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Optimal Execution Strategy for Large Orders in Big Data: Order Type using Q-learning Considerations

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Abstract

Investors who intend to execute large orders have to always a trade-off between price impact and opportunity cost in Big Data. In this study, reinforcement learning (Q-learning) is applied, due to its strength to support an agent, to make the best decision and take suitable action in a dynamic environment to achieve an optimal way to execute a large number of orders in a day trade. Through Q-learning algorithm, the agent has learned the kind of order yet by how much of it should be submitted in each step to achieve the optimum volume-weighted average price, as the objective function of learning in this study. Historical data of shares in Tehran Stock Exchange has been used to consider the possibility of order types to set the parameters of the simulated trading market, and the price impact on large orders has also been considered through this simulated Big Data to make it with higher accuracy. Results show that for a large order both on buy-side and sell-side separately, execution strategy adopting with multiple order types could be more appropriate compared with using a single order type of execution strategy. This case study suggests that passive order type leads the trader to achieve better results. Compared to the market, the optimal strategy has managed to reduce the volume weighted average price (transaction costs) by 0.95 percent on buy-side and increase it 1.31 percent on sell-side.

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Acknowledgements

This work is supported in part by the National Natural Science Foundation of China under Grants 61632009 & 61472451, in part by the Guangdong Provincial Natural Science Foundation under Grant 2017A030308006 and High-Level Talents Program of Higher Education in Guangdong Province under Grant 2016ZJ01.

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Authors and Affiliations

  1. School of Computer Science, Guangzhou University, Guangzhou, 510006, China

    Amir Javadpour & Guojun Wang

  2. School Financial Engineering, Faculty of Financial Science, Kharazmi University, Tehran, 1477893855, Iran

    Kh Saedifar

  3. Department of Computer Science and Information Engineering, Providence University, Taichung City, Taiwan

    Kuan-Ching Li

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  1. Amir Javadpour
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  2. Kh Saedifar
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  3. Guojun Wang
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  4. Kuan-Ching Li
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Correspondence to Guojun Wang.

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Appendix

Appendix

See Fig. 6.

Fig. 6: a
figure 6

The Proposed flowchart, b Proposed algorithm

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Javadpour, A., Saedifar, K., Wang, G. et al. Optimal Execution Strategy for Large Orders in Big Data: Order Type using Q-learning Considerations. Wireless Pers Commun 112, 123–148 (2020). https://doi.org/10.1007/s11277-019-07019-0

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