower post-publication. The out-of-sample decline is an upper bound estimate of data...

We study how 9 different market participants trade with respect to 130 different stock return anomalies and how each participant's trades predict returns. Retail investors trade against anomalies, while firms' and short sellers' trades agree with anomalies. Institutional portfolios are weighted...

We use the daily internet search volume from millions of households to reveal market-level sentiment. By aggregating the volume of queries related to household concerns (e.g. "recession", "unemployment" and "bankruptcy"), we construct a Financial and Economic Attitudes Revealed by Search (FEARS)...

We use data from the Survey of Professional Forecasters to compare point forecasts of GDP growth and inflation with the subjective probability distributions held by forecasters. We find that SPF forecasters summarize their underlying distributions in different ways and that their summaries tend...