{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T16:23:33Z","timestamp":1774023813423,"version":"3.50.1"},"reference-count":20,"publisher":"Tech Science Press","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["CMC"],"published-print":{"date-parts":[[2024]]},"DOI":"10.32604\/cmc.2024.051599","type":"journal-article","created":{"date-parts":[[2024,8,7]],"date-time":"2024-08-07T08:29:45Z","timestamp":1723019385000},"page":"3123-3138","source":"Crossref","is-referenced-by-count":18,"title":["Unleashing the Power of Multi-Agent Reinforcement Learning for Algorithmic Trading in the Digital Financial Frontier and Enterprise Information Systems"],"prefix":"10.32604","volume":"80","author":[{"given":"Saket","family":"Sarin","sequence":"first","affiliation":[]},{"given":"Sunil K.","family":"Singh","sequence":"additional","affiliation":[]},{"given":"Sudhakar","family":"Kumar","sequence":"additional","affiliation":[]},{"given":"Shivam","family":"Goyal","sequence":"additional","affiliation":[]},{"given":"Brij Bhooshan","family":"Gupta","sequence":"additional","affiliation":[]},{"given":"Wadee","family":"Alhalabi","sequence":"additional","affiliation":[]},{"given":"Varsha","family":"Arya","sequence":"additional","affiliation":[]}],"member":"17807","published-online":{"date-parts":[[2024]]},"reference":[{"key":"ref1","doi-asserted-by":"crossref","first-page":"118124","DOI":"10.1016\/j.eswa.2022.118124","article-title":"A multi-agent deep reinforcement learning framework for algorithmic trading in financial markets","volume":"208","author":"Shavandi","year":"2022","journal-title":"Expert. Syst. Appl."},{"key":"ref2","series-title":"Proc. First ACM Int. Conf. AI Finance","first-page":"1","article-title":"Multi-agent reinforcement learning in a realistic limit order book market simulation","author":"Karpe","year":"2020"},{"key":"ref3","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1007\/s10614-020-10038-w","article-title":"Modelling stock markets by multi-agent reinforcement learning","volume":"57","author":"Lussange","year":"2021","journal-title":"Comput. Econ."},{"key":"ref4","unstructured":"Y. Patel, \u201cOptimizing market making using multi-agent reinforcement learning,\u201d arXiv preprint arXiv:1812.10252, 2018."},{"key":"ref5","unstructured":"W. Bao and X. -Y. Liu, \u201cMulti-agent deep reinforcement learning for liquidation strategy analysis,\u201d arXiv preprint arXiv:1906.11046, 2019."},{"key":"ref6","unstructured":"W. Bao, \u201cFairness in multi-agent reinforcement learning for stock trading,\u201d arXiv preprint arXiv:2001.00918, 2019."},{"key":"ref7","doi-asserted-by":"crossref","unstructured":"J. Lee, R. Kim, S. -W. Yi, and J. Kang, \u201cMaps: Multi-agent reinforcement learning-based portfolio management system,\u201d arXiv preprint arXiv:2007.05402, 2020.","DOI":"10.24963\/ijcai.2020\/623"},{"key":"ref8","series-title":"Artificial Neural Netw. Mach. Learn.-ICANN 2018: 27th Int. Conf. Artificial Neural Netw.","first-page":"240","article-title":"Action markets in deep multi-agent reinforcement learning","author":"Schmid","year":"Oct. 4\u20137, 2018"},{"key":"ref9","unstructured":"T. -V. Pricope, \u201cDeep reinforcement learning in quantitative algorithmic trading: A review,\u201d arXiv preprint arXiv:2106.00123, 2021."},{"key":"ref10","doi-asserted-by":"crossref","first-page":"121502","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":"ref11","doi-asserted-by":"crossref","first-page":"153","DOI":"10.3390\/fintech2010011","article-title":"An intelligent system for trading signal of cryptocurrency based on market tweets sentiments","volume":"2","author":"Leung","year":"2023","journal-title":"FinTech"},{"key":"ref12","doi-asserted-by":"crossref","first-page":"1","DOI":"10.4018\/IJSSCI.334711","article-title":"A novel deep federated learning-based model to enhance privacy in critical infrastructure systems","volume":"15","author":"Sharma","year":"2023","journal-title":"Int. J. Softw. Sci. Comput. Intell."},{"key":"ref13","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1007\/s12599-017-0464-6","article-title":"Fintech","volume":"59","author":"Puschmann","year":"2017","journal-title":"Bus. & Inf. Syst. Eng."},{"key":"ref14","series-title":"Int. Conf. Cyber Secur., Privacy Netw.","first-page":"309","article-title":"Automated machine learning (AutoML): The future of computational intelligence","author":"Mengi","year":"2021"},{"key":"ref15","first-page":"1","article-title":"A multi-agent-based VM migration for dynamic load balancing in cloud computing cloud environment","volume":"13","author":"Swarnakar","year":"2023","journal-title":"Int. J. Cloud Appl. Comput."},{"key":"ref16","series-title":"2023 IEEE Int. Conf. Consum. Electron. (ICCE)","first-page":"1","article-title":"Fuzzy based clustering of consumers\u2019 big data in industrial applications","author":"Sharma","year":"2023"},{"key":"ref17","doi-asserted-by":"crossref","first-page":"1","DOI":"10.4018\/IJSSCI.313593","article-title":"Sustainable stock market prediction framework using machine learning models","volume":"14","author":"Pe\u00f1alvo","year":"2022","journal-title":"Int. J. Softw. Sci. Comput. Intell."},{"key":"ref18","doi-asserted-by":"crossref","first-page":"116659","DOI":"10.1016\/j.eswa.2022.116659","article-title":"Machine learning techniques and data for stock market forecasting: A literature review","volume":"197","author":"Kumbure","year":"2022","journal-title":"Expert. Syst. Appl."},{"key":"ref19","doi-asserted-by":"crossref","first-page":"115537","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":"ref20","series-title":"2022 3rd Int. Conf. Emerg. Technol. (INCET)","first-page":"1","article-title":"Efficient loop unrolling factor prediction algorithm using machine learning models","author":"Singh","year":"2022"}],"container-title":["Computers, Materials &amp; Continua"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.techscience.com\/files\/cmc\/2024\/TSP_CMC-80-2\/TSP_CMC_51599\/TSP_CMC_51599.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,6]],"date-time":"2025-03-06T12:14:39Z","timestamp":1741263279000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.techscience.com\/cmc\/v80n2\/57597"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"references-count":20,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2024]]},"published-print":{"date-parts":[[2024]]}},"URL":"https:\/\/doi.org\/10.32604\/cmc.2024.051599","relation":{},"ISSN":["1546-2226"],"issn-type":[{"value":"1546-2226","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]}}}