{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,17]],"date-time":"2026-01-17T06:42:57Z","timestamp":1768632177099,"version":"3.49.0"},"reference-count":74,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2022,9,8]],"date-time":"2022-09-08T00:00:00Z","timestamp":1662595200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100010240","name":"National Social Science Foundation of China","doi-asserted-by":"publisher","award":["18BGL224"],"award-info":[{"award-number":["18BGL224"]}],"id":[{"id":"10.13039\/501100010240","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Systems"],"abstract":"<jats:p>Whether for institutional investors or individual investors, there is an urgent need to explore autonomous models that can adapt to the non-stationary, low-signal-to-noise markets. This research aims to explore the two unique challenges in quantitative portfolio management: (1) the difficulty of representation and (2) the complexity of environments. In this research, we suggest a Markov decision process model-based deep reinforcement learning model including deep learning methods to perform strategy optimization, called SwanTrader. To achieve better decisions of the portfolio-management process from two different perspectives, i.e., the temporal patterns analysis and robustness information capture based on market observations, we suggest an optimal deep learning network in our model that incorporates a stacked sparse denoising autoencoder (SSDAE) and a long\u2013short-term-memory-based autoencoder (LSTM-AE). The findings in times of COVID-19 show that the suggested model using two deep learning models gives better results with an alluring performance profile in comparison with four standard machine learning models and two state-of-the-art reinforcement learning models in terms of Sharpe ratio, Calmar ratio, and beta and alpha values. Furthermore, we analyzed which deep learning models and reward functions were most effective in optimizing the agent\u2019s management decisions. The results of our suggested model for investors can assist in reducing the risk of investment loss as well as help them to make sound decisions.<\/jats:p>","DOI":"10.3390\/systems10050146","type":"journal-article","created":{"date-parts":[[2022,9,8]],"date-time":"2022-09-08T09:51:09Z","timestamp":1662630669000},"page":"146","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Applications of Markov Decision Process Model and Deep Learning in Quantitative Portfolio Management during the COVID-19 Pandemic"],"prefix":"10.3390","volume":"10","author":[{"given":"Han","family":"Yue","sequence":"first","affiliation":[{"name":"College of Economics and Management, China Jiliang University, Hangzhou 310018, China"}]},{"given":"Jiapeng","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Economics and Management, China Jiliang University, Hangzhou 310018, China"}]},{"given":"Qin","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Economics and Management, China Jiliang University, Hangzhou 310018, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Wolf, P., Hubschneider, C., Weber, M., Bauer, A., H\u00e4rtl, J., D\u00fcrr, F., and Z\u00f6llner, J.M. (2017, January 11\u201314). Learning How to Drive in a Real World Simulation with Deep Q-Networks. Proceedings of the 2017 IEEE Intelligent Vehicles Symposium (IV), Los Angeles, CA, USA.","DOI":"10.1109\/IVS.2017.7995727"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Ye, D., Liu, Z., Sun, M., Shi, B., Zhao, P., Wu, H., Yu, H., Yang, S., Wu, X., and Guo, Q. (2020, January 7\u201312). Mastering Complex Control in Moba Games with Deep Reinforcement Learning. Proceedings of the AAAI Conference on Artificial Intelligence, New York, NY, USA.","DOI":"10.1609\/aaai.v34i04.6144"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"354","DOI":"10.1038\/nature24270","article-title":"Mastering the Game of Go without Human Knowledge","volume":"550","author":"Silver","year":"2017","journal-title":"Nature"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Yu, Z., Machado, P., Zahid, A., Abdulghani, A.M., Dashtipour, K., Heidari, H., Imran, M.A., and Abbasi, Q.H. (2020). Energy and Performance Trade-off Optimization in Heterogeneous Computing via Reinforcement Learning. Electronics, 9.","DOI":"10.3390\/electronics9111812"},{"key":"ref_5","unstructured":"Wang, R., Wei, H., An, B., Feng, Z., and Yao, J. (2020). Commission Fee Is Not Enough: A Hierarchical Reinforced Framework for Portfolio Management. arXiv."