Deep Learning Market Microstructure: Dual-Stage Attention-Based Recurrent Neural Networks

This paper applies the Dual-Stage Attention-Based Recurrent Neural Network(DA- RNN) model to predict future price movements using microstructure variables. The biggest feature of the DA-RNN model is that it adaptively selects relevant variables according to market conditions. We analyze whether microstructure variables have predictive power for future price movements, and what factors in uence this predic- tive power. We nd that microstructure variables possess predictive power against the direction of future price movements. This predictive power depends on how many uninformed traders exist in the market. Moreover, the importance of mi- crostructure variables is negatively related to market liquidity. Thus, while mi- crostructure variables are more important in severe market conditions with high transaction costs, the e ect of trading on price dynamics depends on market struc- ture.