How to cite this paper
Hadizadeh, A., Tarokh, M & Ghazani, M. (2025). A convolutional deep reinforcement learning architecture for an emerging stock market analysis.Decision Science Letters , 14(2), 313-326.
Refrences
Abbasimehr, H., & Paki, R. (2022). Improving time series forecasting using LSTM and attention models. Journal of Ambient Intel-ligence and Humanized Computing, 13(1), 673-691.
Abounoori, E., & Tour, M. (2019). Stock market interactions among Iran, USA, Turkey, and UAE. Physica A: Statistical mechan-ics and its applications, 524, 297-305.
Agarap, A. F. (2018). Deep learning using rectified linear units (ReLU). arXiv preprint arXiv:1803.08375.
Amihud, Y. (2002). Illiquidity and stock returns: cross-section and time-series effects. Journal of financial markets, 5(1), 31-56.
Belaghi, R. A., Aminnejad, M., & Alma, Ö. G. (2018). Stock market prediction using nonparametric fuzzy and parametric garch methods. Turkish Journal of Forecasting, 2(1), 1-8.
Bourseview. (2023). Iran Stock market comparisons. https://www.bourseview.com/
Carta, S., Corriga, A., Ferreira, A., Podda, A. S., & Recupero, D. R. (2021). A multi-layer and multi-ensemble stock trader using deep learning and deep reinforcement learning. Applied Intelligence, 51, 889-905.
Chakole, J. B., Kolhe, M. S., Mahapurush, G. D., Yadav, A., & Kurhekar, M. P. (2021). A Q-learning agent for automated trading in equity stock markets. Expert Systems with Applications, 163, 113761.
Chen, X., Wang, Q., Hu, C., & Wang, C. (2024). A stock market decision-making framework based on CMR-DQN. Applied Sci-ences, 14(16), 6881.
Chen, Y., Li, L., Li, W., Guo, Q., Du, Z., & Xu, Z. (2022). AI Computing Systems: An Application Driven Perspective. Elsevier.
Cui, K., Hao, R., Huang, Y., Li, J., & Song, Y. (2023). A novel convolutional neural networks for stock trading based on DDQN algorithm. IEEE Access, 11, 32308-32318.
Deng, Y., Bao, F., Kong, Y., Ren, Z., & Dai, Q. (2016). Deep direct reinforcement learning for financial signal representation and trading. IEEE transactions on neural networks and learning systems, 28(3), 653-664.
Dong, H., Dong, H., Ding, Z., Zhang, S., & Chang, T. (2020). Deep Reinforcement Learning. Singapore: Springer Singapore.
Du, S., & Shen, H. (2024). A Stock Prediction Method Based on Deep Reinforcement Learning and Sentiment Analysis. Applied Sciences, 14(19), 8747.
Goodfellow, I. (2016). Deep learning (Adaptive Computation and Machine Learning series). The MIT Press.
Hao, Y., & Gao, Q. (2020). Predicting the trend of stock market index using the hybrid neural network based on multiple time scale feature learning. Applied Sciences, 10(11), 3961.
Hu, Z., Zhao, Y., & Khushi, M. (2021). A survey of forex and stock price prediction using deep learning. Applied System Innova-tion, 4(1), 9.
Huang, Y., Zhou, C., Cui, K., & Lu, X. (2024). A multi-agent reinforcement learning framework for optimizing financial trading strategies based on timesnet. Expert Systems with Applications, 237, 121502.
Investopedia. (2021). www.investopedia.com
Jansen, S. (2020). Machine Learning for Algorithmic Trading: Predictive models to extract signals from market and alternative da-ta for systematic trading strategies with Python. Packt Publishing Ltd.
Jiang, W. (2021). Applications of deep learning in stock market prediction: recent progress. Expert Systems with Applications, 184, 115537.
Katani, S., Samadi, F., & Hajiha, Z. (2017). Optimization of multi-objective portfolio using imperialist competitive algorithm in Tehran Stock Exchange. International Journal of Business Management, 2(4-2017), 85-97.
