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.

中文翻译：

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知情交易强度

我们在一类知情交易上训练机器学习方法，以开发一种新的知情交易衡量标准，即知情交易强度（ITI）。ITI 在盈利、并购和新闻公告之前增加，并对回报逆转和资产定价产生影响。ITI 之所以有效，是因为它捕捉了知情交易、交易量和波动性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，包括不耐烦的知情交易、知情交易的共性以及知情交易的模型。总体而言，从知情交易数据中学习可以产生有效的知情交易措施。