{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T09:57:18Z","timestamp":1779357438428,"version":"3.51.4"},"reference-count":32,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2019,12,7]],"date-time":"2019-12-07T00:00:00Z","timestamp":1575676800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2019,12,7]],"date-time":"2019-12-07T00:00:00Z","timestamp":1575676800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["SN COMPUT. SCI."],"published-print":{"date-parts":[[2020,1]]},"DOI":"10.1007\/s42979-019-0051-7","type":"journal-article","created":{"date-parts":[[2019,12,7]],"date-time":"2019-12-07T02:02:24Z","timestamp":1575684144000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Discrete-Event Simulation-Based Q-Learning Algorithm Applied to Financial Leverage Effect"],"prefix":"10.1007","volume":"1","author":[{"given":"E.","family":"Barbieri","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0793-8742","authenticated-orcid":false,"given":"L.","family":"Capocchi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"J. F","family":"Santucci","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,12,7]]},"reference":[{"key":"51_CR1","doi-asserted-by":"publisher","unstructured":"Barbieri E, Capocchi L, Santucci J. Devs modeling and simulation based on markov decision process of financial leverage effect in the eu development programs. In: 2017 Winter simulation conference (WSC), 2017; pp 4558\u20134560. https:\/\/doi.org\/10.1109\/WSC.2017.8248203","DOI":"10.1109\/WSC.2017.8248203"},{"key":"51_CR2","volume-title":"Theory of modeling and simulation","author":"P Bernard","year":"2000","unstructured":"Bernard P, Zeigler HP, Kim TG. Theory of modeling and simulation. 2nd ed. Cambridge: Academic Press; 2000.","edition":"2"},{"key":"51_CR3","volume-title":"Dynamic programming: deterministic and stochastic models","author":"DP Bertsekas","year":"1987","unstructured":"Bertsekas DP. Dynamic programming: deterministic and stochastic models. Upper Saddle River: Prentice-Hall Inc; 1987."},{"issue":"6","key":"51_CR4","doi-asserted-by":"publisher","first-page":"901","DOI":"10.1007\/s10732-019-09408-x","volume":"25","author":"S Bromuri","year":"2019","unstructured":"Bromuri S. Dynamic heuristic acceleration of linearly approximated sarsa($$\\lambda$$): using ant colony optimization to learn heuristics dynamically. J Heuristic. 2019;25(6):901\u201332. https:\/\/doi.org\/10.1007\/s10732-019-09408-x.","journal-title":"J Heuristic"},{"key":"51_CR5","doi-asserted-by":"publisher","unstructured":"Capocchi L, Santucci JF, Poggi B, Nicolai C. DEVSimPy: A collaborative python software for modeling and simulation of DEVS systems. In: Proceedings of 20th IEEE international workshops on enabling technologies, 2011; pp 170\u2013175. https:\/\/doi.org\/10.1109\/WETICE.2011.31","DOI":"10.1109\/WETICE.2011.31"},{"key":"51_CR6","unstructured":"Even-Dar E, Mansour Y. Learning rates for q-learning. J Mach Learn Res 2004; 5:1\u201325. http:\/\/dl.acm.org\/citation.cfm?id=1005332.1005333"},{"key":"51_CR7","unstructured":"Floyd MW, Wainer GA. Creation of devs models using imitation learning. In: Proceedings of the 2010 summer computer simulation conference, society for computer simulation international, San Diego, CA, USA, SCSC \u201910, 2010; pp. 334\u2013341. http:\/\/dl.acm.org\/citation.cfm?id=1999416.1999459"},{"key":"51_CR8","doi-asserted-by":"publisher","first-page":"280","DOI":"10.1007\/978-3-662-49381-6_27","volume-title":"Intelligent information and database systems","author":"S Hayashi","year":"2016","unstructured":"Hayashi S, Prasasti N, Kanamori K, Ohwada H. Improving behavior prediction accuracy by using machine learning for agent-based simulation. In: Nguyen NT, Trawi\u0144ski B, Fujita H, Hong TP, editors. Intelligent information and database systems. Heidelberg: Springer; 2016. p. 280\u20139."},{"key":"51_CR9","doi-asserted-by":"publisher","unstructured":"Huang W, Nakamori Y, Wang SY. Forecasting stock market movement direction with support vector machine. Comput Oper Res. 2005;32(10):2513\u201322. https:\/\/doi.org\/10.1016\/j.cor.2004.03.016. http:\/\/www.sciencedirect.com\/science\/article\/pii\/S0305054804000681, applications of Neural Networks.","DOI":"10.1016\/j.cor.2004.03.016"},{"key":"51_CR10","doi-asserted-by":"publisher","unstructured":"Kazem A, Sharifi E, Hussain FK, Saberi M, Hussain OK. Support vector regression with chaos-based firefly algorithm for stock market price forecasting. Applied Soft Computing. 