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444.
Lee, S. W., & Kim, H. Y. (2020). Stock market forecasting with super-high dimensional time-series data using ConvLSTM, trend sampling, and specialized data augmentation. Expert systems with applications, 161, 113704.
Li, Y., Ni, P., & Chang, V. (2020). Application of deep reinforcement learning in stock trading strategies and stock forecasting. Computing, 102(6), 1305-1322.
Li, Z., Zhang, X., & Dong, Z. (2023). TSF-transformer: a time series forecasting model for exhaust gas emission using transformer. Applied Intelligence, 53(13), 17211-17225.
Lin, L. J. (1992). Reinforcement learning for robots using neural networks. Carnegie Mellon University.
Liu, X. Y., Xiong, Z., Zhong, S., Yang, H., & Walid, A. (2018). Practical deep reinforcement learning approach for stock trading. arXiv preprint arXiv:1811.07522.
Livieris, I. E., Pintelas, E., & Pintelas, P. (2020). A CNN–LSTM model for gold price time-series forecasting. Neural computing and applications, 32, 17351-17360.
Lu, J. Y., Lai, H. C., Shih, W. Y., Chen, Y. F., Huang, S. H., Chang, H. H., ... & Dai, T. S. (2022). Structural break-aware pairs trading strategy using deep reinforcement learning. The Journal of Supercomputing, 78(3), 3843-3882.
Lu, W., Li, J., Wang, J., & Qin, L. (2021). A CNN-BiLSTM-AM method for stock price prediction. Neural Computing and Appli-cations, 33(10), 4741-4753.
Ma, C., & Yan, S. (2022). Deep learning in the Chinese stock market: the role of technical indicators. Finance Research Letters, 49, 103025.
Meng, T. L., & Khushi, M. (2019). Reinforcement learning in financial markets. Data, 4(3), 110.
Mnih, V. (2016). Asynchronous Methods for Deep Reinforcement Learning. arXiv preprint arXiv:1602.01783.
Moghadam, H. E., Mohammadi, T., Kashani, M. F., & Shakeri, A. (2019). Complex networks analysis in Iran stock market: The application of centrality. Physica A: Statistical Mechanics and its Applications, 531, 121800.
Montgomery, D. C., Peck, E. A., & Vining, G. G. (2021). Introduction to linear regression analysis. John Wiley & Sons.
Muntean, M., & Militaru, F. D. (2023, January). Metrics for evaluating classification algorithms. In Education, Research and Busi-ness Technologies: Proceedings of 21st International Conference on Informatics in Economy (IE 2022) (pp. 307-317). Singapore: Springer Nature Singapore.
Nabipour, M., Nayyeri, P., Jabani, H., Mosavi, A., & Salwana, E. (2020). Deep learning for stock market prediction. Entropy, 22(8), 840.
Nabipour, M., Nayyeri, P., Jabani, H., Shahab, S., & Mosavi, A. (2020). Predicting stock market trends using machine learning and deep learning algorithms via continuous and binary data; a comparative analysis. IEEE Access, 8, 150199-150212.
Papageorgiou, G., Gkaimanis, D., & Tjortjis, C. (2024). Enhancing Stock Market Forecasts with Double Deep Q-Network in Vola-tile Stock Market Environments. Electronics, 13(9), 1629.
Polamuri, S. R., Srinivas, K., & Mohan, A. K. (2020). Multi model-based hybrid prediction algorithm (MM-HPA) for stock market prices prediction framework (SMPPF). Arabian Journal for Science and Engineering, 45(12), 10493-10509.
Qin, L., Yu, N., & Zhao, D. (2018). Applying the convolutional neural network deep learning technology to behavioural recogni-tion in intelligent video. Tehnički vjesnik, 25(2), 528-535.
