Abstract
This paper develops a structural model to examine high-frequency price dynamics. The key innovation is to allow trades’ permanent price impact to be time-varying—dynamic trade informativeness. A distribution-free filtering technique pins the real-world data to the model. The filtered series significantly recover the efficient price innovation through the dynamics of trade informativeness, improve trades’ explanatory power for future returns, distinguish informativeness from trades’ aggressiveness, gauge informed investors’ patience, and capture systematic patterns around scheduled and unscheduled events, as well as general intraday trends. The framework contributes to the better utilization of high-frequency trading data.
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