{"id":"https://openalex.org/W2018053811","doi":"https://doi.org/10.1145/1774088.1774585","title":"Modeling POMDPs for generating and simulating stock investment policies","display_name":"Modeling POMDPs for generating and simulating stock investment policies","publication_year":2010,"publication_date":"2010-03-22","ids":{"openalex":"https://openalex.org/W2018053811","doi":"https://doi.org/10.1145/1774088.1774585","mag":"2018053811"},"language":"en","primary_location":{"id":"doi:10.1145/1774088.1774585","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1774088.1774585","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2010 ACM Symposium on Applied Computing","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5042152619","display_name":"Augusto Baffa","orcid":"https://orcid.org/0000-0002-7039-4477"},"institutions":[{"id":"https://openalex.org/I83648350","display_name":"Universidade Federal do Estado do Rio de Janeiro","ror":"https://ror.org/04tec8z30","country_code":"BR","type":"education","lineage":["https://openalex.org/I83648350"]}],"countries":["BR"],"is_corresponding":true,"raw_author_name":"Augusto Cesar Esp\u00edndola Baffa","raw_affiliation_strings":["UNIRIO, T\u00e9rreo, Rio de Janeiro - Brazil"],"affiliations":[{"raw_affiliation_string":"UNIRIO, T\u00e9rreo, Rio de Janeiro - Brazil","institution_ids":["https://openalex.org/I83648350"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5045201515","display_name":"Angelo E. M. Ciarlini","orcid":null},"institutions":[{"id":"https://openalex.org/I83648350","display_name":"Universidade Federal do Estado do Rio de Janeiro","ror":"https://ror.org/04tec8z30","country_code":"BR","type":"education","lineage":["https://openalex.org/I83648350"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Angelo E. M. Ciarlini","raw_affiliation_strings":["UNIRIO, T\u00e9rreo, Rio de Janeiro - Brazil"],"affiliations":[{"raw_affiliation_string":"UNIRIO, T\u00e9rreo, Rio de Janeiro - Brazil","institution_ids":["https://openalex.org/I83648350"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5042152619"],"corresponding_institution_ids":["https://openalex.org/I83648350"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.09341859,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"2394","last_page":"2399"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10462","display_name":"Reinforcement Learning in Robotics","score":0.9916999936103821,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10462","display_name":"Reinforcement Learning in Robotics","score":0.9916999936103821,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11195","display_name":"Simulation Techniques and Applications","score":0.9829999804496765,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.9810000061988831,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7341855764389038},{"id":"https://openalex.org/keywords/technical-analysis","display_name":"Technical analysis","score":0.6250933408737183},{"id":"https://openalex.org/keywords/investment-decisions","display_name":"Investment decisions","score":0.5409963130950928},{"id":"https://openalex.org/keywords/operations-research","display_name":"Operations research","score":0.5376533269882202},{"id":"https://openalex.org/keywords/profit","display_name":"Profit (economics)","score":0.5213088393211365},{"id":"https://openalex.org/keywords/stock","display_name":"Stock (firearms)","score":0.49310967326164246},{"id":"https://openalex.org/keywords/markov-decision-process","display_name":"Markov decision process","score":0.48818737268447876},{"id":"https://openalex.org/keywords/flexibility","display_name":"Flexibility (engineering)","score":0.4574129283428192},{"id":"https://openalex.org/keywords/investment","display_name":"Investment (military)","score":0.4506445825099945},{"id":"https://openalex.org/keywords/partially-observable-markov-decision-process","display_name":"Partially observable Markov decision process","score":0.4248770773410797},{"id":"https://openalex.org/keywords/chart","display_name":"Chart","score":0.4140913486480713},{"id":"https://openalex.org/keywords/markov-chain","display_name":"Markov chain","score":0.3975098729133606},{"id":"https://openalex.org/keywords/markov-process","display_name":"Markov process","score":0.3551340699195862},{"id":"https://openalex.org/keywords/economics","display_name":"Economics","score":0.25882625579833984},{"id":"https://openalex.org/keywords/markov-model","display_name":"Markov model","score":0.24535015225410461},{"id":"https://openalex.org/keywords/microeconomics","display_name":"Microeconomics","score":0.18668192625045776},{"id":"https://openalex.org/keywords/finance","display_name":"Finance","score":0.18407469987869263},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.1257019340991974},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.09610557556152344}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7341855764389038},{"id":"https://openalex.org/C117245426","wikidata":"https://www.wikidata.org/wiki/Q235038","display_name":"Technical analysis","level":2,"score":0.6250933408737183},{"id":"https://openalex.org/C2778865806","wikidata":"https://www.wikidata.org/wiki/Q6060850","display_name":"Investment decisions","level":3,"score":0.5409963130950928},{"id":"https://openalex.org/C42475967","wikidata":"https://www.wikidata.org/wiki/Q194292","display_name":"Operations research","level":1,"score":0.5376533269882202},{"id":"https://openalex.org/C181622380","wikidata":"https://www.wikidata.org/wiki/Q26911","display_name":"Profit (economics)","level":2,"score":0.5213088393211365},{"id":"https://openalex.org/C204036174","wikidata":"https://www.wikidata.org/wiki/Q909380","display_name":"Stock (firearms)","level":2,"score":0.49310967326164246},{"id":"https://openalex.org/C106189395","wikidata":"https://www.wikidata.org/wiki/Q176789","display_name":"Markov