Tensor representation in high-frequency financial data for price change prediction

Dat Thanh Tran, Martin Magris, Juho Kanniainen, Moncef Gabbouj, Alexandros Iosifidis

    Tutkimustuotos: KonferenssiartikkeliScientificvertaisarvioitu

    13 Sitaatiot (Scopus)

    Abstrakti

    Nowadays, with the availability of massive amount of trade data collected, the dynamics of the financial markets pose both a challenge and an opportunity for high frequency traders. In order to take advantage of the rapid, subtle movement of assets in High Frequency Trading (HFT), an automatic algorithm to analyze and detect patterns of price change based on transaction records must be available. The multichannel, time-series representation of financial data naturally suggests tensor-based learning algorithms. In this work, we investigate the effectiveness of two multilinear methods for the mid-price prediction problem against other existing methods. The experiments in a large scale dataset which contains more than 4 millions limit orders show that by utilizing tensor representation, multilinear models outperform vector-based approaches and other competing ones.
    AlkuperäiskieliEnglanti
    OtsikkoIEEE Symposium Series on Computational Intelligence (SSCI), 2017
    AlaotsikkoNov. 27-Dec. 1, 2017, Hawaii, USA
    KustantajaIEEE
    Sivumäärä7
    ISBN (elektroninen)978-1-5386-2726-6
    DOI - pysyväislinkit
    TilaJulkaistu - 2017
    OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
    TapahtumaIEEE Symposium Series on Computational Intelligence -
    Kesto: 1 tammik. 1900 → …

    Conference

    ConferenceIEEE Symposium Series on Computational Intelligence
    LyhennettäIEEE SSCI
    Ajanjakso1/01/00 → …

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