Qiu, Y., Liu, R., & Lee, R. S. (2024). The design and implementation of a deep reinforcement learning and quantum finance theory-inspired portfolio investment management system. Expert Systems with Applications, 238, 122243.
rahavard. (2021). https://rahavard365.com/
Semenoglou, A. A., Spiliotis, E., & Assimakopoulos, V. (2023). Image-based time series forecasting: A deep convolutional neural network approach. Neural Networks, 157, 39-53.
Sharma, A., Bhuriya, D., & Singh, U. (2017, April). Survey of stock market prediction using machine learning approach. In 2017 International Conference of Electronics, communication and Aerospace Technology (ICECA) (Vol. 2, pp. 506-509). IEEE.
Shetty, M., & Tamane, S. (2025). Unveiling bitcoin network attack using deep reinforcement learning with Boltzmann exploration. Peer-to-Peer Networking and Applications, 18(1), 1-19.
Shi, Y., Li, W., Zhu, L., Guo, K., & Cambria, E. (2021). Stock trading rule discovery with double deep Q-network. Applied Soft Computing, 107, 107320.
Song, G., Zhao, T., Ma, X., Lin, P., & Cui, C. (2025). Reinforcement learning-based portfolio optimization with deterministic state transition. Information Sciences, 690, 121538.
Sun, T., Huang, D., & Yu, J. (2022). Market making strategy optimization via deep reinforcement learning. IEEE Access, 10, 9085-9093.
Thakkar, A., & Chaudhari, K. (2021). A comprehensive survey on deep neural networks for stock market: The need, challenges, and future directions. Expert Systems with Applications, 177, 114800.
Thakkar, A., & Chaudhari, K. (2021). Fusion in stock market prediction: a decade survey on the necessity, recent developments, and potential future directions. Information Fusion, 65, 95-107.
Théate, T., & Ernst, D. (2021). An application of deep reinforcement learning to algorithmic trading. Expert Systems with Applica-tions, 173, 114632.
Van Hasselt, H., Guez, A., & Silver, D. (2016, March). Deep reinforcement learning with double q-learning. In Proceedings of the AAAI conference on artificial intelligence (Vol. 30, No. 1).
Vergara, G., & Kristjanpoller, W. (2024). Deep reinforcement learning applied to statistical arbitrage investment strategy on cryp-tomarket. Applied Soft Computing, 153, 111255.
Vetrina, R. L., & Kobergb, K. (2024). Reinforcement learning in optimisation of financial market trading strategy parameters. COMPUTER, 16(7), 1793-1812.
Wang, L. (2024). Reinforcement Learning for Solving Financial Problems.
Wu, M. E., Syu, J. H., Lin, J. C. W., & Ho, J. M. (2021). Portfolio management system in equity market neutral using reinforce-ment learning. Applied Intelligence, 51(11), 8119-8131.
Xu, C., Li, J., Feng, B., & Lu, B. (2023). A financial time-series prediction model based on multiplex attention and linear trans-former structure. Applied Sciences, 13(8), 5175.
Yahoofinance. (2023). NYSE COMPOSITE. https://finance.yahoo.com/quote/%5ENYA/chart
Yang, H., Liu, X. Y., Zhong, S., & Walid, A. (2020, October). Deep reinforcement learning for automated stock trading: An en-semble strategy. In Proceedings of the first ACM international conference on AI in finance (pp. 1-8).
Yue, M., & Ma, S. (2023). LSTM-based transformer for transfer passenger flow forecasting between transportation integrated hubs in urban agglomeration. Applied Sciences, 13(1), 637.
Zhang, Y., Zhao, P., Wu, Q., Li, B., Huang, J., & Tan, M. (2020). Cost-sensitive portfolio selection via deep reinforcement learn-ing. IEEE Transactions on Knowledge and Data Engineering, 34(1), 236-248.
Zhong, X., Wei, J., Li, S., & Xu, Q. (2025). Deep reinforcement learning for dynamic strategy interchange in financial markets. Ap-plied Intelligence, 55(1), 1-19.
