Forecasting stock prices from limit order book using convolutional neural networks

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

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Forecasting stock prices from limit order book using convolutional neural networks. / Tsantekidis, Avraam; Passalis, Nikolaos; Tefas, Anastasios; Kanniainen, Juho ; Gabbouj, Moncef; Iosifidis, Alexandros.

19th IEEE International Conference on Business Informatics: Thessaloniki, Greece, 24-27 July 2017. IEEE, 2017.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

Harvard

Tsantekidis, A, Passalis, N, Tefas, A, Kanniainen, J , Gabbouj, M & Iosifidis, A 2017, Forecasting stock prices from limit order book using convolutional neural networks. in 19th IEEE International Conference on Business Informatics: Thessaloniki, Greece, 24-27 July 2017. IEEE, IEEE International Conference on Business Informatics, 1/01/00. https://doi.org/10.1109/CBI.2017.23

APA

Tsantekidis, A., Passalis, N., Tefas, A., Kanniainen, J., Gabbouj, M., & Iosifidis, A. (2017). Forecasting stock prices from limit order book using convolutional neural networks. In 19th IEEE International Conference on Business Informatics: Thessaloniki, Greece, 24-27 July 2017 IEEE. https://doi.org/10.1109/CBI.2017.23

Vancouver

Tsantekidis A, Passalis N, Tefas A, Kanniainen J , Gabbouj M, Iosifidis A. Forecasting stock prices from limit order book using convolutional neural networks. In 19th IEEE International Conference on Business Informatics: Thessaloniki, Greece, 24-27 July 2017. IEEE. 2017 https://doi.org/10.1109/CBI.2017.23

Author

Tsantekidis, Avraam ; Passalis, Nikolaos ; Tefas, Anastasios ; Kanniainen, Juho ; Gabbouj, Moncef ; Iosifidis, Alexandros. / Forecasting stock prices from limit order book using convolutional neural networks. 19th IEEE International Conference on Business Informatics: Thessaloniki, Greece, 24-27 July 2017. IEEE, 2017.

Bibtex - Download

@inproceedings{10cbd44ba7f649bdbe3a5be98f1c9bd4,

title = "Forecasting stock prices from limit order book using convolutional neural networks",

abstract = "In today's financial markets, where most trades are performed in their entirety by electronic means and the largest fraction of them is completely automated, an opportunity has risen from analyzing this vast amount of transactions. Since all the transactions are recorded in great detail, investors can analyze all the generated data and detect repeated patterns of the price movements. Being able to detect them in advance, allows them to take profitable positions or avoid anomalous events in the financial markets. In this work we proposed a deep learning methodology, based on Convolutional Neural Networks (CNNs), that predicts the price movements of stocks, using as input large-scale, high-frequency time-series derived from the order book of financial exchanges. The dataset that we use contains more than 4 million limit order events and our comparison with other methods, like Multilayer Neural Networks and Support Vector Machines, shows that CNNs are better suited for this kind of task.",

author = "Avraam Tsantekidis and Nikolaos Passalis and Anastasios Tefas and Juho Kanniainen and Moncef Gabbouj and Alexandros Iosifidis",

note = "INT=sgn,{"}Tsantekidis, Avraam{"}",

year = "2017",

doi = "10.1109/CBI.2017.23",

language = "English",

booktitle = "19th IEEE International Conference on Business Informatics",

publisher = "IEEE",

}

RIS (suitable for import to EndNote) - Download

TY - GEN

T1 - Forecasting stock prices from limit order book using convolutional neural networks

AU - Tsantekidis, Avraam

AU - Passalis, Nikolaos

AU - Tefas, Anastasios

AU - Kanniainen, Juho

AU - Gabbouj, Moncef

AU - Iosifidis, Alexandros

N1 - INT=sgn,"Tsantekidis, Avraam"

PY - 2017

Y1 - 2017

N2 - In today's financial markets, where most trades are performed in their entirety by electronic means and the largest fraction of them is completely automated, an opportunity has risen from analyzing this vast amount of transactions. Since all the transactions are recorded in great detail, investors can analyze all the generated data and detect repeated patterns of the price movements. Being able to detect them in advance, allows them to take profitable positions or avoid anomalous events in the financial markets. In this work we proposed a deep learning methodology, based on Convolutional Neural Networks (CNNs), that predicts the price movements of stocks, using as input large-scale, high-frequency time-series derived from the order book of financial exchanges. The dataset that we use contains more than 4 million limit order events and our comparison with other methods, like Multilayer Neural Networks and Support Vector Machines, shows that CNNs are better suited for this kind of task.

AB - In today's financial markets, where most trades are performed in their entirety by electronic means and the largest fraction of them is completely automated, an opportunity has risen from analyzing this vast amount of transactions. Since all the transactions are recorded in great detail, investors can analyze all the generated data and detect repeated patterns of the price movements. Being able to detect them in advance, allows them to take profitable positions or avoid anomalous events in the financial markets. In this work we proposed a deep learning methodology, based on Convolutional Neural Networks (CNNs), that predicts the price movements of stocks, using as input large-scale, high-frequency time-series derived from the order book of financial exchanges. The dataset that we use contains more than 4 million limit order events and our comparison with other methods, like Multilayer Neural Networks and Support Vector Machines, shows that CNNs are better suited for this kind of task.

U2 - 10.1109/CBI.2017.23

DO - 10.1109/CBI.2017.23

M3 - Conference contribution

BT - 19th IEEE International Conference on Business Informatics

PB - IEEE

ER -

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