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Jiang, Z., and Liang, J. (2017, January 7\u20138). Cryptocurrency Portfolio Management with Deep Reinforcement Learning. Proceedings of the 2017 Intelligent Systems Conference (IntelliSys), London, UK.","DOI":"10.1109\/IntelliSys.2017.8324237"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Liang, Q., Zhu, M., Zheng, X., and Wang, Y. (2021, January 19\u201326). An Adaptive News-Driven Method for CVaR-Sensitive Online Portfolio Selection in Non-Stationary Financial Markets. Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, International Joint Conferences on Artificial Intelligence Organization, Montreal, QC, Canada.","DOI":"10.24963\/ijcai.2021\/373"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Yang, H., Liu, X.-Y., Zhong, S., and Walid, A. (2020, January 15\u201316). Deep Reinforcement Learning for Automated Stock Trading: An Ensemble Strategy. Proceedings of the First ACM International Conference on AI in Finance, New York, NY, USA.","DOI":"10.1145\/3383455.3422540"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"107788","DOI":"10.1016\/j.asoc.2021.107788","article-title":"Sentiment-Influenced Trading System Based on Multimodal Deep Reinforcement Learning","volume":"112","author":"Chen","year":"2021","journal-title":"Appl. Soft Comput."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2128","DOI":"10.1609\/aaai.v34i02.5587","article-title":"Adaptive Quantitative Trading: An Imitative Deep Reinforcement Learning Approach","volume":"34","author":"Liu","year":"2020","journal-title":"AAAI"},{"key":"ref_11","unstructured":"Lu, D.W. (2017). Agent Inspired Trading Using Recurrent Reinforcement Learning and LSTM Neural Networks. arXiv."},{"key":"ref_12","unstructured":"Verleysen, M., and Fran\u00e7ois, D. The Curse of Dimensionality in Data Mining and Time Series Prediction. Proceedings of the International Work-Conference on Artificial Neural Networks."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"114002","DOI":"10.1016\/j.eswa.2020.114002","article-title":"Deep Reinforcement Learning for Portfolio Management of Markets with a Dynamic Number of Assets","volume":"164","author":"Betancourt","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Huang, Z., and Tanaka, F. (2022). MSPM: A Modularized and Scalable Multi-Agent Reinforcement Learning-Based System for Financial Portfolio Management. PLoS ONE, 17.","DOI":"10.1371\/journal.pone.0265924"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"113573","DOI":"10.1016\/j.eswa.2020.113573","article-title":"An Intelligent Financial Portfolio Trading Strategy Using Deep Q-Learning","volume":"158","author":"Park","year":"2020","journal-title":"Expert Syst. Appl."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"114632","DOI":"10.1016\/j.eswa.2021.114632","article-title":"An Application of Deep Reinforcement Learning to Algorithmic Trading","volume":"173","author":"Ernst","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Meng, Q., Catchpoole, D., Skillicorn, D., and Kennedy, P.J. (2017, January 14\u201319). Relational Autoencoder for Feature Extraction. Proceedings of the 2017 International Joint Conference on Neural Networks (IJCNN), Anchorage, AK, USA.","DOI":"10.1109\/IJCNN.2017.7965877"},{"key":"ref_18","unstructured":"Yashaswi, K. (2022, August 07). Deep Reinforcement Learning for Portfolio Optimization Using Latent Feature State Space (LFSS) Module. Available online: https:\/\/arxiv.org\/abs\/2102.06233."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Jang, J.-G., Choi, D., Jung, J., and Kang, U. (2018, January 22\u201326). Zoom-Svd: Fast and Memory Efficient Method for Extracting Key Patterns in an Arbitrary Time Range. Proceedings of the 27th ACM International Conference on Information and Knowledge Management, Torino Italy.","DOI":"10.1145\/3269206.3271682"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Taylor, G.W., and Hinton, G.E. (2009, January 14\u201318). Factored Conditional Restricted Boltzmann Machines for Modeling Motion Style. Proceedings of the 26th Annual International Conference on Machine Learning, Montreal, QC, Canada.","DOI":"10.1145\/1553374.1553505"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"504","DOI":"10.1126\/science.1127647","article-title":"Reducing the Dimensionality of Data with Neural Networks","volume":"313","author":"Hinton","year":"2006","journal-title":"Science"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"113456","DOI":"10.1016\/j.eswa.2020.113456","article-title":"Financial Portfolio Optimization with Online Deep Reinforcement Learning and Restricted Stacked Autoencoder\u2014DeepBreath","volume":"156","author":"Soleymani","year":"2020","journal-title":"Expert Syst. Appl."