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 characterize how informed investors trade in the options market ahead of corporate news when they receive private, but noisy, information about (i) the timing of the announcement and (ii) its impact on stock prices. Our theoretical framework generates a rich set of predictions about the...

We analyze how informed investors trade in the options market ahead of corporate news when they receive private, but noisy, information about the timing and impact of these announcements on stock prices. We propose a framework that ranks options trading strategies (option type, maturity, and...

Prior research has examined how companies exploit Twitter in communicating with investors, and whether Twitter activity predicts the stock market as a whole. We test whether opinions of individuals tweeted just prior to a firm's earnings announcement predict its earnings and announcement...

This study examines the information content of quarterly earnings announcements, measured as the magnitude of stock price revision at earnings announcements relative to price revision at other times. We investigate whether quarterly earnings announcements are informative using a nonparametric...

We investigate whether mandatory earnings announcement date forecasts are informative to investors and the informational tradeoffs between mandatory and voluntary forecasts. We find: (i) The percentages of the quarter's earnings news conveyed by mandatory China and voluntary US forecasts are...

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...

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...