High-Frequency Stock Price Prediction Using Hybrid CNN-LSTM  Deep Learning Model

Anurag Singh ,Dr. Abhay Shukla

High-Frequency Stock Price Prediction Using Hybrid CNN-LSTM Deep Learning Model

Authors

Anurag Singh ,Dr. Abhay Shukla

Keywords:

Machine Learning, Deep Learning, Convolutional Neural Networks, Long Short Term Memory.

Abstract

High-frequency financial markets generate very fast-changing price patterns that are hard tomodel because of their non-linear behavior and microstructure noise. Classical time series analysis andconventional deep learning models such as DNN are not able to model both short and long-term dependencies at the minute and tick level.

References

Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780.

Nelson, D. M. Q., Pereira, A. C. M., & de Oliveira, R. A. (2017). Stock market’s price movement prediction with LSTM neural networks. 2017 International Joint Conference on Neural Networks (IJCNN), 1419–1426.

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Published

2024-12-20

How to Cite

Anurag Singh ,Dr. Abhay Shukla. (2024). High-Frequency Stock Price Prediction Using Hybrid CNN-LSTM Deep Learning Model. Journal of Computational Analysis and Applications (JoCAAA), 33(08), 8486–8495. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/5349