知情交易强度

IF 7.6 1区经济学 Q1 BUSINESS, FINANCE

Journal of Finance Pub Date : 2024-02-27 DOI:10.1111/jofi.13320

VINCENT BOGOUSSLAVSKY, VYACHESLAV FOS, DMITRIY MURAVYEV

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引用次数: 0

摘要

我们对一类知情交易进行了机器学习方法训练，从而开发出一种新的知情交易衡量方法--知情交易强度（ITI）。ITI 在盈利、并购和新闻发布前会增加，并对回报反转和资产定价产生影响。ITI 之所以有效，是因为它捕捉到了知情交易、交易量和波动率之间的非线性和相互作用。这种数据驱动的方法可以揭示知情交易的经济学原理，包括急躁的知情交易、知情交易的共性以及知情交易的模型。总之，从知情交易数据中学习可以产生有效的知情交易衡量标准。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Informed Trading Intensity

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.

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来源期刊

Journal of Finance Multiple-

CiteScore

12.90

自引率

2.50%

发文量

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.