Informed Trading Intensity

• 成果类型: Article
• 署名作者: Bogousslavsky, Vincent; Fos, Vyacheslav; Muravyev, Dmitriy
• 署名单位: Boston College; Michigan State University; Michigan State University's Broad College of Business
• 刊物名称: JOURNAL OF FINANCE
• ISSN/ISSBN: 0022-1082
• DOI: 10.1111/jofi.13320
• 发表日期: 2024
• 页码: 903-948
• 关键词: Information asymmetry; stock returns; price; liquidity; MARKET; Activism; options; volume; cost; disclosure

摘要

We train a machine learning method on a class of informed trades to develop a new measure of informed trading, informed trading intensity (ITI). ITI increases before earnings, mergers and acquisitions, and news announcements, and has implications for return reversal and asset pricing. ITI is effective because it captures nonlinearities and interactions between informed trading, volume, and volatility. This data-driven approach can shed light on the economics of informed trading, including impatient informed trading, commonality in informed trading, and models of informed trading. Overall, learning from informed trading data can generate an effective informed trading measure.