Zhou, C., Huang, Y., Cui, K., & Lu, X. (2024). R-DDQN: Optimizing Algorithmic Trading Strategies Using a Reward Network in a Double DQN. Mathematics, 12(11), 1621.
Abounoori, E., & Tour, M. (2019). Stock market interactions among Iran, USA, Turkey, and UAE. Physica A: Statistical mechan-ics and its applications, 524, 297-305.
Agarap, A. F. (2018). Deep learning using rectified linear units (ReLU). arXiv preprint arXiv:1803.08375.
Amihud, Y. (2002). Illiquidity and stock returns: cross-section and time-series effects. Journal of financial markets, 5(1), 31-56.
Belaghi, R. A., Aminnejad, M., & Alma, Ö. G. (2018). Stock market prediction using nonparametric fuzzy and parametric garch methods. Turkish Journal of Forecasting, 2(1), 1-8.
Bourseview. (2023). Iran Stock market comparisons. https://www.bourseview.com/
Carta, S., Corriga, A., Ferreira, A., Podda, A. S., & Recupero, D. R. (2021). A multi-layer and multi-ensemble stock trader using deep learning and deep reinforcement learning. Applied Intelligence, 51, 889-905.
Chakole, J. B., Kolhe, M. S., Mahapurush, G. D., Yadav, A., & Kurhekar, M. P. (2021). A Q-learning agent for automated trading in equity stock markets. Expert Systems with Applications, 163, 113761.
Chen, X., Wang, Q., Hu, C., & Wang, C. (2024). A stock market decision-making framework based on CMR-DQN. Applied Sci-ences, 14(16), 6881.
Chen, Y., Li, L., Li, W., Guo, Q., Du, Z., & Xu, Z. (2022). AI Computing Systems: An Application Driven Perspective. Elsevier.
Cui, K., Hao, R., Huang, Y., Li, J., & Song, Y. (2023). A novel convolutional neural networks for stock trading based on DDQN algorithm. IEEE Access, 11, 32308-32318.
Deng, Y., Bao, F., Kong, Y., Ren, Z., & Dai, Q. (2016). Deep direct reinforcement learning for financial signal representation and trading. IEEE transactions on neural networks and learning systems, 28(3), 653-664.
Dong, H., Dong, H., Ding, Z., Zhang, S., & Chang, T. (2020). Deep Reinforcement Learning. Singapore: Springer Singapore.
Du, S., & Shen, H. (2024). A Stock Prediction Method Based on Deep Reinforcement Learning and Sentiment Analysis. Applied Sciences, 14(19), 8747.
Goodfellow, I. (2016). Deep learning (Adaptive Computation and Machine Learning series). The MIT Press.
Hao, Y., & Gao, Q. (2020). Predicting the trend of stock market index using the hybrid neural network based on multiple time scale feature learning. Applied Sciences, 10(11), 3961.
Hu, Z., Zhao, Y., & Khushi, M. (2021). A survey of forex and stock price prediction using deep learning. Applied System Innova-tion, 4(1), 9.
Huang, Y., Zhou, C., Cui, K., & Lu, X. (2024). A multi-agent reinforcement learning framework for optimizing financial trading strategies based on timesnet. Expert Systems with Applications, 237, 121502.
Investopedia. (2021). www.investopedia.com
Jansen, S. (2020). Machine Learning for Algorithmic Trading: Predictive models to extract signals from market and alternative da-ta for systematic trading strategies with Python. Packt Publishing Ltd.
Jiang, W. (2021). Applications of deep learning in stock market prediction: recent progress. Expert Systems with Applications, 184, 115537.
Katani, S., Samadi, F., & Hajiha, Z. (2017). Optimization of multi-objective portfolio using imperialist competitive algorithm in Tehran Stock Exchange. International Journal of Business Management, 2(4-2017), 85-97.
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444.
Lee, S. W., & Kim, H. Y. (2020). Stock market forecasting with super-high dimensional time-series data using ConvLSTM, trend sampling, and specialized data augmentation. Expert systems with applications, 161, 113704.