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Li, L. (2021). An Automated Portfolio Trading System with Feature Preprocessing and Recurrent Reinforcement Learning. arXiv.","DOI":"10.1145\/3490354.3494376"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"6202","DOI":"10.1007\/s10489-021-02218-4","article-title":"Learning to Trade in Financial Time Series Using High-Frequency through Wavelet Transformation and Deep Reinforcement Learning","volume":"51","author":"Lee","year":"2021","journal-title":"Appl. Intell."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"108014","DOI":"10.1109\/ACCESS.2019.2932789","article-title":"Deep Robust Reinforcement Learning for Practical Algorithmic Trading","volume":"7","author":"Li","year":"2019","journal-title":"IEEE Access"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"8119","DOI":"10.1007\/s10489-021-02262-0","article-title":"Portfolio Management System in Equity Market Neutral Using Reinforcement Learning","volume":"51","author":"Wu","year":"2021","journal-title":"Appl. Intell."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1086\/294846","article-title":"Mutual Fund Performance","volume":"39","author":"Sharpe","year":"1966","journal-title":"J. Bus."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1016\/j.ins.2020.05.066","article-title":"Adaptive Stock Trading Strategies with Deep Reinforcement Learning Methods","volume":"538","author":"Wu","year":"2020","journal-title":"Inf. Sci."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1016\/j.eswa.2017.06.023","article-title":"An Adaptive Portfolio Trading System: A Risk-Return Portfolio Optimization Using Recurrent Reinforcement Learning with Expected Maximum Drawdown","volume":"87","author":"Almahdi","year":"2017","journal-title":"Expert Syst. Appl."},{"key":"ref_30","unstructured":"Grinold, R.C., and Kahn, R.N. (1995). Active Portfolio Management: Quantitative Theory and Applications, Probus."},{"key":"ref_31","first-page":"99","article-title":"Maximum Drawdown","volume":"17","author":"Atiya","year":"2004","journal-title":"Risk Mag."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Benhamou, E., Guez, B., and Paris, N. (2019). Omega and Sharpe Ratio. arXiv.","DOI":"10.2139\/ssrn.3469888"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"9","DOI":"10.22381\/emfm17120221","article-title":"Goods Tariff vs Digital Services Tax: Transatlantic Financial Market Reactions","volume":"17","author":"Bin","year":"2022","journal-title":"Econ. Manag. Financ. Mark."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"V\u0103t\u0103m\u0103nescu, E.-M., Bratianu, C., Dabija, D.-C., and Popa, S. (2022). Capitalizing Online Knowledge Networks: From Individual Knowledge Acquisition towards Organizational Achievements. J. Knowl. Manag.","DOI":"10.1108\/JKM-04-2022-0273"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"9","DOI":"10.22381\/emfm16320211","article-title":"An Exploratory Study on the Impact of the COVID-19 Confinement on the Financial Behavior of Individual Investors","volume":"16","author":"Priem","year":"2021","journal-title":"Econ. Manag. Financ. Mark."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1415","DOI":"10.3390\/jtaer16050080","article-title":"Customer Experience in Fintech","volume":"16","author":"Barbu","year":"2021","journal-title":"J. Theor. Appl. Electron. Commer. Res."},{"key":"ref_37","unstructured":"Fischer, T.G. (2022, August 07). Reinforcement Learning in Financial Markets\u2014A Survey; FAU Discussion Papers in Economics. Available online: https:\/\/www.econstor.eu\/handle\/10419\/183139."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Chen, L., and Gao, Q. (2019, January 18\u201320). Application of Deep Reinforcement Learning on Automated Stock Trading. Proceedings of the 2019 IEEE 10th International Conference on Software Engineering and Service Science (ICSESS), Beijing, China.","DOI":"10.1109\/ICSESS47205.2019.9040728"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Dang, Q.-V. (2019, January 19\u201320). Reinforcement Learning in Stock Trading. Proceedings of the International Conference on Computer Science, Applied Mathematics and Applications, Hanoi, Vietnam.","DOI":"10.1007\/978-3-030-38364-0_28"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1016\/j.eswa.2018.09.036","article-title":"Improving Financial Trading Decisions Using Deep Q-Learning: Predicting the Number of Shares, Action Strategies, and Transfer Learning","volume":"117","author":"Jeong","year":"2019","journal-title":"Expert Syst. Appl."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"653","DOI":"10.1109\/TNNLS.2016.2522401","article-title":"Deep Direct Reinforcement Learning for Financial Signal Representation and Trading","volume":"28","author":"Deng","year":"2017","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"875","DOI":"10.1109\/72.935097","article-title":"Learning to Trade via Direct Reinforcement","volume":"12","author":"Moody","year":"2001","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_43","unstructured":"Zhang, Z., Zohren, S., and Roberts, S. (2019). Deep Reinforcement Learning for Trading. arXiv."