{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T07:48:38Z","timestamp":1777880918915,"version":"3.51.4"},"reference-count":39,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62572095"],"award-info":[{"award-number":["62572095"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["DUTZD25216"],"award-info":[{"award-number":["DUTZD25216"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["DUT25YG246"],"award-info":[{"award-number":["DUT25YG246"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100009965","name":"Dalian Science and Technology Bureau","doi-asserted-by":"publisher","award":["2023JJ12SN029"],"award-info":[{"award-number":["2023JJ12SN029"]}],"id":[{"id":"10.13039\/501100009965","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100009965","name":"Dalian Science and Technology Bureau","doi-asserted-by":"publisher","award":["2023JJ11CG005"],"award-info":[{"award-number":["2023JJ11CG005"]}],"id":[{"id":"10.13039\/501100009965","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100009965","name":"Dalian Science and Technology Bureau","doi-asserted-by":"publisher","award":["2024JJ12GX025"],"award-info":[{"award-number":["2024JJ12GX025"]}],"id":[{"id":"10.13039\/501100009965","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Engineering Applications of Artificial Intelligence"],"published-print":{"date-parts":[[2026,4]]},"DOI":"10.1016\/j.engappai.2026.114130","type":"journal-article","created":{"date-parts":[[2026,2,9]],"date-time":"2026-02-09T13:17:46Z","timestamp":1770643066000},"page":"114130","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Adaptive multi-agent stock trading decision support system based on deep reinforcement learning"],"prefix":"10.1016","volume":"169","author":[{"given":"Xu","family":"Yuan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiaqiang","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-7765-1449","authenticated-orcid":false,"given":"Shaokui","family":"Gu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yi","family":"Guo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ange","family":"Qi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shijin","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6301-1311","authenticated-orcid":false,"given":"Liang","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"78","reference":[{"key":"10.1016\/j.engappai.2026.114130_b1","unstructured":"Aadhitya, A., Rajapriya, R., Vineetha, R., Bagde, A.M., 2023. Predicting Stock Market Time-Series Data using CNN-LSTM Neural Network Model. Technical Report."},{"key":"10.1016\/j.engappai.2026.114130_b2","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2023.121849","article-title":"Deep reinforcement learning for financial trading using multi-modal features","volume":"238","author":"Avramelou","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.engappai.2026.114130_b3","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2021.107119","article-title":"Technical analysis strategy optimization using a machine learning approach in stock market indices","volume":"225","author":"Ayala","year":"2021","journal-title":"Knowl.-Based Syst."},{"issue":"4","key":"10.1016\/j.engappai.2026.114130_b4","doi-asserted-by":"crossref","DOI":"10.1111\/exsy.12514","article-title":"Trend following deep Q-Learning strategy for stock trading","volume":"37","author":"Chakole","year":"2020","journal-title":"Expert Syst."},{"issue":"6","key":"10.1016\/j.engappai.2026.114130_b5","doi-asserted-by":"crossref","first-page":"1887","DOI":"10.1093\/rfs\/hhae008","article-title":"Equity return expectations and portfolios: Evidence from large asset managers","volume":"37","author":"Dahlquist","year":"2024","journal-title":"Rev. Financ. Stud."},{"issue":"1","key":"10.1016\/j.engappai.2026.114130_b6","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1257\/jel.20241733","article-title":"Deep learning for economists","volume":"63","author":"Dell","year":"2025","journal-title":"J. Econ. Lit."},{"issue":"3","key":"10.1016\/j.engappai.2026.114130_b7","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":"2016","journal-title":"IEEE Trans. Neural Networks Learn. Syst."},{"key":"10.1016\/j.engappai.2026.114130_b8","series-title":"Constrained max drawdown: a fast and robust portfolio optimization approach","author":"Dorador","year":"2024"},{"issue":"3","key":"10.1016\/j.engappai.2026.114130_b9","doi-asserted-by":"crossref","first-page":"1074","DOI":"10.1017\/S0022109024000036","article-title":"Estimating stock market betas via machine learning","volume":"60","author":"Drobetz","year":"2025","journal-title":"J. Financ. Quant. Anal."