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

Yoonjae Noh, Jong-Min Kim, Soongoo Hong () and Sangjin Kim ()
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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
Sangjin Kim: Department of Management Information Systems, Dong-A University, Busan 49236, Republic of Korea

Mathematics, 2023, vol. 11, issue 16, 1-18

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.

Keywords: multivariate high-frequency time-series data; deep learning; ResNet; attention mechanism; stock index; S&P 500; KOSPI; DJIA (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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