Modern society is driven by electricity usage. I construct a proxy tracing regional economic intensity using electricity usage provided by utility firms in the United States from 1990 to 2020. After comparing electricity usage growth with common regional consumption measures, I find it a leading...

We apply state-of-the-art financial machine learning to assess the return-predictive value of more than 45,000 earnings announcements on a majority of S&P1500 constituents. To represent the diverse information content of earnings announcements, we generate predictor variables based on various...

Post-earnings-announcement drift (PEAD) is one of the most solidly documented asset pricing anomalies. We use the controlled conditions of an experimental lab to investigate whether earnings autocorrelation is the driving cause of this anomaly. We observe PEAD in settings with uncorrelated and...

We use trade-level data to examine the role of actively managed funds (AMFs) in earnings news dissemination. We find AMFs are drawn to, and participate disproportionately more in, earnings announcements (EAs) that include bundled managerial guidance. When the two pieces of news are directionally...