Real-Time Forecasting of Stock Trends using Particle Swarm Optimization
Suraj Kumar Sahu1, Zubair Ahmed Khan2, Abhishek Guru3, Ankita Singh Baghel4, Divya Soni5
1Suraj Kumar Sahu, Assistant Professor, Department of Computer Science and Engineering, Mats School of Engineering and Technology, MATS University, Raipur (Chhattisgarh), India.
2Dr. Zubair Ahmed Khan, Assistant Professor, Department of Computer Science and Engineering, Mats School of Engineering and Technology, MATS University, Raipur (Chhattisgarh), India.
3Dr. Abhishek Guru, Assistant Professor, Department of Computer Science and Engineering, Mats School of Engineering and Technology, MATS University, Raipur (Chhattisgarh), India.
4Ankita Singh Baghel, Department of Computer Science and Engineering, Mats School of Engineering and Technology, MATS University, Raipur (Chhattisgarh), India.
5Divya Soni, Department of Computer Science and Engineering, Mats School of Engineering and Technology, MATS University, Raipur (Chhattisgarh), India.
Manuscript received on 16 April 2025 | First Revised Manuscript received on 24 April 2025 | Second Revised Manuscript received on 19 October 2025 | Manuscript Accepted on 15 November 2025 | Manuscript published on 30 November 2025 | PP: 26-30 | Volume-5 Issue-2, November 2025 | Retrieval Number: 100.1/ijef.A262105010525 | DOI: 10.54105/ijef.A2621.05021125
Open Access | Editorial and Publishing Policies | Cite | Zenodo | OJS | Indexing and Abstracting
© The Authors. Published by Lattice Science Publication (LSP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The traditional neural network algorithm applied to stock price forecasting is prone to falling into local optima. To enhance the accuracy of stock price forecasting and reduce forecasting time, this paper introduces an improved Particle Optimisation Neural Network Algorithm. By integrating neural networks and particle swarm optimisation algorithms, a more effective forecasting model is constructed that better reflects the dynamic changes in stock prices. Meanwhile, introducing chaos interference factors and mutation factors can enhance the diversity of the algorithm, thereby further improving the accuracy and stability of the forecasting. This method presents a novel solution for research and application in stock price forecasting, offering a valuable reference for relevant practitioners.
Keywords: Stock Market Forecasting, Real-Time Prediction, Stock Trend Analysis, PSO, Algorithmic Trading.
Scope of the Article: Finance