• Medientyp: E-Book
  • Titel: Informed Trading Intensity
  • Beteiligte: Bogousslavsky, Vincent [VerfasserIn]; Fos, Vyacheslav [VerfasserIn]; Muravyev, Dmitriy [VerfasserIn]
  • Erschienen: [S.l.]: SSRN, 2022
  • Umfang: 1 Online-Ressource (87 p)
  • Sprache: Englisch
  • DOI: 10.2139/ssrn.3865990
  • Identifikator:
  • Schlagwörter: Informed trading ; machine learning ; adverse selection ; stock returns ; intraday data
  • Entstehung:
  • Anmerkungen: Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments December 13, 2022 erstellt
  • Beschreibung: We train a machine learning method on a class of informed trades to develop a new measure of informed trading, the Informed Trading Intensity (``ITI''). ITI increases before earnings, M&A, 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
  • Zugangsstatus: Freier Zugang