nep-mst New Economics Papers
on Market Microstructure
Issue of 2021‒12‒06
three papers chosen by
Thanos Verousis

  1. Deep Learning Market Microstructure: Dual-Stage Attention-Based Recurrent Neural Networks By Chaeshick Chung; Sukjin Park
  2. FinRL-Podracer: High Performance and Scalable Deep Reinforcement Learning for Quantitative Finance By Zechu Li; Xiao-Yang Liu; Jiahao Zheng; Zhaoran Wang; Anwar Walid; Jian Guo
  3. The Geography of Investor Attention By Stefano Mengoli; Marco Pagano; Pierpaolo Pattitoni

  1. By: Chaeshick Chung (Department of Economics, Sogang University); Sukjin Park (Department of Economics, Sogang University)
    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
    JEL: G10 G14 G17
    Date: 2021
  2. By: Zechu Li; Xiao-Yang Liu; Jiahao Zheng; Zhaoran Wang; Anwar Walid; Jian Guo
    Abstract: Machine learning techniques are playing more and more important roles in finance market investment. However, finance quantitative modeling with conventional supervised learning approaches has a number of limitations. The development of deep reinforcement learning techniques is partially addressing these issues. Unfortunately, the steep learning curve and the difficulty in quick modeling and agile development are impeding finance researchers from using deep reinforcement learning in quantitative trading. In this paper, we propose an RLOps in finance paradigm and present a FinRL-Podracer framework to accelerate the development pipeline of deep reinforcement learning (DRL)-driven trading strategy and to improve both trading performance and training efficiency. FinRL-Podracer is a cloud solution that features high performance and high scalability and promises continuous training, continuous integration, and continuous delivery of DRL-driven trading strategies, facilitating a rapid transformation from algorithmic innovations into a profitable trading strategy. First, we propose a generational evolution mechanism with an ensemble strategy to improve the trading performance of a DRL agent, and schedule the training of a DRL algorithm onto a GPU cloud via multi-level mapping. Then, we carry out the training of DRL components with high-performance optimizations on GPUs. Finally, we evaluate the FinRL-Podracer framework for a stock trend prediction task on an NVIDIA DGX SuperPOD cloud. FinRL-Podracer outperforms three popular DRL libraries Ray RLlib, Stable Baseline 3 and FinRL, i.e., 12% \sim 35% improvements in annual return, 0.1 \sim 0.6 improvements in Sharpe ratio and 3 times \sim 7 times speed-up in training time. We show the high scalability by training a trading agent in 10 minutes with $80$ A100 GPUs, on NASDAQ-100 constituent stocks with minute-level data over 10 years.
    Date: 2021–11
  3. By: Stefano Mengoli (University of Bologna); Marco Pagano (University of Naples Federico II, CSEF and EIEF); Pierpaolo Pattitoni (University of Bologna)
    Abstract: Retail investors pay over twice as much attention to local companies than non-local ones, based on Google searches. News volume and volatility amplify this attention gap. Attention appears causally related to perceived proximity: first, acquisition by a nonlocal company is associated with less attention by locals, and more by nonlocals close to the acquirer; second, COVID-19 travel restrictions correlate with a drop in relative attention to nonlocal companies, especially in locations with fewer flights after the outbreak. Finally, local attention predicts volatility, bid-ask spreads and nonlocal attention, not viceversa. These findings are consistent with local investors having an information-processing advantage.
    Date: 2021

This nep-mst issue is ©2021 by Thanos Verousis. It is provided as is without any express or implied warranty. It may be freely redistributed in whole or in part for any purpose. If distributed in part, please include this notice.
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