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

Author

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  • 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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