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

DOI10.1080/14697688.2019.1622314zbMath1420.91435OpenAlexW2957390582WikidataQ127548042 ScholiaQ127548042MaRDI QIDQ5234374

Hiroyasu Matsushima, Daigo Tashiro, Hiroki Sakaji, Kiyoshi Izumi

Publication date: 26 September 2019

Published in: Quantitative Finance (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/14697688.2019.1622314

zbMATH Keywords

deep learningconvolutional neural networkmid-price trend forecast

Mathematics Subject Classification ID

Computational methods for problems pertaining to game theory, economics, and finance (91-08) Portfolio theory (91G10)

Related Items (1)

Cross-impact of order flow imbalance in equity markets

Cites Work

• The price impact of order book events: market orders, limit orders and cancellations
• Modelling high-frequency limit order book dynamics with support vector machines
• Deep learning for limit order books

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