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Encoding of high-frequency order information and prediction of short-term stock price by deep learning

Author

Listed:
  • Daigo Tashiro
  • Hiroyasu Matsushima
  • Kiyoshi Izumi
  • Hiroki Sakaji

Abstract

Predicting the price trends of stocks based on deep learning and high-frequency data has been studied intensively in recent years. Especially, the limit order book which describes supply-demand balance of a market is used as the feature of a neural network; however these methods do not utilize the properties of market orders. On the other hand, the order-encoding method of our prior work can take advantage of these properties. In this paper, we apply some types of convolutional neural network architectures to order-based features to predict the direction of mid-price trends. The results show that smoothing filters which we propose to employ rather than embedding features of orders improve accuracy. Furthermore, inspection of the embedding layer and investment simulation are conducted to demonstrate the practicality and effectiveness of our model.

Suggested Citation

  • Daigo Tashiro & Hiroyasu Matsushima & Kiyoshi Izumi & Hiroki Sakaji, 2019. "Encoding of high-frequency order information and prediction of short-term stock price by deep learning," Quantitative Finance, Taylor & Francis Journals, vol. 19(9), pages 1499-1506, September.
    • Handle: RePEc:taf:quantf:v:19:y:2019:i:9:p:1499-1506
    DOI: 10.1080/14697688.2019.1622314
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    Citations

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    Cited by:

    1. Yang, Haijun & Xue, Feng, 2021. "Analysis of stock market volatility: Adjusted VPIN with high-frequency data," International Review of Economics & Finance, Elsevier, vol. 75(C), pages 210-222.
    2. Salman Bahoo & Marco Cucculelli & Xhoana Goga & Jasmine Mondolo, 2024. "Artificial intelligence in Finance: a comprehensive review through bibliometric and content analysis," SN Business & Economics, Springer, vol. 4(2), pages 1-46, February.
    3. Pankaj Kumar, 2021. "Deep Hawkes Process for High-Frequency Market Making," Papers 2109.15110, arXiv.org.
    4. Iwao Maeda & David deGraw & Michiharu Kitano & Hiroyasu Matsushima & Hiroki Sakaji & Kiyoshi Izumi & Atsuo Kato, 2020. "Deep Reinforcement Learning in Agent Based Financial Market Simulation," JRFM, MDPI, vol. 13(4), pages 1-17, April.
    5. Nosratabadi, Saeed & Mosavi, Amir & Duan, Puhong & Ghamisi, Pedram & Filip, Ferdinand & Band, Shahab S. & Reuter, Uwe & Gama, Joao & Gandomi, Amir H., 2020. "Data science in economics: comprehensive review of advanced machine learning and deep learning methods," MetaArXiv haf2v, Center for Open Science.
    6. Nosratabadi, Saeed & Mosavi, Amir & Duan, Puhong & Ghamisi, Pedram & Filip, Ferdinand & Band, Shahab S. & Reuter, Uwe & Gama, Joao & Gandomi, Amir H., 2020. "Data science in economics: comprehensive review of advanced machine learning and deep learning methods," SocArXiv 9vdwf, Center for Open Science.
    7. Nosratabadi, Saeed & Mosavi, Amir & Duan, Puhong & Ghamisi, Pedram & Filip, Ferdinand & Band, Shahab S. & Reuter, Uwe & Gama, Joao & Gandomi, Amir H., 2020. "Data science in economics: comprehensive review of advanced machine learning and deep learning methods," OSF Preprints yc6e2, Center for Open Science.
    8. Masanori Hirano & Kiyoshi Izumi & Takashi Shimada & Hiroyasu Matsushima & Hiroki Sakaji, 2020. "Impact Analysis of Financial Regulation on Multi-Asset Markets Using Artificial Market Simulations," JRFM, MDPI, vol. 13(4), pages 1-20, April.
    9. Nosratabadi, Saeed & Mosavi, Amir & Duan, Puhong & Ghamisi, Pedram & Filip, Ferdinand & Band, Shahab S. & Reuter, Uwe & Gama, Joao & Gandomi, Amir H., 2020. "Data science in economics: comprehensive review of advanced machine learning and deep learning methods," EdArXiv 5dwrt, Center for Open Science.
    10. Nosratabadi, Saeed & Mosavi, Amir & Duan, Puhong & Ghamisi, Pedram & Filip, Ferdinand & Band, Shahab S. & Reuter, Uwe & Gama, Joao & Gandomi, Amir H., 2020. "Data science in economics: comprehensive review of advanced machine learning and deep learning methods," LawArXiv kczj5, Center for Open Science.
    11. Nosratabadi, Saeed & Mosavi, Amir & Duan, Puhong & Ghamisi, Pedram & Filip, Ferdinand & Band, Shahab S. & Reuter, Uwe & Gama, Joao & Gandomi, Amir H., 2020. "Data science in economics: comprehensive review of advanced machine learning and deep learning methods," Thesis Commons auyvc, Center for Open Science.
    12. Iwao Maeda & David deGraw & Michiharu Kitano & Hiroyasu Matsushima & Kiyoshi Izumi & Hiroki Sakaji & Atsuo Kato, 2020. "Latent Segmentation of Stock Trading Strategies Using Multi-Modal Imitation Learning," JRFM, MDPI, vol. 13(11), pages 1-12, October.
    13. Masanori Hirano & Kiyoshi Izumi & Hiroyasu Matsushima & Hiroki Sakaji, 2020. "Comparing Actual and Simulated HFT Traders' Behavior for Agent Design," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 23(3), pages 1-6.
    14. Qinkai Chen, 2021. "Stock Movement Prediction with Financial News using Contextualized Embedding from BERT," Papers 2107.08721, arXiv.org.
    15. Saeed Nosratabadi & Amir Mosavi & Puhong Duan & Pedram Ghamisi, 2020. "Data Science in Economics," Papers 2003.13422, arXiv.org.
    16. Saeed Nosratabadi & Amirhosein Mosavi & Puhong Duan & Pedram Ghamisi & Ferdinand Filip & Shahab S. Band & Uwe Reuter & Joao Gama & Amir H. Gandomi, 2020. "Data Science in Economics: Comprehensive Review of Advanced Machine Learning and Deep Learning Methods," Mathematics, MDPI, vol. 8(10), pages 1-25, October.

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