Abstract
The modeling and prediction of stock prices is the core work in securities investment, and it is of enormous significance to reducing decision-making risks and improving investment returns. Existing research mainly focuses on mid or low-frequency stock price prediction, which is challenging to apply to intraday high-frequency trading scenarios. Meanwhile, the model accuracy face limitation due to the neglect of the influence of random noise and the refinement of the price sequence law. This paper proposes a high-frequency stock price prediction method based on mode decomposition and deep learning to improve intraday stock price prediction accuracy. Firstly, this method stabilizes the stock price series through empirical mode decomposition to tackle the issue of random noise interference. Then the convolutional neural network is introduced to extract the high-dimensional data features hidden in the stock price series by using multiple convolution kernels. Furthermore, the gated recurrent unit is used to process time-sequential data and to predict the stock prices at the minute level. The experimental result indicates that the proposed high-frequency stock price prediction method can achieve a significant forecasting effect, and its accuracy outperforms the existing methods.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Long, J., Chen, Z., He, W., et al.: An integrated framework of deep learning and knowledge graph for prediction of stock price trend: an application in Chinese stock exchange market. Appl. Soft Comput. 91, 106205 (2020)
Yuan, X., Yuan, J., Jiang, T., et al.: Integrated long-term stock selection models based on feature selection and machine learning algorithms for China stock market. IEEE Access 8, 22672–22685 (2020)
Aslam, S., Rasool, A., Jiang, Q., et al.: LSTM based model for real-time stock market prediction on unexpected incidents. In: 2021 IEEE International Conference on Real-time Computing and Robotics (RCAR), pp. 1149–1153. IEEE (2021)
Guo, Y., Han, S., Shen, C., et al.: An adaptive SVR for high-frequency stock price forecasting. IEEE Access 6, 11397–11404 (2018)
Akyildirim, E., Bariviera, A.F., Nguyen, D.K., et al.: Forecasting high-frequency stock returns: a comparison of alternative methods. Ann. Oper. Res. 2022, 1–52 (2022)
Liu, T., Luo, Z., Huang, J., et al.: A comparative study of four kinds of adaptive decomposition algorithms and their applications. Sensors 18(7), 2120 (2018)
Dhillon, A., Verma, G.K.: Convolutional neural network: a review of models, methodologies and applications to object detection. Prog. Artif. Intell. 9(2), 85–112 (2020)
Shen, G., Tan, Q., Zhang, H., et al.: Deep learning with gated recurrent unit networks for financial sequence predictions. Proc. Comput. Sci. 131, 895–903 (2018)
Liu, Z.: Strategy optimization of stock technical analysis under high-frequency trading: method based on Adaboost neural network. Shanghai Normal University (2019)
Gu, Y., Yan, D.: Price forecast with high-frequency finance data: an autoregressive recurrent neural network model with technical indicators. In: Proceedings of the 29th ACM International Conference on Information & Knowledge Management, pp. 2485–2492 (2020)
Kong, A., Zhu, H., Azencott, R.: Predicting intraday jumps in stock prices using liquidity measures and technical indicators. J. Forecast. 40(3), 416–438 (2021)
Kumar Chandar, S.: Stock price prediction based on technical indicators with soft computing models. In: Chen, J.-Z., Tavares, J.M.R.S., Shakya, S., Iliyasu, A.M. (eds.) ICIPCN 2020. AISC, vol. 1200, pp. 685–699. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-51859-2_62
Song, Y., Sun, Y.: High frequency financial time series prediction: ARIMA model based on adaptive filtering. J. Jilin Bus. Technol. Coll. 37(2), 82–86 (2021)
Li, Z., Han, J., Song, Y.: On the forecasting of high-frequency financial time series based on ARIMA model improved by deep learning. J. Forecast. 39(7), 1081–1097 (2020)
Huang, S.-F., Guo, M., Chen, M.-R.: Stock market trend prediction using a functional time series approach. Quant. Finan. 20(1), 69–79 (2020)
Taroon, G., Tomar, A., Manjunath, C., et al.: Employing deep learning in intraday stock trading. In: Fifth International Conference on Research in Computational Intelligence and Communication Networks, pp. 209–214 (2020)
SolÃs, E., Noboa, S., Cuenca, E.: Financial time series forecasting applying deep learning algorithms. In: Salgado Guerrero, J.P., Chicaiza Espinosa, J., Cerrada Lozada, M., Berrezueta-Guzman, S. (eds.) TICEC 2021. CCIS, vol. 1456, pp. 46–60. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-89941-7_4
Chong, E., Han, C., Park, F.C.: Deep learning networks for stock market analysis and prediction: methodology, data representations, and case studies. Expert Syst. Appl. 83, 187–205 (2017)
Lachiheb, O., Gouider, M.S.: A hierarchical deep neural network design for stock returns prediction. Proc. Comput. Sci. 126, 264–272 (2018)
Lanbouri, Z., Achchab, S.: Stock market prediction on high frequency data using long-short term memory. Proc. Comput. Sci. 175, 603–608 (2020)
Ma, A., Yu, T.: Technical analysis with empirical mode decomposition: a case in the Hong Kong stock market. J. Wealth Manag. 24(1), 41–48 (2021)
Chen, W., Jiang, M.: A novel graph convolutional feature based convolutional neural network for stock trend prediction. Inf. Sci. 556, 67–94 (2021)
Li, C., **ao, F., Fan, Y.: An approach to state of charge estimation of lithium-ion batteries based on recurrent neural networks with gated recurrent unit. Energies 12(9), 1592 (2019)
Hong, W., Li, S., Hu, Z.: Improving relation extraction by knowledge representation learning. In: 2021 IEEE 33rd International Conference on Tools with Artificial Intelligence, pp. 1211–1215. IEEE (2021)
Lu, W., Li, J., Wang, J., et al.: A CNN-BiLSTM-AM method for stock price prediction. Neural Comput. Appl. 33(10), 4741–4753 (2021)
Wang, J., Kim, J.: Predicting stock price trend using MACD optimized by historical volatility. Math. Probl. Eng. 2018, 1–12 (2018)
Khanderwal, S., Mohanty, D.: Stock price prediction using ARIMA model. Int. J. Mark. Hum. Resour. Res. 2(2), 98–107 (2021)
Hoseinzade, E., Haratizadeh, S.: CNNpred: CNN-based stock market prediction using a diverse set of variables. Expert Syst. Appl. 129, 273–285 (2019)
Gupta, U., Bhattacharjee, V., Bishnu, P.S.: StockNet-GRU based stock index prediction. Expert Syst. Appl. 207, 117986 (2022)
Funding
This work is supported by the National Key Research and Development Program of China under fund number 2021YFF1200104, and the Key Technologies Research and Development Program of Guangdong Province under fund number 2020B010165003.
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Chen, W., Jiang, Q., Jia, X., Rasool, A., Jiang, W. (2023). A High-Frequency Stock Price Prediction Method Based on Mode Decomposition and Deep Learning. In: Tian, Y., Ma, T., Jiang, Q., Liu, Q., Khan, M.K. (eds) Big Data and Security. ICBDS 2022. Communications in Computer and Information Science, vol 1796. Springer, Singapore. https://doi.org/10.1007/978-981-99-3300-6_5
Download citation
DOI: https://doi.org/10.1007/978-981-99-3300-6_5
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-3299-3
Online ISBN: 978-981-99-3300-6
eBook Packages: Computer ScienceComputer Science (R0)