Abstract :

Stock Market Prediction Is A Complex And Dynamic Task Influenced By Multiple Factors Such As Economic Indicators, Market Sentiment, And Global Events. Traditional Machine Learning Models Often Struggle To Capture Long-term Dependencies And Nonlinear Relationships In Financial Data. This Project Proposes A Hybrid Artificial Intelligence Approach That Combines Transformer Models And Quantum-Inspired Neural Networks (QINN) To Improve Stock Market Prediction Accuracy. Transformers, Known For Their Self-attention Mechanism, Are Highly Effective In Capturing Temporal Dependencies And Patterns In Time-series Data Such As Stock Prices. They Analyze Historical Trends And Identify Relationships Across Different Time Intervals. On The Other Hand, Quantum-Inspired Neural Networks Leverage Principles From Quantum Computing, Such As Superposition And Probabilistic Representation, To Model Complex Feature Interactions And Enhance Decision-making Capabilities. By Integrating These Two Approaches, The System Aims To Overcome The Limitations Of Traditional Models And Provide More Accurate Predictions. The Proposed Hybrid Model Processes Historical Stock Data, Extracts Meaningful Features, And Applies Deep Learning Techniques To Forecast Future Price Movements. Experimental Results Demonstrate Improved Prediction Accuracy, Better Generalization, And Enhanced Performance Compared To Standalone Models. However, Challenges Such As Computational Complexity And Data Volatility Remain. This Research Highlights The Potential Of Combining Advanced AI Techniques For Financial Forecasting And Provides A Scalable Framework For Intelligent Stock Market Analysis.

Published:

08-4-2026

Issue:

Vol. 26 No. 4 (2026)

Page Nos:

1814-1819

Section:

Articles

License:

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

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