References
Afsharirad, E., Alavi, S. E. & Sinaei, H. (2018). Developing an Intelligent Model to Predict Stock Trend Using the Technical Analysis. Financial Research Journal, 20(2), 249–264. https://doi.org/10.22059/frj.2018.233763.1006452 (in Persian)
Ajiga, D. I., Adeleye, R. A., Tubokirifuruar, T. S., Bello, B. G., Ndubuisi, N. L., Asuzu, O. F. & Owolabi, O. R. (2024). Machine learning for stock market forecasting: a review of models and accuracy. Finance & Accounting Research Journal, 6(2), 112-124.
Awad, M. & Khanna, R. (2015). Efficient learning machines: theories, concepts, and applications for engineers and system designers (p. 268). Springer nature.
Ayala, J., García-Torres, M., Noguera, J. L. V., Gómez-Vela, F. & Divina, F. (2021). Technical analysis strategy optimization using a machine learning approach in stock market indices. Knowledge-Based Systems, 225, 107-119.
Chaajer, P., Shah, M. & Kshirsagar, A. (2021). The applications of artificial neural networks, support vector machines, and long-short term memory for stock market prediction. Decision Analytics Journal, 100015.
Fallahpour, S., Golarzi, G. & Fatourechian, N. (2013). Predicting Stock Price Movement Using Support Vector Machine Based on Genetic Algorithm in Tehran Stock Exchange Market. Financial Research Journal, 15(2), 269–288. https://doi.org/10.22059/jfr.2013.51081
(in Persian)
Farman Ara, V., Komijani, A., Farzinvash, A. & Ghaffari, F. (2019). The role of capital markets in financing economic growth A Case Study of Iran and some selected developing countries. Journal of Financial Economics (Financial Economics and Development), 13(47), 19-37. SID. https://sid.ir/paper/229015/en (in Persian)
Fischer, T. & Krauss, C. (2018). Deep learning with long short-term memory networks for financial market prediction. European Journal of Operational Research, 270(2), 654–669.
Gholizadeh Kapourchali, L., Fallah, M. F. & Minooei, M. (2024). Presenting an explanatory model of factors which explains the effect of the company's factors on the prediction of accepted profits in the Tehran Stock Exchange using machine learning, neural network and linear regression. Journal of Management Accounting and Auditing Knowledge, 13(50), 245-257. (in Persian)
Hoque, K. E. & Aljamaan, H. (2021). Impact of hyperparameter tuning on machine learning models in stock price forecasting. IEEE Access, 9, 163815-163830.
Hutter, F., Kotthoff, L. & Vanschoren, J. (2019). Automated machine learning: methods, systems, challenges (p. 219). Springer Nature.
Izadi, M., Ashtab, A. & Zavari Rezaei, A. (2025). Comparing the Estimation Power of Machine Learning Models and Statistical Models in Predicting Profit Component Changes and Selecting the Optimal Model. Financial Research Journal, 27(1), 31–57. https://doi.org/10.22059/frj.2024.373472.1007580 (in Persian)
Khan, W., Ghazanfar, M. A., Azam, M. A., Karami, A., Alyoubi, K. H. & Alfakeeh, A. S. (2022). Stock market prediction using machine learning classifiers and social media, news. Journal of Ambient Intelligence and Humanized Computing, 1-24.
Khanna, M., Kulshrestha, M., Singh, L. K., Thawkar, S. & Shrivastava, K. (2022). Performance evaluation of machine learning algorithms for stock price and stock index movement prediction using trend deterministic data prediction. International Journal of Applied Metaheuristic Computing (IJAMC), 13(1), 1-30.
Kubat, M. (2017). An introduction to machine learning. Springer, Cham.
Lee, M. C. (2009). Using support vector machine with a hybrid feature selection method to the stock trend prediction. Expert systems with applications, 36(8), 10896-10904.
Makian, S.N. & Mousavi, F.S. (2011). Stock Price Forecasting Through Using Ann and Arima Techniques: A Case Study of Pars Petroleum Company. Economic Modeling Scientific-Research Quarterly, 6 (18), 105-121. SID. https://sid.ir/paper/176324/en (in Persian)
Malkiel, B. G. & Fama, E. F. (1970). Efficient capital markets: A review of theory and empirical work. The Journal of Finance, 25(2), 383–417.
Moradi, B., Bahri Sales, J., Jabbarzadeh Kangarlooi, S. & Ashtab, A. (2022). Explaining and Proposing a Market Liquidity Prediction Model in Tehran Stock Exchange. Financial Research Journal, 24(1), 134–156. https://doi.org/10.22059/frj.2021.323611.1007184
(in Persian)
Moradzadehfard, M., Darabi, R. & Shahalizadeh, R. (2015). Combination of Artificial Intelligence Methods for Presenting Stock Price Forecasting Models. The Financial Accounting and Auditing Research, 6(24), 89-101. SID. https://sid.ir/paper/198056/en
(in Persian)
Patel, J., Shah, S., Thakkar, P. & Kotecha, K. (2015). Predicting stock market index using fusion of machine learning techniques. Expert systems with applications, 42(4), 2162-2172.
Saadat, M. & Zamani Boroujeni, F. (2016). A review of methods for improving the efficiency of the random forest technique. Proceedings of the First National Conference on Information Technology, Communications, and Soft Computing, Isfahan, Iran: Islamic Azad University, Isfahan (Khorasgan) Branch. (in Persian).
Sangeetha, J. M. & Alfia, K. J. (2024). Financial stock market forecast using evaluated linear regression based machine learning technique. Measurement: Sensors, 31, 100-950.
Schratz, P., Muenchow, J., Iturritxa, E., Richter, J. & Brenning, A. (2019). Hyperparameter tuning and performance assessment of statistical and machine-learning algorithms using spatial data. Ecological Modelling, 406, 109-120.
Smola, A. J. & Schölkopf, B. (2004). A tutorial on support vector regression. Statistics and computing, 14, 199-222.
Sohrabi, M., Mirbargkar, S.M., Chirani, E. & Kheradiar, S. (2023). Comparison of different machine learning models in stock market index prediction. Financial engineering and securities management. 56 (14), 19-36. (in Persian)
Tsinaslanidis, P. E. & Kugiumtzis, D. (2014). A prediction scheme using perceptually important points and dynamic time warping. Expert Systems with Applications, 41, 6848–6860.
Vapnik, V. N. (1999). An overview of statistical learning theory. IEEE transactions on neural networks, 10(5), 988-999.