An analysis of about 300000 earnings forecasts, created by 18000 individual forecasters for earnings of over 300 S&P listed firms, shows that these forecasts are predictable to a large extent using a statistical model that includes publicly available information. When we focus on the...

Using novel earnings calendar data, we show that firms' advanced scheduling of earnings announcement dates foreshadows their earnings news. Firms that schedule later-than-expected announcement dates subsequently announce worse news than those scheduling earlier-than-expected announcement dates....

The purpose of this paper is to analyse the predictability of earnings information before the quarterly disclosure date. Two categories of firms are contrasted: the firms that announce better quarterly earnings than the prior period and the firms that do not. The paper uses a sample of 67...

We develop a measure of how information events impact investors' perceptions of risk that is broadly applicable and simple to implement. We derive this measure from an option-pricing model where investors anticipate an announcement that simultaneously conveys information on the announcer's...

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...