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Informed Trading Intensity

Author

Listed:
  • VINCENT BOGOUSSLAVSKY
  • VYACHESLAV FOS
  • DMITRIY MURAVYEV

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.

Suggested Citation

  • Vincent Bogousslavsky & Vyacheslav Fos & Dmitriy Muravyev, 2024. "Informed Trading Intensity," Journal of Finance, American Finance Association, vol. 79(2), pages 903-948, April.
    • Handle: RePEc:bla:jfinan:v:79:y:2024:i:2:p:903-948
    DOI: 10.1111/jofi.13320
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