},{"key":"10.1016\/j.engappai.2026.114130_b10","series-title":"2025 IEEE 14th International Conference on Communication Systems and Network Technologies","first-page":"59","article-title":"Automatic optimization of trading strategies based on reinforcement learning","author":"Fan","year":"2025"},{"key":"10.1016\/j.engappai.2026.114130_b11","series-title":"NASDAQ index historical data","author":"Finance","year":"2024"},{"issue":"8081","key":"10.1016\/j.engappai.2026.114130_b12","doi-asserted-by":"crossref","first-page":"633","DOI":"10.1038\/s41586-025-09422-z","article-title":"DeepSeek-R1 incentivizes reasoning in LLMs through reinforcement learning","volume":"645","author":"Guo","year":"2025","journal-title":"Nature"},{"issue":"1\u20132","key":"10.1016\/j.engappai.2026.114130_b13","first-page":"89","article-title":"An intelligent stock trading decision system based on ensemble classifier through multimodal perturbation","volume":"48","author":"Hou","year":"2025","journal-title":"J. Intell. Fuzzy Systems"},{"key":"10.1016\/j.engappai.2026.114130_b14","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1016\/j.inffus.2022.10.006","article-title":"Attentive gated graph sequence neural network-based time-series information fusion for financial trading","volume":"91","author":"Huang","year":"2023","journal-title":"Inf. Fusion"},{"key":"10.1016\/j.engappai.2026.114130_b15","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2023.122581","article-title":"A novel deep reinforcement learning framework with BiLSTM-Attention networks for algorithmic trading","volume":"240","author":"Huang","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.engappai.2026.114130_b16","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2023.121502","article-title":"A multi-agent reinforcement learning framework for optimizing financial trading strategies based on timesnet","volume":"237","author":"Huang","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.engappai.2026.114130_b17","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2021.115537","article-title":"Applications of deep learning in stock market prediction: recent progress","volume":"184","author":"Jiang","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.engappai.2026.114130_b18","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2021.115019","article-title":"A hybrid model integrating deep learning with investor sentiment analysis for stock price prediction","volume":"178","author":"Jing","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.engappai.2026.114130_b19","first-page":"168","article-title":"Integrating LSTM and CNN for stock market prediction: A dynamic machine learning approach","volume":"5","author":"Joshi","year":"2025","journal-title":"J. Artif. Intell. Technol."},{"key":"10.1016\/j.engappai.2026.114130_b20","doi-asserted-by":"crossref","DOI":"10.1016\/j.jfineco.2024.103837","article-title":"In-sample and out-of-sample sharpe ratios of multi-factor asset pricing models","volume":"155","author":"Kan","year":"2024","journal-title":"J. Financ. Econ."},{"issue":"8","key":"10.1016\/j.engappai.2026.114130_b21","doi-asserted-by":"crossref","first-page":"10896","DOI":"10.1016\/j.eswa.2009.02.038","article-title":"Using support vector machine with a hybrid feature selection method to the stock trend prediction","volume":"36","author":"Lee","year":"2009","journal-title":"Expert Syst. Appl."},{"issue":"2","key":"10.1016\/j.engappai.2026.114130_b22","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1016\/j.jfineco.2021.08.017","article-title":"Machine learning in the Chinese stock market","volume":"145","author":"Leippold","year":"2022","journal-title":"J. Financ. Econ."},{"issue":"1","key":"10.1016\/j.engappai.2026.114130_b23","article-title":"Enterprise financial influencing factors and early warning based on decision tree model","volume":"2022","author":"Liao","year":"2022","journal-title":"Sci. Program."},{"key":"10.1016\/j.engappai.2026.114130_b24","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1016\/j.knosys.2018.10.034","article-title":"Deep learning-based feature engineering for stock price movement prediction","volume":"164","author":"Long","year":"2019","journal-title":"Knowl.-Based Syst."