{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T03:23:19Z","timestamp":1768533799548,"version":"3.49.0"},"reference-count":34,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,12,14]],"date-time":"2025-12-14T00:00:00Z","timestamp":1765670400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,1,15]],"date-time":"2026-01-15T00:00:00Z","timestamp":1768435200000},"content-version":"vor","delay-in-days":32,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Comput Intell Syst"],"DOI":"10.1007\/s44196-025-01094-x","type":"journal-article","created":{"date-parts":[[2025,12,14]],"date-time":"2025-12-14T03:33:27Z","timestamp":1765683207000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["RiskawareTrader: A Reinforcement Learning based Portfolio Optimization for Risk Averter"],"prefix":"10.1007","volume":"19","author":[{"given":"Min","family":"Yang","sequence":"first","affiliation":[]},{"given":"Jin","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Yi","family":"Hu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,12,14]]},"reference":[{"issue":"1","key":"1094_CR1","first-page":"71","volume":"7","author":"HM Markowits","year":"1952","unstructured":"Markowits, H.M.: Portfolio selection. J. Finance 7(1), 71\u201391 (1952)","journal-title":"J. Finance"},{"issue":"3","key":"1094_CR2","doi-asserted-by":"publisher","first-page":"1009","DOI":"10.1111\/1540-6261.00556","volume":"58","author":"D Hirshleifer","year":"2003","unstructured":"Hirshleifer, D., Shumway, T.: Good day sunshine: stock returns and the weather. J. Finance 58(3), 1009\u20131032 (2003)","journal-title":"J. Finance"},{"issue":"1","key":"1094_CR3","doi-asserted-by":"publisher","first-page":"295","DOI":"10.1146\/annurev-economics-080614-115502","volume":"9","author":"TJ Kehoe","year":"2017","unstructured":"Kehoe, T.J., Pujolas, P.S., Rossbach, J.: Quantitative trade models: developments and challenges. Annual Rev. Econ. 9(1), 295\u2013325 (2017)","journal-title":"Annual Rev. Econ."},{"issue":"1","key":"1094_CR4","first-page":"236","volume":"34","author":"Y Zhang","year":"2020","unstructured":"Zhang, Y., Zhao, P., Wu, Q., Li, B., Huang, J., Tan, M.: Cost-sensitive portfolio selection via deep reinforcement learning. IEEE Trans. Knowl. Data Eng. 34(1), 236\u2013248 (2020)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"1094_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.119707","volume":"221","author":"T Zhao","year":"2023","unstructured":"Zhao, T., Ma, X., Li, X., Zhang, C.: Asset correlation based deep reinforcement learning for the portfolio selection. Expert Syst. Appl. 221, 119707 (2023)","journal-title":"Expert Syst. Appl."},{"key":"1094_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2024.111697","volume":"293","author":"W Jiang","year":"2024","unstructured":"Jiang, W., et al.: New reinforcement learning based on representation transfer for portfolio management. Knowl.-Based Syst. 293, 111697 (2024)","journal-title":"Knowl.-Based Syst."},{"key":"1094_CR7","unstructured":"Vajda, I.: Analysis of semi-log-optimal investment strategies. Prague Stochastics (2006)"},{"key":"1094_CR8","doi-asserted-by":"crossref","unstructured":"Agarwal, A., Hazan, E., Kale, S., et al.: Algorithms for portfolio management based on the newton method. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 9\u201316 (2006)","DOI":"10.1145\/1143844.1143846"},{"key":"1094_CR9","unstructured":"Li, B., Hoi, S.C.H.: On-line portfolio selection with moving average reversion. arXiv preprint arXiv:1206.4626 (2012)"},{"issue":"2","key":"1094_CR10","first-page":"337","volume":"16","author":"L Gy\u00f6rfi","year":"2006","unstructured":"Gy\u00f6rfi, L., Lugosi, G., Udina, F.: Nonparametric kernel-based sequential investment strategies. Math. Finance Int. J. Math. Stat. Financ. Econ. 16(2), 337\u2013357 (2006)","journal-title":"Math. Finance Int. J. Math. Stat. Financ. Econ."},{"key":"1094_CR11","doi-asserted-by":"crossref","unstructured":"Hester, T., Vecerik, M., Pietquin, O., et al.: Deep q-learning from demonstrations. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018)","DOI":"10.1609\/aaai.v32i1.11757"},{"issue":"7540","key":"1094_CR12","doi-asserted-by":"publisher","first-page":"529","DOI":"10.1038\/nature14236","volume":"518","author":"V Mnih","year":"2015","unstructured":"Mnih, V., Kavukcuoglu, K., Silver, D., et al.: Human-level control through deep reinforcement learning. Nature 518(7540), 529\u2013533 (2015)","journal-title":"Nature"},{"key":"1094_CR13","unstructured":"Jiang, Z., Xu, D., Liang, J.: A deep reinforcement learning framework for the financial portfolio management problem. arXiv preprint arXiv:1706.10059 (2017)"},{"key":"1094_CR14","unstructured":"Xiong, Z., Liu, X.