Estimating price impact via deep reinforcement learning

Abstract

AbstractPrice impact is the adverse change of the asset price against trader's action. As a crucial part of the indirect trading cost, price impact has attracted increasing attention in both econometric and data science literature. In this paper, we draw upon both strands of the literature and develop a deep neural network enhanced recursive (DeRecv) model to estimate temporary and permanent price impact of an order or trade. The temporary price impact is calculated as the sum of the expected immediate impact at each time point after taking action in an ad hoc market condition. The permanent price impact is defined as a new permanent level at which the information of the incoming order is entirely absorbed by the market. Through the experimental evaluation based on data from 10 stocks at NASDAQ and Shanghai Stock Exchange, we show that the proposed DeRecv model is better than the reinforcement learning model and the traditional vector autoregressive model.