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...

We examine the role of the disposition effect in market efficiency following the arrival of private signals to a small group of informed traders. Subjects trade an ambiguous asset via a computer-based double auction. Using a 2x2x2 design, we endow two types of signal, i.e., positive vs....

This study examines how competition affects the profitability of mutual funds' trading on the post earnings announcement drift (PEAD). Our results show that while the average fund actively pursuing this strategy does not generate significant outperformance, funds that manage to avoid competition...

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...