decision process","level":3,"score":0.48818737268447876},{"id":"https://openalex.org/C2780598303","wikidata":"https://www.wikidata.org/wiki/Q65921492","display_name":"Flexibility (engineering)","level":2,"score":0.4574129283428192},{"id":"https://openalex.org/C27548731","wikidata":"https://www.wikidata.org/wiki/Q88272","display_name":"Investment (military)","level":3,"score":0.4506445825099945},{"id":"https://openalex.org/C17098449","wikidata":"https://www.wikidata.org/wiki/Q176814","display_name":"Partially observable Markov decision process","level":4,"score":0.4248770773410797},{"id":"https://openalex.org/C190812933","wikidata":"https://www.wikidata.org/wiki/Q28923","display_name":"Chart","level":2,"score":0.4140913486480713},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.3975098729133606},{"id":"https://openalex.org/C159886148","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov process","level":2,"score":0.3551340699195862},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.25882625579833984},{"id":"https://openalex.org/C163836022","wikidata":"https://www.wikidata.org/wiki/Q6771326","display_name":"Markov model","level":3,"score":0.24535015225410461},{"id":"https://openalex.org/C175444787","wikidata":"https://www.wikidata.org/wiki/Q39072","display_name":"Microeconomics","level":1,"score":0.18668192625045776},{"id":"https://openalex.org/C10138342","wikidata":"https://www.wikidata.org/wiki/Q43015","display_name":"Finance","level":1,"score":0.18407469987869263},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.1257019340991974},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.09610557556152344},{"id":"https://openalex.org/C2778348673","wikidata":"https://www.wikidata.org/wiki/Q739302","display_name":"Production (economics)","level":2,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C78519656","wikidata":"https://www.wikidata.org/wiki/Q101333","display_name":"Mechanical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/1774088.1774585","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1774088.1774585","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2010 ACM Symposium on Applied Computing","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.455.3419","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.455.3419","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.webscience.org.br/wiki/images/0/02/Baffa_ciarlini_sac_2010.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16","score":0.6100000143051147}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W56161077","https://openalex.org/W93851859","https://openalex.org/W118422903","https://openalex.org/W125541279","https://openalex.org/W180325379","https://openalex.org/W1480714584","https://openalex.org/W1532688806","https://openalex.org/W1571851576","https://openalex.org/W1898724844","https://openalex.org/W1946238955","https://openalex.org/W2099430963","https://openalex.org/W2099873296","https://openalex.org/W2119567691","https://openalex.org/W2130364193","https://openalex.org/W2134802714","https://openalex.org/W2187595073","https://openalex.org/W2189613824","https://openalex.org/W2334782222","https://openalex.org/W2341171179","https://openalex.org/W4205241946","https://openalex.org/W4256238177"],"related_works":["https://openalex.org/W2096013579","https://openalex.org/W52153049","https://openalex.org/W1760611253","https://openalex.org/W1589140671","https://openalex.org/W1515117609","https://openalex.org/W4323315247","https://openalex.org/W2294884454","https://openalex.org/W3169161914","https://openalex.org/W4321379664","https://openalex.org/W2211790881"],"abstract_inverted_index":{"Analysts":[0],"and":[1,10,21,26,38,52,102,134,184],"investors":[2],"use":[3],"Technical":[4,110,142,157],"Analysis":[5,111,143,158],"tools":[6,44],"to":[7,48,78,149,153,168,173],"create":[8,169],"charts":[9],"price":[11],"indicators":[12,22],"that":[13,45,92,118,171],"help":[14],"them":[15,58],"in":[16,95],"decision":[17,66],"making.":[18,67],"Chart":[19],"patterns":[20],"are":[23,147,161],"not":[24],"deterministic":[25],"even":[27],"analysts":[28],"may":[29],"have":[30,76],"different":[31,186],"interpretations,":[32],"depending":[33],"on":[34,57],"their":[35],"experience,":[36],"background":[37],"emotional":[39],"state.":[40],"In":[41,68,113],"this":[42,69],"way,":[43],"allow":[46],"users":[47],"formalize":[49],"these":[50],"concepts":[51],"study":[53],"investment":[54,82,93],"policies":[55,94,170],"based":[56],"can":[59,99,135],"provide":[60,150],"a":[61,73,131],"more":[62],"solid":[63],"basis":[64],"for":[65,121,182],"paper,":[70],"we":[71,75,116],"present":[72],"tool":[74,178],"built":[77],"formally":[79],"model":[80],"stock":[81,97],"contexts":[83],"as":[84],"Partially":[85],"Observable":[86],"Markov":[87],"Decision":[88],"Processes":[89],"(POMDP),":[90],"so":[91],"the":[96,107,119,122,128,154,175],"market":[98],"be":[100,136],"generated":[101],"simulated,":[103],"taking":[104],"into":[105],"consideration":[106],"accuracy":[108,155],"of":[109,127,141,156],"techniques.":[112,144],"our":[114],"models,":[115],"assume":[117],"trend":[120],"future":[123],"prices":[124],"is":[125],"part":[126],"state":[129],"at":[130],"certain":[132],"time":[133],"\"partially":[137],"observed\"":[138],"by":[139,163],"means":[140],"Historical":[145],"series":[146],"used":[148,162],"probabilities":[151],"related":[152],"techniques,":[159],"which":[160],"an":[164],"automated":[165],"planning":[166],"algorithm":[167],"try":[172],"maximize":[174],"profit.":[176],"The":[177],"also":[179],"provides":[180],"flexibility":[181],"trying":[183],"comparing":[185],"models.":[187]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2017,"cited_by_count":2},{"year":2016,"cited_by_count":2},{"year":2015,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
