Warp speed price moves: Jumps after earnings announcements

2025-05-01

Journal of Financial Economics

AI Summary

This study investigates whether financial earnings announcements induce price jumps in an efficient market and addresses the empirical challenges posed by microstructure noise in high-frequency data. To this end, the paper proposes a jump detection method robust to microstructure noise, integrating high-frequency data analysis, event study methodology, and co-jump identification techniques to systematically examine the impact of announcements on both individual stocks and market-wide jump behavior. The findings reveal that earnings announcements almost invariably trigger significant price jumps in the announcing firms and substantially increase the likelihood of co-jumps among non-announcing firms and the broader market. Moreover, after 2016, post-announcement trading strategies yield returns consistent with efficient price formation, supporting the efficient market hypothesis. This work provides the first evidence of cross-asset spillover effects from earnings information, offering new insights into market efficiency and information diffusion mechanisms.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Game Theory and Economic Paradigms: Auctions and Market-Based Systems
• Data Mining & Knowledge Management: Anomaly/Outlier Detection

Application Category

• Economics, Online Markets and Human Computation: Advertising auctions, pricing, markets, and exchanges
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Security and Privacy: Large-scale security measurements

Problem

• earnings announcements
• price jumps
• high-frequency data
• market efficiency
• co-jumps

Innovation

• jump test
• microstructure noise robustness
• earnings announcements
• price co-jumps
• high-frequency data