deep learning approach for stock price prediction: comparing single and hybrid models

This study utilizes deep-learning models for stock price prediction, focusing on data from five companies listed on the tehran stock exchange over the period 2001 to 2022. five models are employed, including two hybrid models and three single models. the hybrid cnn-lstm model serves as the primary model, with its predictive accuracy compared against the other four models. results indicate that the cnn-lstm model demonstrates superior performance relative to the others, although the cnn-gru hybrid model also yields satisfactory results. interestingly, among the single models, the cnn model surpasses both the lstm and gru models, defying initial expectations. the accuracy of the models is notably impacted by factors such as volatility, which increases uncertainty. this research, which exclusively relies on technical indicators, suggests that achieving optimal results hinges not only on selecting the right neural network but also on determining the appropriate number of layers in each model. overall, the cnn-lstm model delivers the best performance across four of the five stocks, with the cnn-gru model slightly outperforming it for one stock. among the single models, the cnn model consistently outperforms the others.