Abdulrashid Jamnia - Department of Economics, Higher Education Complex of Saravan, Saravan, Sistan and Baluchestan province (IRAN)
Mohammad Reza Sasouli - Department of Economics, Higher Education Complex of Saravan, (Saravan, Sistan and Baluchestan province), IRAN
Emambakhsh Heidouzahi - Department of Economics, Higher Education Complex of Saravan, (Saravan, Sistan and Baluchestan province), IRAN
Mohsen Dahmarde Ghaleno - Department of Accounting, Higher Education Complex of Saravan, Saravan, (Saravan, Sistan and Baluchestan province), IRAN

The capital or stock market along with the money market is one of the most important parts of financial sector of the nation’s economy, providing long-term financing required for efficient production and service activities. The total stock price index as reflector of stock market fluctuation is important for finance practitioners and policy-makers. Therefore, in this research, a comparative investigation was presented on two superior deep-learning-based models, including long short-term memory (LSTM), and convolutional neural network long short-term memory (CNN)-LSTM, applied for analysing prediction of the total stock price index of Tehran stock exchange (TSE) market. The complete dataset utilized in the current analysis covered the period from September ۲۳, ۲۰۱۱ to June ۲۲, ۲۰۲۱ with a total of ۳,۷۳۹ trading days in the TSE market. Forecasting accuracy and performance of the two proposed models were appraised using root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) criteria. Based on the results, the CNN-LSTM showed the lowest values of the aforementioned metrics compared to the LSTM model, and it was found that the CNN-LSTM model could be effective in providing the best prediction performance of the total stock price index on the TSE market. Eventually, graphically and numerically, various prediction results obtained from the proposed models were analysed for more comprehensive analysis.

LSTM, CNN-LSTM, Stock Market, Prediction