TY - JOUR
T1 - Informed Trading Intensity
AU - Bogousslavsky, Vincent
AU - Fos, Vyacheslav
AU - Muravyev, Dmitriy
N1 - Publisher Copyright:
© 2024 the American Finance Association.
PY - 2024/4
Y1 - 2024/4
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85186182875&partnerID=8YFLogxK
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U2 - 10.1111/jofi.13320
DO - 10.1111/jofi.13320
M3 - Article
AN - SCOPUS:85186182875
SN - 0022-1082
VL - 79
SP - 903
EP - 948
JO - Journal of Finance
JF - Journal of Finance
IS - 2
ER -