A Novel Approach of Stock Price Forecast Using Deep Learning Practices

Authors

  • Minakshi Tomer Assistant Professor, Information Technology Department, Maharaja Surajmal Institute of Technology
  • Tripti Rathee Assistant Professor, Department of Information Technology, Maharaja Surajmal Institute of Technology
  • Harjit Singh Assistant Professor, APSN Campus, Punjabi University Patiala (Punjab) - India
  • Nandhini Mahadevan Assistant professor, Department of Computer science and Engineering (Data Science) Madanapalle Institute of technology & Science, Andhra Pradesh
  • Gurwinder Singh Associate Professor, Department of AIT-CSE, Chandigarh University, Punjab, India.
  • B. Kameswara Rao Associate Professor, Department of Computer Science and Engineering, Gandhi Institute of Technology and Management, GITAM (Deemed to be University) Visakhapatnam Campus, Andhra Pradesh, Visakhapatnam,530045, India

Keywords:

Stock Market Prediction, Deep Learning Techniques, LSTM, Financial Decision-Making

Abstract

The intricate dynamics of stock market data present an ongoing challenge for accurate forecasting, underscoring the need for advanced predictive models. This research paper explores the application of deep learning techniques, specifically focusing on LSTM, to enhance the prediction of complex stock market movements. By delving into historical data, this study aims to develop a robust predictive model capable of capturing intricate patterns and trends, thus providing valuable insights for investors, traders, and financial analysts. Recognizing the critical role of accurate predictions in financial decision-making, the research emphasizes the potential impact of leveraging deep learning in the stock market domain. The study underscores the importance of staying ahead in an ever-changing market landscape, where the ability to anticipate market movements is crucial. To address this, the research adopts the LSTM technique, a specialized recurrent neural network architecture known for its efficacy in handling sequential data and capturing long-term dependencies. This approach is expected to contribute significantly to advancing the precision and efficiency of stock market predictions, empowering stakeholders with valuable tools for navigating the complexities of financial markets.

Downloads

Download data is not yet available.

References

Jiang, M., Chen, W., Xu, H., & Liu, Y. (2024). A novel interval dual convolutional neural network method for interval-valued stock price prediction. Pattern Recognition, 145, 109920.

Peng, P., Chen, Y., Lin, W., & Wang, J. Z. (2024). Attention-based CNN–LSTM for high-frequency multiple cryptocurrency trend prediction. Expert Systems with Applications, 237, 121520.

Lu, M., & Xu, X. (2024). TRNN: An efficient time-series recurrent neural network for stock price prediction. Information Sciences, 657, 119951.

Pourdaryaei, A., Mohammadi, M., Mubarak, H., Abdellatif, A., Karimi, M., Gryazina, E., & Terzija, V. (2024). A new framework for electricity price forecasting via multi-head self-attention and CNN-based techniques in the competitive electricity market. Expert Systems with Applications, 235, 121207.

Li, B., Lv, X., & Chen, J. (2024). Demand and supply gap analysis of Chinese new energy vehicle charging infrastructure: Based on CNN-LSTM prediction model. Renewable Energy, 220, 119618.

Ghimire, S., Deo, R. C., Casillas-Pérez, D., & Salcedo-Sanz, S. (2024). Two-step deep learning framework with error compensation technique for short-term, half-hourly electricity price forecasting. Applied Energy, 353, 122059.

Ahmed, A. A. M., Jui, S. J. J., AL-Musaylh, M. S., Raj, N., Saha, R., Deo, R. C., & Saha, S. K. (2024). Hybrid deep learning model for wave height prediction in Australia's wave energy region. Applied Soft Computing, 150, 111003.

Zhou, F., Liu, X., Jia, C., Li, S., Tian, J., Zhou, W., & Wu, C. (2024). Unified CNN-LSTM for keyhole status prediction in PAW based on spatial-temporal features. Expert Systems with Applications, 237, 121425.

Chen, G., Tian, H., Xiao, T., Xu, T., & Lei, H. (2024). Time series forecasting of oil production in Enhanced Oil Recovery system based on a novel CNN-GRU neural network. Geoenergy Science and Engineering, 233, 212528.

Eapen, J., Bein, D., & Verma, A. (2019, January). Novel deep learning model with CNN and bi-directional LSTM for improved stock market index prediction. In 2019 IEEE 9th annual computing and communication workshop and conference (CCWC) (pp. 0264-0270). IEEE.

Yang, C., Zhai, J., & Tao, G. (2020). Deep learning for price movement prediction using convolutional neural network and long short-term memory. Mathematical Problems in Engineering, 2020.

Zulqarnain, M., Ghazali, R., Ghouse, M. G., Hassim, Y. M. M., & Javid, I. (2020). Predicting financial prices of stock market using recurrent convolutional neural networks. International Journal of Intelligent Systems and Applications (IJISA), 12(6), 21-32.

Mehtab, S., Sen, J., & Dasgupta, S. (2020, November). Robust analysis of stock price time series using CNN and LSTM-based deep learning models. In 2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA) (pp. 1481-1486). IEEE.

Shu, W., & Gao, Q. (2020). Forecasting stock price based on frequency components by EMD and neural networks. Ieee Access, 8, 206388-206395.

Lee, C. Y., & Soo, V. W. (2017, December). Predict stock price with financial news based on recurrent convolutional neural networks. In 2017 conference on technologies and applications of artificial intelligence (TAAI) (pp. 160-165). IEEE.

Downloads

Published

07.02.2024

How to Cite

Tomer, M. ., Rathee, T. ., Singh, H. ., Mahadevan, N. ., Singh, G. ., & Rao, B. K. . (2024). A Novel Approach of Stock Price Forecast Using Deep Learning Practices. International Journal of Intelligent Systems and Applications in Engineering, 12(15s), 594–603. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4809

Issue

Vol. 12 No. 15s (2024)

Section

Research Article

License

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.

IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.

Most read articles by the same author(s)