A Novel Approach of Stock Price Forecast Using Deep Learning Practices


  • 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


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


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.


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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



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