[1] Bernstein, S., Lebow, R. N., Stein, J. G., & Weber, S. (2000). God gave physics the easy problems: Adapting social science to an unpredictable world. European Journal of International Relations, 6(1), 43-76.
[2] amiri, M., aghaei, M. (2021). Using Set Covering Approach for Decision-Making Criteria Classification while Correlation between Criteria Exist. Management Research in Iran, 21(3), 1-23.
[3] Malkiel, B. G. (2003). The efficient market hypothesis and its critics. Journal of economic perspectives, 17(1), 59-82.
[4] Lo, A. W., & MacKinlay, A. C. (2011). A non-random walk down Wall Street. Princeton University Press.
[5] Basu, S. (1983). The relationship between earnings' yield, market value and return for NYSE common stocks: Further evidence. Journal of financial economics, 12(1), 129-156.
[6] Fama, E. F., & French, K. R. (1988). Permanent and temporary components of stock prices. Journal of political Economy, 96(2), 246-273.
[7] Lakonishok, J., Shleifer, A., & Vishny, R. W. (1994). Contrarian investment, extrapolation, and risk. The journal of finance, 49(5), 1541-1578.
[8] Shah, D., Isah, H., & Zulkernine, F. (2019). Stock market analysis: A review and taxonomy of prediction techniques. International Journal of Financial Studies, 7(2), 26.
[9] saranj, A., karimi, T., Shahrami Babakan, M. (2017). The Application of Rough Set Theory in Stock Price Forecasting (Case Study: Iran Saderat Bank). Financial Management Strategy, 5(3), 119-144. doi: 10.22051/jfm.2017.12680.1189 (in Persian).
[10] Hiransha, M., Gopalakrishnan, E. A., Menon, V. K., & Soman, K. P. (2018). NSE stock market prediction using deep-learning models. Procedia computer science, 132, 1351-1362.
[11] Fama, E. F. (1995). Random walks in stock market prices. Financial analysts journal, 51(1), 75-80.
[12] Abu-Mostafa, Y. S., & Atiya, A. F. (1996). Introduction to financial forecasting. Applied intelligence, 6(3), 205-213.
[13] Zhong, X., & Enke, D. (2017). Forecasting daily stock market return using dimensionality reduction. Expert Systems with Applications, 67, 126-139.
[14] Park, C. H., & Irwin, S. H. (2007). What do we know about the profitability of technical analysis?. Journal of Economic surveys, 21(4), 786-826.
[15] Nguyen, T. H., Shirai, K., & Velcin, J. (2015). Sentiment analysis on social media for stock movement prediction. Expert Systems with Applications, 42(24), 9603-9611.
[16] Arévalo, R., García, J., Guijarro, F., & Peris, A. (2017). A dynamic trading rule based on filtered flag pattern recognition for stock market price forecasting. Expert Systems with Applications, 81, 177-192.
[17] Hu, Y., Liu, K., Zhang, X., Su, L., Ngai, E. W. T., & Liu, M. (2015). Application of evolutionary computation for rule discovery in stock algorithmic trading: A literature review. Applied Soft Computing, 36, 534-551.
[18] Gordon, M. J., & Shapiro, E. (1956). Capital equipment analysis: the required rate of profit. Management science, 3(1), 102-110.
[19] Gordon, M. J. (1959). Dividends, earnings, and stock prices. The review of economics and statistics, 99-105.
[20] Imam, S., Barker, R., & Clubb, C. (2008). The use of valuation models by UK investment analysts. European Accounting Review, 17(3), 503-535.
[21] Dutta, A., Bandopadhyay, G., & Sengupta, S. (2012). Prediction of stock performance in the Indian stock market using logistic regression. International Journal of Business and Information, 7(1), 105.
[22] Peymany Foroushany, Moslem; Erza, Amir Hossein; Salehi, Mahdi; Salehi, Ahmad (1399). Trades Return Based on Candlestick Charts in Tehran Stock Exchange. Financial Research, (22) 1, 69-89.
[23] Seif, Samira; Jamshidi Navid, Babak; Ghanbari, Mehrdad; Ismailpour, Mansour. (1400). Predicting the stock market trend in Iran using Elliott wave oscillation and relative strength index. Financial research. (1) 23. 134-157.
[24] Pourzamani, Zahra; Rezvani Aghdam, Mohsen (1396). Comparison of the effectiveness of combined strategies of technical analysis with the method of buying and holding for buying stocks in the uptrend and downtrends. Quarterly Journal of Financial Research in Securities Analysis, (10) 33, 17-31.
[25] Ghorbani, M., & Chong, E. K. (2020). Stock price prediction using principal components. PloS one, 15(3), e0230124.
[26] Azar, Adel; Khadivar, Amene (1398). Application of multivariate statistical analysis in management, Negah Danesh, Tehran, third edition.
