[1] Selvamuthu, D., Kumar, V., & Mishra, A. (2019). Indian stock market prediction using artificial neural networks on tick data. Financial Innovation, 5(1), 16.
https://doi.org/10.1186/s40854-019-0131-7
[2] Chopra, S., Yadav, D., & Chopra, A. N. (2019). Artificial neural networks based Indian stock market price prediction: Before and after demonetization. Journal of Swarm Intelligence and Evolutionary Computation, 8(174), 2.
[3] Heidari, A. A., Faris, H., Mirjalili, S., Aljarah, I., & Mafarja, M. (2020). Ant lion optimizer: Theory, literature review, and application in multi-layer perceptron neural networks. In Nature-Inspired Optimizers (pp. 23–46). Springer, Cham.
https://doi.org/10.1007/978-3-030-12127-3_3
[4] Ahmadkhan Beigi, S., & Abdolvand, N. (2017). Stock price prediction using a hybrid approach of artificial neural networks and imperialist competitive algorithm based on chaos theory. Financial Management Strategy, 5(3).
[5] Mirghafari, S., & Rostgar, M. A. (2017). A fuzzy hybrid model for stock return prediction using fuzzy neural networks and the ant colony algorithm. In Proceedings of the 2nd National Conference on Soft Computing, Gilan-Roudsar, University of Gilan.
[6] Rahimi Garkani, A. (2017). Identifying the most effective model for predicting stock prices of companies listed on the structured securities market using artificial neural networks. In Proceedings of the 2nd International Conference on Knowledge-Based Research in Computer Engineering and Information Technology, Tehran, Majlesi University.
[7] Ahmadian, D., & Farkhandeh Rooz, O. (2017). Neural network methods for predicting the overall stock market index during 2016–2017. In Proceedings of the 1st International Conference on Management Patterns in the Age of Progress, Tehran, Islamic Government Research Institute – NAJA Social Studies, University of Tehran.
[8] Hasani Bagherani, A., Arab Bagherani, M., & Esmaeilian, G. (2018). Examining the application of neuro-fuzzy models in predicting stock prices of companies listed on the Tehran Stock Exchange. In Proceedings of the 1st National Conference on Accounting and Management, Natanz, Islamic Azad University, Natanz Branch.
[9] Babajani, J., Taghva, M. R., Bolou, G., & Abdollahi, M. (2019). Stock price prediction on the Tehran Stock Exchange using recurrent neural networks optimized with the artificial bee colony algorithm. Financial Management Strategy, 7(2).
[10] Guresen, E., Kayakutlu, G., & Daim, T. U. (2011). Using artificial neural network models in stock market index prediction. Expert Systems with Applications, 38(8), 10389–10397.
https://doi.org/10.1016/j.eswa.2011.02.068
[11] Moghaddam, A. H., Moghaddam, M. H., & Esfandyari, M. (2016). Stock market index prediction using artificial neural network. Journal of Economics, Finance and Administrative Science, 21(41), 89–93.
https://doi.org/10.1016/j.jefas.2016.07.002
[12] Ghasemiyeh, R., Moghdani, R., & Sana, S. S. (2017). A hybrid artificial neural network with metaheuristic algorithms for predicting stock price. Cybernetics and Systems, 48(4), 365–392.
https://doi.org/10.1080/01969722.2017.1285162
[13] Nelson, D. M., Pereira, A. C., & de Oliveira, R. A. (2017, May). Stock market's price movement prediction with LSTM neural networks. In 2017 International Joint Conference on Neural Networks (IJCNN) (pp. 1419–1426). IEEE.
https://doi.org/10.1109/IJCNN.2017.7966019
[14] Yong, B. X., Rahim, M. R. A., & Abdullah, A. S. (2017, August). A stock market trading system using deep neural network. In Asian Simulation Conference (pp. 356–364). Springer, Singapore.
https://doi.org/10.1007/978-981-10-6463-0_31
[15] Mirjalili, S. (2015). The ant lion optimizer. Advances in Engineering Software, 83, 80–98.
https://doi.org/10.1016/j.advengsoft.2015.01.010
[16] Heidari, A. A., Faris, H., Aljarah, I., & Mirjalili, S. (2019). An efficient hybrid multilayer perceptron neural network with grasshopper optimization. Soft Computing, 23(17), 7941–7958.
https://doi.org/10.1007/s00500-018-3424-2