VINCENT BOGOUSSLAVSKY, VYACHESLAV FOS, DMITRIY MURAVYEV
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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.
期刊介绍：
The Journal of Finance is a renowned publication that disseminates cutting-edge research across all major fields of financial inquiry. Widely regarded as the most cited academic journal in finance, each issue reaches over 8,000 academics, finance professionals, libraries, government entities, and financial institutions worldwide. Published bi-monthly, the journal serves as the official publication of The American Finance Association, the premier academic organization dedicated to advancing knowledge and understanding in financial economics. Join us in exploring the forefront of financial research and scholarship.
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