2013;13(2):947\u201358. https:\/\/doi.org\/10.1016\/j.asoc.2012.09.024. http:\/\/www.sciencedirect.com\/science\/article\/pii\/S1568494612004449.","DOI":"10.1016\/j.asoc.2012.09.024"},{"key":"51_CR11","doi-asserted-by":"crossref","unstructured":"Lake BM, Ullman TD, Tenenbaum JB, Gershman SJ. Building machines that learn and think like people. 2016; CoRR arXiv:1604.00289","DOI":"10.1017\/S0140525X16001837"},{"key":"51_CR12","doi-asserted-by":"publisher","unstructured":"Lin W, Hu Y, Tsai C. Machine learning in financial crisis prediction: a survey. IEEE Trans Syst Man Cybern Part C (Appl Rev) 2012;42(4):421\u2013436. https:\/\/doi.org\/10.1109\/TSMCC.2011.2170420","DOI":"10.1109\/TSMCC.2011.2170420"},{"key":"51_CR13","doi-asserted-by":"publisher","unstructured":"Meraji S, Tropper C. A machine learning approach for optimizing parallel logic simulation. In: 2010 39th International Conference on Parallel Processing, 2010; pp 545\u2013554. https:\/\/doi.org\/10.1109\/ICPP.2010.62","DOI":"10.1109\/ICPP.2010.62"},{"issue":"6","key":"51_CR14","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1109\/MPOT.2017.2737200","volume":"36","author":"JM Mulvey","year":"2017","unstructured":"Mulvey JM. Machine learning and financial planning. IEEE Potential. 2017;36(6):8\u201313. https:\/\/doi.org\/10.1109\/MPOT.2017.2737200.","journal-title":"IEEE Potential"},{"key":"51_CR15","doi-asserted-by":"publisher","unstructured":"Nielsen NR. Application of artificial intelligence techniques to simulation. Springer, New York, 1991; pp 1\u201319. https:\/\/doi.org\/10.1007\/978-1-4612-3040-3_1","DOI":"10.1007\/978-1-4612-3040-3_1"},{"key":"51_CR16","doi-asserted-by":"publisher","unstructured":"Patel J, Shah S, Thakkar P, Kotecha K. Predicting stock and stock price index movement using trend deterministic data preparation and machine learning techniques. Exp Syst Appl. 2015;42(1):259\u201368. https:\/\/doi.org\/10.1016\/j.eswa.2014.07.040. http:\/\/www.sciencedirect.com\/science\/article\/pii\/S0957417414004473.","DOI":"10.1016\/j.eswa.2014.07.040"},{"key":"51_CR17","doi-asserted-by":"publisher","DOI":"10.1002\/9780470316887","volume-title":"Markov Decision processes: discrete stochastic dynamic programming","author":"ML Puterman","year":"1994","unstructured":"Puterman ML. Markov Decision processes: discrete stochastic dynamic programming. 1st ed. New York: Wiley; 1994.","edition":"1"},{"key":"51_CR18","unstructured":"Rachelson E, Quesnel G, Garcia F, Fabiani P. A simulation-based approach for solving generalized semi-markov decision processes. In: Proceedings of the 2008 Conference on ECAI 2008: 18th European Conference on Artificial Intelligence, IOS Press, Amsterdam, 2008; pp 583\u2013587. http:\/\/dl.acm.org\/citation.cfm?id=1567281.1567408."},{"key":"51_CR19","volume-title":"Artificial intelligence: a modern approach","author":"S Russell","year":"2009","unstructured":"Russell S, Norvig P. Artificial intelligence: a modern approach. 3rd ed. Upper Saddle River: Prentice Hall Press; 2009.","edition":"3"},{"key":"51_CR20","doi-asserted-by":"publisher","unstructured":"Saadawi H, Wainer G, Pliego G. Devs execution acceleration with machine learning. In: 2016 Symposium on Theory of Modeling and Simulation (TMS-DEVS), 2016; pp 1\u20136. https:\/\/doi.org\/10.23919\/TMS.2016.7918816","DOI":"10.23919\/TMS.2016.7918816"},{"key":"51_CR21","unstructured":"Seo C, Zeigler BP, Kim D. Devs markov modeling and simulation: formal definition and implementation. In: Proceedings of the theory of modeling and simulation symposium, society for computer simulation international, San Diego, TMS \u201918, 2018; pp 1:1\u20131:12. http:\/\/dl.acm.org\/citation.cfm?id=3213187.3213188"},{"key":"51_CR22","volume-title":"Introduction to reinforcement learning","author":"RS Sutton","year":"1998","unstructured":"Sutton RS, Barto AG. Introduction to reinforcement learning. 