Li, Y., Ni, P., & Chang, V. (2020). Application of deep reinforcement learning in stock trading strategies and stock forecasting. Computing, 102(6), 1305-1322.
Li, Z., Zhang, X., & Dong, Z. (2023). TSF-transformer: a time series forecasting model for exhaust gas emission using transformer. Applied Intelligence, 53(13), 17211-17225.
Lin, L. J. (1992). Reinforcement learning for robots using neural networks. Carnegie Mellon University.
Liu, X. Y., Xiong, Z., Zhong, S., Yang, H., & Walid, A. (2018). Practical deep reinforcement learning approach for stock trading. arXiv preprint arXiv:1811.07522.
Livieris, I. E., Pintelas, E., & Pintelas, P. (2020). A CNN–LSTM model for gold price time-series forecasting. Neural computing and applications, 32, 17351-17360.
Lu, J. Y., Lai, H. C., Shih, W. Y., Chen, Y. F., Huang, S. H., Chang, H. H., ... & Dai, T. S. (2022). Structural break-aware pairs trading strategy using deep reinforcement learning. The Journal of Supercomputing, 78(3), 3843-3882.
Lu, W., Li, J., Wang, J., & Qin, L. (2021). A CNN-BiLSTM-AM method for stock price prediction. Neural Computing and Appli-cations, 33(10), 4741-4753.
Ma, C., & Yan, S. (2022). Deep learning in the Chinese stock market: the role of technical indicators. Finance Research Letters, 49, 103025.
Meng, T. L., & Khushi, M. (2019). Reinforcement learning in financial markets. Data, 4(3), 110.
Mnih, V. (2016). Asynchronous Methods for Deep Reinforcement Learning. arXiv preprint arXiv:1602.01783.
Moghadam, H. E., Mohammadi, T., Kashani, M. F., & Shakeri, A. (2019). Complex networks analysis in Iran stock market: The application of centrality. Physica A: Statistical Mechanics and its Applications, 531, 121800.
Montgomery, D. C., Peck, E. A., & Vining, G. G. (2021). Introduction to linear regression analysis. John Wiley & Sons.
Muntean, M., & Militaru, F. D. (2023, January). Metrics for evaluating classification algorithms. In Education, Research and Busi-ness Technologies: Proceedings of 21st International Conference on Informatics in Economy (IE 2022) (pp. 307-317). Singapore: Springer Nature Singapore.
Nabipour, M., Nayyeri, P., Jabani, H., Mosavi, A., & Salwana, E. (2020). Deep learning for stock market prediction. Entropy, 22(8), 840.
Nabipour, M., Nayyeri, P., Jabani, H., Shahab, S., & Mosavi, A. (2020). Predicting stock market trends using machine learning and deep learning algorithms via continuous and binary data; a comparative analysis. IEEE Access, 8, 150199-150212.
Papageorgiou, G., Gkaimanis, D., & Tjortjis, C. (2024). Enhancing Stock Market Forecasts with Double Deep Q-Network in Vola-tile Stock Market Environments. Electronics, 13(9), 1629.
Polamuri, S. R., Srinivas, K., & Mohan, A. K. (2020). Multi model-based hybrid prediction algorithm (MM-HPA) for stock market prices prediction framework (SMPPF). Arabian Journal for Science and Engineering, 45(12), 10493-10509.
Qin, L., Yu, N., & Zhao, D. (2018). Applying the convolutional neural network deep learning technology to behavioural recogni-tion in intelligent video. Tehnički vjesnik, 25(2), 528-535.
Qiu, Y., Liu, R., & Lee, R. S. (2024). The design and implementation of a deep reinforcement learning and quantum finance theory-inspired portfolio investment management system. Expert Systems with Applications, 238, 122243.
rahavard. (2021). https://rahavard365.com/
Semenoglou, A. A., Spiliotis, E., & Assimakopoulos, V. (2023). Image-based time series forecasting: A deep convolutional neural network approach. Neural Networks, 157, 39-53.