-Y., Zhong, S., Yang, H., Walid, A.: Practical deep reinforcement learning approach for stock trading. arXiv preprint arXiv:1811.07522 (2018)"},{"issue":"4","key":"1094_CR15","doi-asserted-by":"publisher","first-page":"857","DOI":"10.1016\/j.icte.2024.04.010","volume":"10","author":"B Enkhsaikhan","year":"2024","unstructured":"Enkhsaikhan, B., Jo, O.: Risk-averse reinforcement learning for portfolio optimization. ICT Express 10(4), 857\u2013862 (2024)","journal-title":"ICT Express"},{"issue":"5","key":"1094_CR16","doi-asserted-by":"publisher","first-page":"2489","DOI":"10.1109\/TBDATA.2025.3533905","volume":"11","author":"B Enkhsaikhan","year":"2025","unstructured":"Enkhsaikhan, B., Jo, O.: Risk-constrained reinforcement learning with augmented lagrangian multiplier for portfolio optimization. IEEE Trans. Big Data 11(5), 2489\u20132502 (2025)","journal-title":"IEEE Trans. Big Data"},{"key":"1094_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.econmod.2022.106078","volume":"119","author":"T Cui","year":"2023","unstructured":"Cui, T., Ding, S., Jin, H., Zhang, Y.: Portfolio constructions in cryptocurrency market: a cvar-based deep reinforcement learning approach. Econ. Model. 119, 106078 (2023)","journal-title":"Econ. Model."},{"key":"1094_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.120297","volume":"227","author":"SJ Yoo","year":"2023","unstructured":"Yoo, S.J., Gu, Y.H., et al.: Safety aarl: weight adjustment for reinforcement-learning-based safety dynamic asset allocation strategies. Expert Syst. Appl. 227, 120297 (2023)","journal-title":"Expert Syst. Appl."},{"issue":"4","key":"1094_CR19","doi-asserted-by":"publisher","first-page":"2033","DOI":"10.1007\/s10614-023-10509-w","volume":"64","author":"T-F Chen","year":"2024","unstructured":"Chen, T.-F., Kuang, X.-J., Liao, S.-L., Lin, S.-K.: Portfolio allocation with dynamic risk preferences via reinforcement learning. Comput. Econ. 64(4), 2033\u20132052 (2024)","journal-title":"Comput. Econ."},{"issue":"3","key":"1094_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2022.103247","volume":"60","author":"J Li","year":"2023","unstructured":"Li, J., Zhang, Y., Yang, X., Chen, L.: Online portfolio management via deep reinforcement learning with high-frequency data. Inf. Process. Manag. 60(3), 103247 (2023)","journal-title":"Inf. Process. Manag."},{"key":"1094_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.122801","volume":"242","author":"J Zou","year":"2024","unstructured":"Zou, J., Lou, J., Wang, B., Liu, S.: A novel deep reinforcement learning based automated stock trading system using cascaded lstm networks. Expert Syst. Appl. 242, 122801 (2024). https:\/\/doi.org\/10.1016\/j.eswa.2023.122801","journal-title":"Expert Syst. Appl."},{"key":"1094_CR22","doi-asserted-by":"crossref","unstructured":"Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, pp. 4647\u20134653 (2024)","DOI":"10.24963\/ijcai.2020\/641"},{"issue":"1","key":"1094_CR23","first-page":"1","volume":"6","author":"D Ramya","year":"2025","unstructured":"Ramya, D., et al.: Reinforcement learning driven trading algorithm with optimized stock portfolio management scheme to control financial risk. SN Comput. Sci. 6(1), 1\u201316 (2025)","journal-title":"SN Comput. Sci."},{"issue":"3","key":"1094_CR24","doi-asserted-by":"publisher","first-page":"437","DOI":"10.1111\/mafi.12382","volume":"33","author":"B Hambly","year":"2023","unstructured":"Hambly, B., Xu, R., Yang, H.: Recent advances in reinforcement learning in finance. Math. Financ. 33(3), 437\u2013503 (2023)","journal-title":"Math. Financ."},{"issue":"1","key":"1094_CR25","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1016\/j.jfds.2016.03.002","volume":"2","author":"R Dash","year":"2016","unstructured":"Dash, R., Dash, P.K.: A hybrid stock trading framework integrating technical analysis with machine learning techniques. J. Finance Data Sci. 2(1), 42\u201357 (2016). https:\/\/doi.org\/10.1016\/j.jfds.2016.03.002","journal-title":"J. Finance Data Sci."