A Hybrid Method for Stock Price Prediction in the Iranian Stock Market Using Optimized Deep Learning

Abstract :

Accurate stock price prediction has consistently been one of the fundamental challenges in financial markets, and the development of intelligent models can play a significant role in supporting investors’ decision-making processes. In this study, a hybrid deep learning–based framework is proposed for stock price prediction in the Iranian capital market. The proposed approach employs a Convolutional Neural Network (CNN) as the core learning architecture and integrates the Harris Hawks Optimization (HHO) algorithm as a metaheuristic strategy to optimize the model’s weights and parameters. The primary objective of this integration is to enhance prediction accuracy while reducing computational complexity through automatic feature extraction within the intermediate layers of the network. The dataset used in this research consists of daily stock information of Bahman Khodro Company from 18/01/1380 to 23/12/1399 (Persian calendar), including variables such as the number of transactions, trading volume, trading value, and prices (previous, opening, closing, final, lowest, and highest). Simulation results demonstrate that the proposed CNN-HHO model outperforms conventional neural network–based and metaheuristic-based methods, achieving a significantly lower Mean Squared Error (MSE). Overall, the findings indicate that the integration of CNN with the HHO algorithm can serve as an intelligent, accurate, and efficient approach for financial time-series forecasting, providing an effective tool for more informed decision-making in the stock market.

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