Sharma, A., Bhuriya, D., & Singh, U. (2017, April). Survey of stock market prediction using machine learning approach. In 2017 International Conference of Electronics, communication and Aerospace Technology (ICECA) (Vol. 2, pp. 506-509). IEEE.
Shetty, M., & Tamane, S. (2025). Unveiling bitcoin network attack using deep reinforcement learning with Boltzmann exploration. Peer-to-Peer Networking and Applications, 18(1), 1-19.
Shi, Y., Li, W., Zhu, L., Guo, K., & Cambria, E. (2021). Stock trading rule discovery with double deep Q-network. Applied Soft Computing, 107, 107320.
Song, G., Zhao, T., Ma, X., Lin, P., & Cui, C. (2025). Reinforcement learning-based portfolio optimization with deterministic state transition. Information Sciences, 690, 121538.
Sun, T., Huang, D., & Yu, J. (2022). Market making strategy optimization via deep reinforcement learning. IEEE Access, 10, 9085-9093.
Thakkar, A., & Chaudhari, K. (2021). A comprehensive survey on deep neural networks for stock market: The need, challenges, and future directions. Expert Systems with Applications, 177, 114800.
Thakkar, A., & Chaudhari, K. (2021). Fusion in stock market prediction: a decade survey on the necessity, recent developments, and potential future directions. Information Fusion, 65, 95-107.
Théate, T., & Ernst, D. (2021). An application of deep reinforcement learning to algorithmic trading. Expert Systems with Applica-tions, 173, 114632.
Van Hasselt, H., Guez, A., & Silver, D. (2016, March). Deep reinforcement learning with double q-learning. In Proceedings of the AAAI conference on artificial intelligence (Vol. 30, No. 1).
Vergara, G., & Kristjanpoller, W. (2024). Deep reinforcement learning applied to statistical arbitrage investment strategy on cryp-tomarket. Applied Soft Computing, 153, 111255.
Vetrina, R. L., & Kobergb, K. (2024). Reinforcement learning in optimisation of financial market trading strategy parameters. COMPUTER, 16(7), 1793-1812.
Wang, L. (2024). Reinforcement Learning for Solving Financial Problems.
Wu, M. E., Syu, J. H., Lin, J. C. W., & Ho, J. M. (2021). Portfolio management system in equity market neutral using reinforce-ment learning. Applied Intelligence, 51(11), 8119-8131.
Xu, C., Li, J., Feng, B., & Lu, B. (2023). A financial time-series prediction model based on multiplex attention and linear trans-former structure. Applied Sciences, 13(8), 5175.
Yahoofinance. (2023). NYSE COMPOSITE. https://finance.yahoo.com/quote/%5ENYA/chart
Yang, H., Liu, X. Y., Zhong, S., & Walid, A. (2020, October). Deep reinforcement learning for automated stock trading: An en-semble strategy. In Proceedings of the first ACM international conference on AI in finance (pp. 1-8).
Yue, M., & Ma, S. (2023). LSTM-based transformer for transfer passenger flow forecasting between transportation integrated hubs in urban agglomeration. Applied Sciences, 13(1), 637.
Zhang, Y., Zhao, P., Wu, Q., Li, B., Huang, J., & Tan, M. (2020). Cost-sensitive portfolio selection via deep reinforcement learn-ing. IEEE Transactions on Knowledge and Data Engineering, 34(1), 236-248.
Zhong, X., Wei, J., Li, S., & Xu, Q. (2025). Deep reinforcement learning for dynamic strategy interchange in financial markets. Ap-plied Intelligence, 55(1), 1-19.
Zhou, C., Huang, Y., Cui, K., & Lu, X. (2024). R-DDQN: Optimizing Algorithmic Trading Strategies Using a Reward Network in a Double DQN. Mathematics, 12(11), 1621.