},{"key":"10.1016\/j.engappai.2026.114130_b25","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2023.121245","article-title":"Algorithmic trading using continuous action space deep reinforcement learning","volume":"235","author":"Majidi","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.engappai.2026.114130_b26","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2023.121711","article-title":"A deep Q-learning based algorithmic trading system for commodity futures markets","volume":"237","author":"Massahi","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.engappai.2026.114130_b27","series-title":"CNN-DRL for scalable actions in finance","author":"Montazeri","year":"2024"},{"key":"10.1016\/j.engappai.2026.114130_b28","doi-asserted-by":"crossref","DOI":"10.1016\/j.physa.2025.130533","article-title":"Stock market forecasting based on machine learning: The role of investor sentiment","volume":"666","author":"Ren","year":"2025","journal-title":"Phys. A"},{"key":"10.1016\/j.engappai.2026.114130_b29","doi-asserted-by":"crossref","DOI":"10.1109\/ACCESS.2025.3546099","article-title":"A novel rms-driven deep reinforcement learning for optimized portfolio management in stock trading","author":"Sattar","year":"2025","journal-title":"IEEE Access"},{"issue":"12","key":"10.1016\/j.engappai.2026.114130_b30","doi-asserted-by":"crossref","first-page":"10109","DOI":"10.1007\/s11042-024-19340-3","article-title":"Automated passive income from stock market using machine learning and big data analytics with security aspects","volume":"84","author":"Sharma","year":"2025","journal-title":"Multimedia Tools Appl."},{"key":"10.1016\/j.engappai.2026.114130_b31","first-page":"59047","article-title":"Trademaster: A holistic quantitative trading platform empowered by reinforcement learning","volume":"36","author":"Sun","year":"2023","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.engappai.2026.114130_b32","first-page":"125","article-title":"Of machine learning techniques","volume":"1320","author":"Sutaria","year":"2025"},{"key":"10.1016\/j.engappai.2026.114130_b33","doi-asserted-by":"crossref","DOI":"10.1016\/j.physa.2023.129044","article-title":"FTX\u2019s downfall and Binance\u2019s consolidation: The fragility of centralised digital finance","volume":"625","author":"Vidal-Tom\u00e1s","year":"2023","journal-title":"Phys. A"},{"issue":"2","key":"10.1016\/j.engappai.2026.114130_b34","doi-asserted-by":"crossref","first-page":"685","DOI":"10.1007\/s12065-021-00694-8","article-title":"Adaptive deep Q learning network with reinforcement learning for crime prediction","volume":"16","author":"Vimala Devi","year":"2023","journal-title":"Evol. Intell."},{"key":"10.1016\/j.engappai.2026.114130_b35","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2023.110033","article-title":"Multi-criteria fuzzy portfolio selection based on three-way decisions and cumulative prospect theory","volume":"134","author":"Wang","year":"2023","journal-title":"Appl. Soft Comput."},{"issue":"1","key":"10.1016\/j.engappai.2026.114130_b36","doi-asserted-by":"crossref","first-page":"290","DOI":"10.54097\/5yzwty57","article-title":"Research on finance credit risk quantification model based on machine learning algorithm","volume":"10","author":"Wang","year":"2024","journal-title":"Acad. J. Sci. Technol."},{"key":"10.1016\/j.engappai.2026.114130_b37","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1016\/j.eswa.2017.02.041","article-title":"Stock market one-day ahead movement prediction using disparate data sources","volume":"79","author":"Weng","year":"2017","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.engappai.2026.114130_b38","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":"Inform. Sci."},{"key":"10.1016\/j.engappai.2026.114130_b39","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2022.110211","article-title":"Deep reinforcement learning based on transformer and U-Net framework for stock trading","volume":"262","author":"Yang","year":"2023","journal-title":"Knowl.-Based Syst."}],"updated-by":[{"DOI":"10.1016\/j.engappai.2026.114209","type":"erratum","label":"Erratum","source":"publisher","updated":{"date-parts":[[2026,2,17]],"date-time":"2026-02-17T00:00:00Z","timestamp":1771286400000}},{"DOI":"10.1016\/j.engappai.2026.114209","type":"erratum","label":"Erratum","source":"publisher","updated":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T00:00:00Z","timestamp":1776211200000}}],"container-title":["Engineering Applications of Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0952197626004112?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0952197626004112?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:26:31Z","timestamp":1777595191000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0952197626004112"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4]]},"references-count":39,"alternative-id":["S0952197626004112"],"URL":"https:\/\/doi.org\/10.1016\/j.engappai.2026.114130","relation":{},"ISSN":["0952-1976"],"issn-type":[{"value":"0952-1976","type":"print"}],"subject":[],"published":{"date-parts":[[2026,4]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Adaptive multi-agent stock trading decision support system based on deep reinforcement learning","name":"articletitle","label":"Article Title"},{"value":"Engineering Applications of Artificial Intelligence","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.engappai.2026.114130","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"114130"}}