[1] Sharma, A., D. Bhuriya, and U. Singh. Survey of stock market prediction using machine learning approach. in 2017 International conference of electronics, communication and aerospace technology (ICECA). 2017. IEEE.
[2] Sutton, R.S. and A.G. Barto, Reinforcement learning: An introduction. Robotica, 1999. 17(2): p. 229-235.
[3] Barroso, B., R. Cardoso, and M. Melo, Performance analysis of the integration between Portfolio Optimization and Technical Analysis strategies in the Brazilian stock market. Expert Systems with Applications, 2021. 186: p. 115687.
[4] DEHGHAN, A. and M. Kamyabi, On Tehran stock market in cyclical condition. 2019.
[5] Hejazi, R., et al., The Relationship between Stock Prices Crash and Dedicated Institutional Investors and Transient Institutional Investors. 2020.
[6] Ali Asghar, A.R. and B. Lari Semnani, Assessing the Relationships of Bank Deposits and Governmental Industrial Development Bonds Investments with the Attracitveness of Investing (Lequidity and Capitalization) in Tehran Stock Exchange (TSE). Management Research in Iran, 2007. 11(20): p. 1-29.
[7] Fairchild, R.J. and J. Kinsella, An Emotional Finance Framework for Examining Bubbles and Crashes. Available at SSRN 3999323, 2022.
[8] Hatefi Madjumerd, M., G. Zamanian, and M.N. Shahiki Tash, Evaluation of Multiple Bubbles in the Stock Market of Tehran. Quarterly Journal of Quantitative Economics, 2017. 14(2): p. 85-110.
[9] Bagherzadeh, S., The Initial Public Offerings Underpricing and Its Determinants in Tehran Stock Exchange. Human Sciences Modares, 2011. 15(1): p. 77-107.
[10] Afsar, A. and F. Helyel, A Hybrid Approach to Portfolio Optimization Using Technical Analysis and Data Mining. Modern Research in Decision Making, 2017. 2(2): p. 1-22.
[11] Katani, S., F. Samadi, and Z. Hajiha, Optimization of multi-objective portfolio using imperialist competitive algorithm in Tehran Stock Exchange. 2021.
[12] BELAGHİ, R.A., M. AMİNNEJAD, and Ö.G. ALMA, Stock Market Prediction Using Nonparametric Fuzzy and Parametric GARCH Methods. Turkish Journal of Forecasting, 2018. 2(1): p. 1-8.
[13] Nabipour, M., et al., Deep learning for stock market prediction. Entropy, 2020. 22(8): p. 840.
[14] Nabipour, M., et al., Predicting Stock Market Trends Using Machine Learning and Deep Learning Algorithms Via Continuous and Binary Data; a Comparative Analysis. IEEE Access, 2020. 8: p. 150199-150212.
[15] Deng, Y., et al., Deep direct reinforcement learning for financial signal representation and trading. IEEE transactions on neural networks and learning systems, 2016. 28(3): p. 653-664.
[16] Xiong, Z., et al., Practical deep reinforcement learning approach for stock trading. arXiv preprint arXiv:1811.07522, 2018.
[17] Meng, T.L. and M. Khushi, Reinforcement learning in financial markets. Data, 2019. 4(3): p. 110.
[18] Li, Y., P. Ni, and V. Chang, Application of deep reinforcement learning in stock trading strategies and stock forecasting. Computing, 2020. 102(6): p. 1305-1322.
[19] Carta, S., et al., Multi-DQN: An ensemble of Deep Q-learning agents for stock market forecasting. Expert systems with applications, 2021. 164: p. 113820.
[20] Shi, Y., et al., Stock trading rule discovery with double deep Q-network. Applied Soft Computing, 2021. 107: p. 107320.
[21] Théate, T. and D. Ernst, An application of deep reinforcement learning to algorithmic trading. Expert Systems with Applications, 2021. 173: p. 114632.
[22] Sun, T., D. Huang, and J. Yu, Market Making Strategy Optimization via Deep Reinforcement Learning. IEEE Access, 2022.
[23] Jiang, W., Applications of deep learning in stock market prediction: recent progress. Expert Systems with Applications, 2021. 184: p. 115537.
[24] Amihud, Y., Illiquidity and stock returns: cross-section and time-series effects. Journal of financial markets, 2002. 5(1): p. 31-56.
[25] Jansen, S., Machine Learning for Algorithmic Trading: Predictive Models to Extract Signals from Market and Alternative Data for Systematic Trading Strategies with Python. 2020: Packt Publishing Limited.
[26] Mehrabanpour, M., A. Azar, and M. Shahrami Babkan, Stock price forecasting by presenting a hybrid model using principal component analysis and rough set theory. Modern Research in Decision Making, 2022. 7(2): p. 137-167.
[27] Minh, D.L., et al., Deep learning approach for short-term stock trends prediction based on two-stream gated recurrent unit network. Ieee Access, 2018. 6: p. 55392-55404.
[28] Hessel, M., et al. Rainbow: Combining improvements in deep reinforcement learning. in Thirty-second AAAI conference on artificial intelligence. 2018.
[29] Index. 1400; Available from: https://rahavard365.com/.