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Deep Learning Model for Multivariate High-Frequency Time-Series Data: Financial Market Index Prediction

Author

Listed:
  • Yoonjae Noh

    (Department of Management Information Systems, Dong-A University, Busan 49236, Republic of Korea)

  • Jong-Min Kim

    (Division of Science and Mathematics, University of Minnesota-Morris, Morris, MN 56267, USA)

  • Soongoo Hong

    (International School, Duy Tan University, 254 Nguyen Van Linh, Danang 550000, Vietnam
    These authors contributed equally to this work.)

  • Sangjin Kim

    (Department of Management Information Systems, Dong-A University, Busan 49236, Republic of Korea
    These authors contributed equally to this work.)

Abstract

The stock index is actively used for the realization of profits using derivatives and via the hedging of assets; hence, the prediction of the index is important for market participants. As market uncertainty has increased during the COVID-19 pandemic and with the rapid development of data engineering, a situation has arisen wherein extensive amounts of information must be processed at finer time intervals. Addressing the prevalent issues of difficulty in handling multivariate high-frequency time-series data owing to multicollinearity, resource problems in computing hardware, and the gradient vanishing problem due to the layer stacking in recurrent neural network (RNN) series, a novel algorithm is developed in this study. For financial market index prediction with these highly complex data, the algorithm combines ResNet and a variable-wise attention mechanism. To verify the superior performance of the proposed model, RNN, long short-term memory, and ResNet18 models were designed and compared with and without the attention mechanism. As per the results, the proposed model demonstrated a suitable synergistic effect with the time-series data and excellent classification performance, in addition to overcoming the data structure constraints that the other models exhibit. Having successfully presented multivariate high-frequency time-series data analysis, this study enables effective investment decision making based on the market signals.

Suggested Citation

  • Yoonjae Noh & Jong-Min Kim & Soongoo Hong & Sangjin Kim, 2023. "Deep Learning Model for Multivariate High-Frequency Time-Series Data: Financial Market Index Prediction," Mathematics, MDPI, vol. 11(16), pages 1-18, August.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:16:p:3603-:d:1221074
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    References listed on IDEAS

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