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Vishal, M., Satija, Y., and Babu, B.S. (2021, January 16\u201318). Trading Agent for the Indian Stock Market Scenario Using Actor-Critic Based Reinforcement Learning. Proceedings of the 2021 IEEE International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS), Bangalore, India. Available online: https:\/\/ieeexplore.ieee.org\/abstract\/document\/9683467.","DOI":"10.1109\/CSITSS54238.2021.9683467"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Pretorius, R., and van Zyl, T. (2022, August 05). Deep Reinforcement Learning and Convex Mean-Variance Optimisation for Portfolio Management 2022. Available online: https:\/\/arxiv.org\/abs\/2203.11318.","DOI":"10.36227\/techrxiv.19165745.v1"},{"key":"ref_46","unstructured":"Raffin, A., Hill, A., Ernestus, M., Gleave, A., Kanervisto, A., and Dormann, N. (2022, August 07). Stable Baselines3. Available online: https:\/\/www.ai4europe.eu\/sites\/default\/files\/2021-06\/README_5.pdf."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"160397","DOI":"10.1109\/ACCESS.2019.2951526","article-title":"DDSA: A Defense against Adversarial Attacks Using Deep Denoising Sparse Autoencoder","volume":"7","author":"Bakhti","year":"2019","journal-title":"IEEE Access"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Bao, W., Yue, J., and Rao, Y. (2017). A Deep Learning Framework for Financial Time Series Using Stacked Autoencoders and Long-Short Term Memory. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0180944"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"6647534","DOI":"10.1155\/2021\/6647534","article-title":"Forecasting Foreign Exchange Volatility Using Deep Learning Autoencoder-LSTM Techniques","volume":"2021","author":"Jung","year":"2021","journal-title":"Complexity"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"115127","DOI":"10.1016\/j.eswa.2021.115127","article-title":"Deep Graph Convolutional Reinforcement Learning for Financial Portfolio Management\u2013DeepPocket","volume":"182","author":"Soleymani","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"012011","DOI":"10.1088\/1742-6596\/1828\/1\/012011","article-title":"The Design and Implementation of Quantum Finance-Based Hybrid Deep Reinforcement Learning Portfolio Investment System","volume":"1828","author":"Qiu","year":"2021","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1527","DOI":"10.1162\/neco.2006.18.7.1527","article-title":"A Fast Learning Algorithm for Deep Belief Nets","volume":"18","author":"Hinton","year":"2006","journal-title":"Neural Comput."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Vincent, P., Larochelle, H., Bengio, Y., and Manzagol, P.-A. (2008, January 5\u20139). Extracting and Composing Robust Features with Denoising Autoencoders. Proceedings of the 25th International Conference on Machine Learning, New York, NY, USA.","DOI":"10.1145\/1390156.1390294"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Graves, A. (2012). Long Short-Term Memory. Supervised Sequence Labelling with Recurrent Neural Networks, Springer.","DOI":"10.1007\/978-3-642-24797-2"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Nelson, D.M., Pereira, A.C., and De Oliveira, R.A. (2017, January 14\u201319). Stock Market\u2019s Price Movement Prediction with LSTM Neural Networks. Proceedings of the 2017 International Joint Conference on Neural Networks (IJCNN), Anchorage, AK, USA.","DOI":"10.1109\/IJCNN.2017.7966019"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Yao, S., Luo, L., and Peng, H. (2018, January 8\u201311). High-Frequency Stock Trend Forecast Using LSTM Model. Proceedings of the 2018 13th International Conference on Computer Science & Education (ICCSE), Colombo, Sri Lanka.","DOI":"10.1109\/ICCSE.2018.8468703"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Zhao, Z., Rao, R., Tu, S., and Shi, J. (2017, January 6\u20138). Time-Weighted LSTM Model with Redefined Labeling for Stock Trend Prediction. Proceedings of the 2017 IEEE 29th International Conference on Tools with Artificial Intelligence (ICTAI), Boston, MA, USA.","DOI":"10.1109\/ICTAI.2017.00184"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Liu, X.-Y., Yang, H., Gao, J., and Wang, C.D. (2021, January 3). FinRL: Deep Reinforcement Learning Framework to Automate Trading in Quantitative Finance. Proceedings of the Second ACM International Conference on AI in Finance, New York, NY, USA.","DOI":"10.1145\/3490354.3494366"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Yang, H., Liu, X.