Forecasting stock prices from limit order book using convolutional neural networks

Avraam Tsantekidis, Nikolaos Passalis, Anastasios Tefas, Juho Kanniainen, Moncef Gabbouj, Alexandros Iosifidis

    Tutkimustuotos: KonferenssiartikkeliScientificvertaisarvioitu

    81 Sitaatiot (Scopus)


    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.
    Otsikko19th IEEE International Conference on Business Informatics
    AlaotsikkoThessaloniki, Greece, 24-27 July 2017
    ISBN (elektroninen)978-1-5386-3035-8
    DOI - pysyväislinkit
    TilaJulkaistu - 2017
    OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
    TapahtumaIEEE International Conference on Business Informatics -
    Kesto: 1 tammik. 1900 → …


    ConferenceIEEE International Conference on Business Informatics
    Ajanjakso1/01/00 → …


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