},{"key":"1094_CR26","doi-asserted-by":"publisher","unstructured":"Zhang, H., Fang, Y., Liu, X.: A deep reinforcement learning model for portfolio management incorporating historical stock prices and risk information. In: Proceedings of the 2024 8th International Conference on Deep Learning Technologies. ICDLT \u201924, pp. 1\u20138. Association for Computing Machinery, New York, NY, USA (2024). https:\/\/doi.org\/10.1145\/3695719.3695720","DOI":"10.1145\/3695719.3695720"},{"key":"1094_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.119556","volume":"218","author":"J Jang","year":"2023","unstructured":"Jang, J., Seong, N.: Deep reinforcement learning for stock portfolio optimization by connecting with modern portfolio theory. Expert Syst. Appl. 218, 119556 (2023). https:\/\/doi.org\/10.1016\/j.eswa.2023.119556","journal-title":"Expert Syst. Appl."},{"key":"1094_CR28","doi-asserted-by":"crossref","unstructured":"Yang, H., et al.: Deep reinforcement learning for automated stock trading: an ensemble strategy. In: Proceedings of the First ACM International Conference on AI in Finance (2020)","DOI":"10.1145\/3383455.3422540"},{"issue":"1","key":"1094_CR29","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1111\/j.1467-9965.1991.tb00002.x","volume":"1","author":"TM Cover","year":"1991","unstructured":"Cover, T.M.: Universal portfolios. Math. Finance 1(1), 1\u201329 (1991)","journal-title":"Math. Finance"},{"issue":"3","key":"1094_CR30","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/1961189.1961193","volume":"2","author":"B Li","year":"2011","unstructured":"Li, B., Hoi, S.C., Gopalkrishnan, V.: Corn: correlation-driven nonparametric learning approach for portfolio selection. ACM Trans. Intell. Syst. Technol. (TIST) 2(3), 1\u201329 (2011)","journal-title":"ACM Trans. Intell. Syst. Technol. (TIST)"},{"issue":"4","key":"1094_CR31","doi-asserted-by":"publisher","first-page":"325","DOI":"10.1111\/1467-9965.00058","volume":"8","author":"DP Helmbold","year":"1998","unstructured":"Helmbold, D.P., Schapire, R.E., Singer, Y., Warmuth, M.K.: On-line portfolio selection using multiplicative updates. Math. Financ. 8(4), 325\u2013347 (1998)","journal-title":"Math. Financ."},{"key":"1094_CR32","doi-asserted-by":"crossref","unstructured":"Gu, J., Du, W., Rahman, A.M.M., Wang, G.: Margin trader: A reinforcement learning framework for portfolio management with margin and constraints. In: 4th ACM International Conference on AI in Finance, pp. 610\u2013618 (2023)","DOI":"10.1145\/3604237.3626906"},{"key":"1094_CR33","unstructured":"Li, Z., Tam, V., Yeung, K.L.: Developing a multi-agent and self-adaptive framework with deep reinforcement learning for dynamic portfolio risk management. In: Proceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems (AAMAS \u201924), pp. 1174\u20131182 (2024)"},{"key":"1094_CR34","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2024.127800","volume":"594","author":"L-C Cheng","year":"2024","unstructured":"Cheng, L.-C., Sun, J.-S.: Multiagent-based deep reinforcement learning framework for multi-asset adaptive trading and portfolio management. Neurocomputing 594, 127800 (2024)","journal-title":"Neurocomputing"}],"container-title":["International Journal of Computational Intelligence Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44196-025-01094-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s44196-025-01094-x","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44196-025-01094-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,15]],"date-time":"2026-01-15T10:48:28Z","timestamp":1768474108000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s44196-025-01094-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,14]]},"references-count":34,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["1094"],"URL":"https:\/\/doi.org\/10.1007\/s44196-025-01094-x","relation":{},"ISSN":["1875-6883"],"issn-type":[{"value":"1875-6883","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,14]]},"assertion":[{"value":"19 June 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 October 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 November 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 December 2025","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The author has no conflict of interest to declare in relation to the content of this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}}],"article-number":"25"}}