Benchmark dataset for mid‐price forecasting of limit order book data with machine learning methods

Adamantios Ntakaris, Martin Magris, Juho Kanniainen, Moncef Gabbouj, Alexandros Iosifidis

    Tutkimustuotos: ArtikkeliScientificvertaisarvioitu

    22 Sitaatiot (Scopus)
    631 Lataukset (Pure)

    Abstrakti

    Managing the prediction of metrics in high‐frequency financial markets is a challenging task. An efficient way is by monitoring the dynamics of a limit order book to identify the information edge. This paper describes the first publicly available benchmark dataset of high‐frequency limit order markets for mid‐price prediction. We extracted normalized data representations of time series data for five stocks from the Nasdaq Nordic stock market for a time period of 10 consecutive days, leading to a dataset of ∼4,000,000 time series samples in total. A day‐based anchored cross‐validation experimental protocol is also provided that can be used as a benchmark for comparing the performance of state‐of‐the‐art methodologies. Performance of baseline approaches are also provided to facilitate experimental comparisons. We expect that such a large‐scale dataset can serve as a testbed for devising novel solutions of expert systems for high‐frequency limit order book data analysis.
    AlkuperäiskieliEnglanti
    Sivut852-866
    Sivumäärä15
    JulkaisuJOURNAL OF FORECASTING
    Vuosikerta37
    Numero8
    Varhainen verkossa julkaisun päivämäärä22 elok. 2018
    DOI - pysyväislinkit
    TilaJulkaistu - jouluk. 2018
    OKM-julkaisutyyppiA1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä

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