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

Chaeshick Chung () and Sukjin Park ()
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Chaeshick Chung: Department of Economics, Sogang University
Sukjin Park: Department of Economics, Sogang University

No 2108, Working Papers from Nam Duck-Woo Economic Research Institute, Sogang University (Former Research Institute for Market Economy)

Abstract: 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.

Keywords: Attention Mechanism; Deep Learning; Machine Learning; Market Mi- crostructure; Informed Trading (search for similar items in EconPapers)
JEL-codes: G10 G14 G17 (search for similar items in EconPapers)
Pages: 32 pages
Date: 2021
New Economics Papers: this item is included in nep-big, nep-cmp, nep-mst and nep-ore
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Persistent link: https://EconPapers.repec.org/RePEc:sgo:wpaper:2108

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