A Hybrid Genetic Algorithm and Deep Learning Architecture for Banking Stock Trend Prediction

Author(s)	Dr. T. Saritha
Country	India
Abstract	This paper proposes a hybrid model integrating Genetic Algorithms (GA) with Deep Learning (DL) for predicting banking stock trends. Traditional stock prediction models often fail to capture nonlinear dependencies, leading to suboptimal results. Deep learning models, particularly Long Short-Term Memory (LSTM) networks, have demonstrated superior predictive capabilities but require careful hyperparameter optimization. Genetic Algorithms provide a robust approach for global optimization of these hyperparameters, enabling better model accuracy and generalization. A synthetic dataset of 1,000 stock records was generated to simulate realistic banking stock trends. The results indicate that the hybrid GA-DL architecture outperforms standalone deep learning models in prediction accuracy and stability. Future work will focus on applying this approach to real-world financial data streams and integrating reinforcement learning for adaptive predictions.
Keywords	Genetic Algorithm, Deep Learning, LSTM, Stock Trend Prediction, Banking Stocks, Hybrid Models.
Field	Mathematics > Statistics
Published In	Volume 17, Issue 1, January-June 2026
Published On	2026-01-12
Cite This	A Hybrid Genetic Algorithm and Deep Learning Architecture for Banking Stock Trend Prediction - Dr. T. Saritha - IJAIDR Volume 17, Issue 1, January-June 2026.

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