Boroumand - Department of Finance, Esfarayen Branch, Islamic Azad University, Esfarayen, Iran
Doaei - Department of Finance, Esfarayen Branch, Islamic Azad University, Esfarayen, Iran.

For investors, predicting stock market changes has always been attractive and challenging because it helps them accurately identify profits and reduce potential risks. Deep learning-based models, as a subset of machine learning, receive attention in the field of price prediction through the improvement of traditional neural network models. In this paper, we propose a model for predicting stock prices of Tehran Stock Exchange companies using a long-short-term memory (LSTM) deep neural network. The model consists of two LSTM layers, one Dense layer, and two DropOut layers. In this study, using our studies and evaluations, the adjusted stock price with ۱۲ technical index variables was taken as an input for the model. In assessing the model's predictive outcomes, we considered RMSE, MAE, and MAPE as criteria. According to the results, integrating technical indicators increases the model's accuracy in predicting the stock price, with the LSTM model outperforming the RNN model in this task.

Stock price prediction, Artificial Neural Networks, Deep learning, Long Short-Term Memory, Recurrent Neural Networks