-Y., and Wu, Q. (2018, January 1\u20133). A Practical Machine Learning Approach for Dynamic Stock Recommendation. Proceedings of the 2018 17th IEEE International Conference on Trust, Security and Privacy in Computing and Communications\/12th IEEE International Conference on Big Data Science and Engineering (TrustCom\/BigDataSE), New York, NY, USA.","DOI":"10.1109\/TrustCom\/BigDataSE.2018.00253"},{"key":"ref_60","unstructured":"Zhang, Y., Clavera, I., Tsai, B., and Abbeel, P. (2019). Asynchronous Methods for Model-Based Reinforcement Learning. arXiv."},{"key":"ref_61","unstructured":"Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., and Kavukcuoglu, K. (2016, January 20\u201322). Asynchronous Methods for Deep Reinforcement Learning. Proceedings of the International Conference on Machine Learning, PMLR, New York, NY, USA."},{"key":"ref_62","unstructured":"Brockman, G., Cheung, V., Pettersson, L., Schneider, J., Schulman, J., Tang, J., and Zaremba, W. (2016). Openai Gym. arXiv."},{"key":"ref_63","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A Method for Stochastic Optimization. arXiv."},{"key":"ref_64","first-page":"40","article-title":"Calmar Ratio: A Smoother Tool","volume":"20","author":"Young","year":"1991","journal-title":"Futures"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"579","DOI":"10.1613\/jair.1336","article-title":"Can We Learn to Beat the Best Stock","volume":"21","author":"Borodin","year":"2004","journal-title":"JAIR"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1142\/9789814293501_0015","article-title":"Universal Portfolios","volume":"Volume 3","author":"Cover","year":"2011","journal-title":"The Kelly Capital Growth Investment Criterion"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/1989734.1989741","article-title":"CORN: Correlation-Driven Nonparametric Learning Approach for Portfolio Selection","volume":"2","author":"Li","year":"2011","journal-title":"ACM Trans. Intell. Syst. Technol."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Agarwal, A., Hazan, E., Kale, S., and Schapire, R.E. (2006, January 25\u201329). Algorithms for Portfolio Management Based on the Newton Method. Proceedings of the 23rd International Conference on Machine Learning, New York, NY, USA.","DOI":"10.1145\/1143844.1143846"},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Yang, H., Liu, X.-Y., Zhong, S., and Walid, A. (2020). Deep Reinforcement Learning for Automated Stock Trading: An Ensemble Strategy. SSRN J.","DOI":"10.2139\/ssrn.3690996"},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Yao, W., Ren, X., and Su, J. (2022, January 4). An Inception Network with Bottleneck Attention Module for Deep Reinforcement Learning Framework in Financial Portfolio Management. Proceedings of the 2022 7th International Conference on Big Data Analytics (ICBDA), Guangzhou, China.","DOI":"10.1109\/ICBDA55095.2022.9760343"},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Ye, Y., Pei, H., Wang, B., Chen, P.-Y., Zhu, Y., Xiao, J., and Li, B. (2020, January 7\u201312). Reinforcement-Learning Based Portfolio Management with Augmented Asset Movement Prediction States. Proceedings of the AAAI Conference on Artificial Intelligence, New York, NY, USA.","DOI":"10.1609\/aaai.v34i01.5462"},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Ren, X., Jiang, Z., and Su, J. (2021, January 5). The Use of Features to Enhance the Capability of Deep Reinforcement Learning for Investment Portfolio Management. Proceedings of the 2021 IEEE 6th International Conference on Big Data Analytics (ICBDA), Xiamen, China.","DOI":"10.1109\/ICBDA51983.2021.9403019"},{"key":"ref_73","unstructured":"Jorion, P. (2022, August 07). Value at Risk. Available online: http:\/\/bear.warrington.ufl.edu\/aitsahlia\/Financial_Risk_Management.pdf."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"1443","DOI":"10.1016\/S0378-4266(02)00271-6","article-title":"Conditional Value-at-Risk for General Loss Distributions","volume":"26","author":"Rockafellar","year":"2002","journal-title":"J. Bank. Financ."}],"container-title":["Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2079-8954\/10\/5\/146\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:27:38Z","timestamp":1760142458000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2079-8954\/10\/5\/146"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9,8]]},"references-count":74,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2022,10]]}},"alternative-id":["systems10050146"],"URL":"https:\/\/doi.org\/10.3390\/systems10050146","relation":{},"ISSN":["2079-8954"],"issn-type":[{"value":"2079-8954","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,9,8]]}}}