1st ed. Cambridge: MIT Press; 1998.","edition":"1"},{"key":"51_CR23","unstructured":"Toma S. Detection and identication methodology for multiple faults in complex systems using discrete-events and neural networks: applied to the wind turbines diagnosis. Theses, University of Corsica, 2014a. https:\/\/hal.archives-ouvertes.fr\/tel-01141844"},{"key":"51_CR24","unstructured":"Toma S. Detection methodology and identify multiple faults in complex systems from discrete events and neural networks: applications for wind turbines. Theses, Universit\u00e9 Pascal Paoli, 2014b. https:\/\/tel.archives-ouvertes.fr\/tel-01127073"},{"key":"51_CR25","unstructured":"Van\u00a0Tendeloo Y, Vangheluwe H. The modular architecture of the Python(P)DEVS simulation kernel (WIP). In: Proceedings of the Symposium on Theory of Modeling & Simulation - DEVS Integrative, Society for Computer Simulation International, San Diego, DEVS \u201914, 2014; pp 14:1\u201314:6. http:\/\/dl.acm.org\/citation.cfm?id=2665008.2665022"},{"key":"51_CR26","doi-asserted-by":"publisher","unstructured":"Wallis L, Paich M. Integrating artifical intelligence with anylogic simulation. In: 2017 Winter Simulation Conference (WSC), 2017; pp 4449\u20134449. https:\/\/doi.org\/10.1109\/WSC.2017.8248156","DOI":"10.1109\/WSC.2017.8248156"},{"key":"51_CR27","unstructured":"Watkins CJCH. Learning from delayed rewards. PhD thesis, King\u2019s College, Cambridge, 1989."},{"key":"51_CR28","doi-asserted-by":"publisher","unstructured":"Yeh CY, Huang CW, Lee SJ. A multiple-kernel support vector regression approach for stock market price forecasting. Exp Syst Appl. 2011;38(3):2177\u201386. https:\/\/doi.org\/10.1016\/j.eswa.2010.08.004, http:\/\/www.sciencedirect.com\/science\/article\/pii\/S0957417410007876.","DOI":"10.1016\/j.eswa.2010.08.004"},{"key":"51_CR29","doi-asserted-by":"publisher","unstructured":"Yu H, Mahmood AR, Sutton RS (2017) On generalized bellman equations and temporal-difference learning. In: Advances in Artificial Intelligence - 30th Canadian Conference on Artificial Intelligence, Canadian AI 2017, Edmonton, AB, Canada, May 16\u201319, 2017, Proceedings, pp 3\u201314. https:\/\/doi.org\/10.1007\/978-3-319-57351-9_1","DOI":"10.1007\/978-3-319-57351-9_1"},{"key":"51_CR30","volume-title":"Theory of modeling and simulation","author":"BP Zeigler","year":"1976","unstructured":"Zeigler BP. Theory of modeling and simulation. Cambridge: Academic Press; 1976."},{"key":"51_CR31","first-page":"27","volume-title":"Guide to modeling and simulation of systems, simulation foundations, methods and applications","author":"BP Zeigler","year":"2013","unstructured":"Zeigler BP, Sarjoughian HS. System entity structure basics. Guide to modeling and simulation of systems, simulation foundations, methods and applications. London: Springer; 2013. p. 27\u201337."},{"key":"51_CR32","doi-asserted-by":"publisher","unstructured":"Zeigler BP, Muzy A, Kofman E. Theory of Modeling and Simulation, third edition. Academic Press, New York 2019. https:\/\/doi.org\/10.1016\/B978-0-12-813370-5.00003-1","DOI":"10.1016\/B978-0-12-813370-5.00003-1"}],"container-title":["SN Computer Science"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-019-0051-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s42979-019-0051-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-019-0051-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,12,6]],"date-time":"2020-12-06T00:34:34Z","timestamp":1607214874000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s42979-019-0051-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,12,7]]},"references-count":32,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2020,1]]}},"alternative-id":["51"],"URL":"https:\/\/doi.org\/10.1007\/s42979-019-0051-7","relation":{},"ISSN":["2662-995X","2661-8907"],"issn-type":[{"value":"2662-995X","type":"print"},{"value":"2661-8907","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,12,7]]},"assertion":[{"value":"13 September 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 November 2019","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 December 2019","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"50"}}