{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T04:58:58Z","timestamp":1750309138279,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":12,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,10,27]],"date-time":"2023-10-27T00:00:00Z","timestamp":1698364800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/https:\/\/doi.org\/10.13039\/501100012456","name":"National Social Science Fund of China","doi-asserted-by":"publisher","award":["18BGL224"],"award-info":[{"award-number":["18BGL224"]}],"id":[{"id":"10.13039\/https:\/\/doi.org\/10.13039\/501100012456","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,10,27]]},"DOI":"10.1145\/3650215.3650362","type":"proceedings-article","created":{"date-parts":[[2024,4,16]],"date-time":"2024-04-16T22:11:20Z","timestamp":1713305480000},"page":"840-844","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["A novel portfolio strategy approach using deep reinforcement learning"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-1305-9434","authenticated-orcid":false,"given":"Xu","family":"Yang","sequence":"first","affiliation":[{"name":"School of Economics and Management, China Jiliang University, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-7186-024X","authenticated-orcid":false,"given":"Chunyu","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Economics and Management, China Jiliang University, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-2189-4596","authenticated-orcid":false,"given":"Jiapeng","family":"Liu","sequence":"additional","affiliation":[{"name":"Commercial college, Zhejiang Wanli University, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-6373-2228","authenticated-orcid":false,"given":"Han","family":"Yue","sequence":"additional","affiliation":[{"name":"School of Economics and Management, China Jiliang University, China"}]}],"member":"320","published-online":{"date-parts":[[2024,4,16]]},"reference":[{"key":"e_1_3_2_1_1_1","first-page":"12","volume-title":"Deep learning for finance: deep portfolios. Applied Stochastic Models in Business and Industry vol 33","author":"Heaton J B","unstructured":"Heaton J B, Polson N G, Witte J H. 2017. Deep learning for finance: deep portfolios. Applied Stochastic Models in Business and Industry vol 33(Wiley Press) pp 3\u201312."},{"key":"e_1_3_2_1_2_1","first-page":"112872","volume-title":"Time-driven feature-aware jointly deep reinforcement learning for financial signal representation and algorithmic trading. Expert Systems with Applications vol 140","author":"Lei K","unstructured":"Lei K, Zhang B, Li Y. 2020. Time-driven feature-aware jointly deep reinforcement learning for financial signal representation and algorithmic trading. Expert Systems with Applications vol 140 (Elsevier press) p 112872."},{"key":"e_1_3_2_1_3_1","first-page":"1937","volume-title":"Int. Conf. on Machine Learning","author":"Mnih V","year":"2016","unstructured":"Mnih V, Badia A P, Mirza M. 2016. Asynchronous methods for deep reinforcement learning. Int. Conf. on Machine Learning (New York, U.S.) pp 1928\u20131937."},{"key":"e_1_3_2_1_4_1","first-page":"279","volume-title":"Reinforcement Learning for Trading Systems and Portfolios. Int. Conf. on Knowledge Discovery and Data Mining","author":"Moody J E","year":"1998","unstructured":"Moody J E, Saffell M, Liao Y. 1998. Reinforcement Learning for Trading Systems and Portfolios. Int. Conf. on Knowledge Discovery and Data Mining (New York, U.S.) pp 279-283."},{"key":"e_1_3_2_1_5_1","article-title":"Deep direct reinforcement learning for financial signal representation and trading","volume":"28","author":"Deng Y","year":"2016","unstructured":"Deng Y, Bao F, Kong Y. 2016. Deep direct reinforcement learning for financial signal representation and trading. IEEE Transactions on Neural Networks and Learning Systems vol 28 (IEEE press) pp 653\u2013664.","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_6_1","DOI":"10.1109\/IntelliSys.2017.8324237"},{"key":"e_1_3_2_1_7_1","first-page":"2715","volume-title":"An Adaptive News-Driven Method for CVaR-Sensitive Online Portfolio Selection in Non-Stationary Financial Markets. Int. Joint Conf. on Artificial Intelligence.","author":"Liang Q","year":"2021","unstructured":"Liang Q, Zhu M, Zheng X. 2021. An Adaptive News-Driven Method for CVaR-Sensitive Online Portfolio Selection in Non-Stationary Financial Markets. Int. Joint Conf. on Artificial Intelligence. (Montreal, Canada) pp 2708\u20132715."},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_8_1","DOI":"10.1109\/ICBDA49040.2020.9101333"},{"key":"e_1_3_2_1_9_1","first-page":"182","volume-title":"Portfolio Trading System of Digital Currencies: A Deep Reinforcement Learning with Multidimensional Attention Gating Mechanism. Neurocomputing, vol 402","author":"Weng L","unstructured":"Weng L, Sun X, Xia M. 2020. Portfolio Trading System of Digital Currencies: A Deep Reinforcement Learning with Multidimensional Attention Gating Mechanism. Neurocomputing, vol 402 (Elsevier press) pp 171\u2013182."},{"key":"e_1_3_2_1_10_1","first-page":"34","volume-title":"U.S.A.\/AAAI press)","author":"Ye Y","unstructured":"Ye Y, Pei H, Wang B. 2020. Reinforcement-learning based portfolio management with augmented asset movement prediction states Proceedings of the AAAI Conf. on Artificial Intelligence vol 4 (California, U.S.A.\/AAAI press) p 34."},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_11_1","DOI":"10.1145\/3490354.3494376"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_12_1","DOI":"10.1145\/3383455.3422540"}],"event":{"acronym":"ICMLCA 2023","name":"ICMLCA 2023: 2023 4th International Conference on Machine Learning and Computer Application","location":"Hangzhou China"},"container-title":["2023 4th International Conference on Machine Learning and Computer Application"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3650215.3650362","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3650215.3650362","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T22:50:13Z","timestamp":1750287013000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3650215.3650362"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,27]]},"references-count":12,"alternative-id":["10.1145\/3650215.3650362","10.1145\/3650215"],"URL":"https:\/\/doi.org\/10.1145\/3650215.3650362","relation":{},"subject":[],"published":{"date-parts":[[2023,10,27]]},"assertion":[{"value":"2024-04-16","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}