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Informed Trading Intensity

Abstract
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.
Authors
Vincent Bogousslavsky Vyacheslav Fos and Dmitriy Muravyev
Keywords
Rank
0.91
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Machine Learning
Series
Journal of Finance 2024 vol. 79 issue 2 903-948
Time Added
2024/03/18 03:33
Total Downloads
0
Year Published
2024
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