[27] Beaumont, R. (2012). An introduction to principal component analysis & factor analysis using SPSS 19 and R (psych package). Factor Analysis and Principal Component Analysis (PCA), 24(8-9).
[28] Sadeghi Moghadam, M., Karimi, T., bandesi, S. (2021). Service Supply Chain Risk Assessment Applying Rough Set Theory Approach: Case of Payment Service Providers. Management Research in Iran, 22(1), 69-94.
[29] Pawlak, Z. (2012). Rough sets: Theoretical aspects of reasoning about data (Vol. 9). Springer Science & Business Media.
[30] Shahraki, A., Tahmasbi Abdar, Z. (2019). Failure Mode and Effects Analysis Using Rough Set Theory and Grey Relational Projection Method. Modern Research in Decision Making, 4(2), 1-35.
[31] Fazayeli, F., Wang, L., & Mandziuk, J. (2008, October). Feature selection based on the rough set theory and expectation-maximization clustering algorithm. In International Conference on Rough Sets and Current Trends in Computing (pp. 272-282). Springer, Berlin, Heidelberg.
[32] Chouchoulas, A., & Shen, Q. (2001). Rough set-aided keyword reduction for text categorization. Applied Artificial Intelligence, 15(9), 843-873.
[33] Rusu, V., & Rusu, C. (2003). Forecasting methods and stock market analysis. Creative Math, 12, 103-110.
[34] Al-Qaheri, H., Hassanien, A. E., & Abraham, A. (2008). Discovering stock price prediction rules using rough sets. Neural Network World, 18(3), 181.
[35] Cheng, C. H., Chen, T. L., & Wei, L. Y. (2010). A hybrid model based on rough sets theory and genetic algorithms for stock price forecasting. Information Sciences, 180(9), 1610-1629.
[36] Tsai, C. F., & Hsiao, Y. C. (2010). Combining multiple feature selection methods for stock prediction:union, intersection, and multi-intersection approaches. Decision Support Systems, 50(1), 258-269.
[37] Ma, X., & Spreij, P. J. C. (2011). PCA-Fuzzy-SVR Stock Price Prediction.
[38] Fallahpour, Saeed; Gol Arzi, Gholam Hossein; Fatoreh Chian, Nasser (1392). Predicting the moving trend of stock prices using a support vector machine based on genetic algorithm in Tehran Stock Exchange. Financial Research, 15 (2), 269 -288.
[39] Shakeri, Mehdi; Moradpour, Mona. (1394). Tehran Stock Exchange Stock Price Forecast Using Rough Set Theory, 3rd International Conference on Accounting and Management, Tehran, https://civilica.com/doc/441723.
[40] Wang, J., & Wang, J. (2015). Forecasting stock market indexes using principle component analysis and stochastic time effective neural networks. Neurocomputing, 156, 68-78.
[41] Ince, H., & Trafalis, T. B. (2007). Kernel principal component analysis and support vector machines for stock price prediction. IIE Transactions, 39(6), 629-637.
[42] Guo, Z., Wang, H., Yang, J., & Miller, D. J. (2015). A stock market forecasting model combining two-directional two-dimensional principal component analysis and radial basis function neural network. PloS one, 10(4), e0122385.
[43] Zahedi, J., & Rounaghi, M. M. (2015). Application of artificial neural network models and principal component analysis method in predicting stock prices on Tehran Stock Exchange. Physica A: Statistical Mechanics and its Applications, 438, 178-187.
[44] Yang, L. (2015). An application of principal component analysis to stock portfolio management.
[45] Afsar, A., Helyel, F. (2017). A Hybrid Approach to Portfolio Optimization Using Technical Analysis and Data Mining. Modern Research in Decision Making, 2(2), 1-22.
[46] Mohammadi, S., Mohammadi, E., Barzinpour, F. (2018). Portfolio Optimization in Tehran Stock Exchange by Using Data Envelopment Analysis and Symbiotic Organisms Search. Modern Research in Decision Making, 3(2), 223-248.
[47] Fakhari, Hussein; Valipour Khatir, Mohammad; Mousavi, Seyedeh Maedeh. (1396). Investigating Performance of Bayesian and Levenberg-Marquardt Neural Network in Comparison Classical Models in Stock Price Forecasting. Financial Research, (2) 19. 299-318.
[48] Afshari Rad, Elham; Alawi, Sayyid Enayatullah; Sinayi, Hassan Ali (1397). An intelligent model for predicting stock prices using technical analysis methods. Financial Research, (20) 2, 249-264.
[49] Golamian, Elham; Davoodi, Mohammad Reza (1397). Predicting the price trend in the stock market using a random forest algorithm. Journal of Financial Engineering and Securities Management, 9 (35), 301-322.