{"id":"https://openalex.org/W4226205224","doi":"https://doi.org/10.1109/cifer52523.2022.9776121","title":"High-Dimensional Stock Portfolio Trading with Deep Reinforcement Learning","display_name":"High-Dimensional Stock Portfolio Trading with Deep Reinforcement Learning","publication_year":2022,"publication_date":"2022-05-01","ids":{"openalex":"https://openalex.org/W4226205224","doi":"https://doi.org/10.1109/cifer52523.2022.9776121"},"language":"en","primary_location":{"id":"doi:10.1109/cifer52523.2022.9776121","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cifer52523.2022.9776121","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE Symposium on Computational Intelligence for Financial Engineering and Economics (CIFEr)","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/A5069123428","display_name":"Uta Pigorsch","orcid":"https://orcid.org/0009-0003-0137-1110"},"institutions":[{"id":"https://openalex.org/I167360494","display_name":"University of Wuppertal","ror":"https://ror.org/00613ak93","country_code":"DE","type":"education","lineage":["https://openalex.org/I167360494"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Uta Pigorsch","raw_affiliation_strings":["University of Wuppertal,Schumpeter School of Business and Economics,Wuppertal,Germany","Schumpeter School of Business and Economics, University of Wuppertal, Wuppertal, Germany"],"affiliations":[{"raw_affiliation_string":"University of Wuppertal,Schumpeter School of Business and Economics,Wuppertal,Germany","institution_ids":["https://openalex.org/I167360494"]},{"raw_affiliation_string":"Schumpeter School of Business and Economics, University of Wuppertal, Wuppertal, Germany","institution_ids":["https://openalex.org/I167360494"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101826692","display_name":"Sebastian Sch\u00e4fer","orcid":null},"institutions":[{"id":"https://openalex.org/I167360494","display_name":"University of Wuppertal","ror":"https://ror.org/00613ak93","country_code":"DE","type":"education","lineage":["https://openalex.org/I167360494"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Sebastian Schafer","raw_affiliation_strings":["University of Wuppertal,Schumpeter School of Business and Economics,Wuppertal,Germany","Schumpeter School of Business and Economics, University of Wuppertal, Wuppertal, Germany"],"affiliations":[{"raw_affiliation_string":"University of Wuppertal,Schumpeter School of Business and Economics,Wuppertal,Germany","institution_ids":["https://openalex.org/I167360494"]},{"raw_affiliation_string":"Schumpeter School of Business and Economics, University of Wuppertal, Wuppertal, Germany","institution_ids":["https://openalex.org/I167360494"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5069123428"],"corresponding_institution_ids":["https://openalex.org/I167360494"],"apc_list":null,"apc_paid":null,"fwci":1.6729,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.84422111,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10047","display_name":"Financial Markets and Investment Strategies","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/2003","display_name":"Finance"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T10047","display_name":"Financial Markets and Investment Strategies","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/2003","display_name":"Finance"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.9998000264167786,"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/T11270","display_name":"Complex Systems and Time Series Analysis","score":0.9932000041007996,"subfield":{"id":"https://openalex.org/subfields/2002","display_name":"Economics and Econometrics"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.6752001643180847},{"id":"https://openalex.org/keywords/portfolio","display_name":"Portfolio","score":0.6220269203186035},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6147195100784302},{"id":"https://openalex.org/keywords/asset-allocation","display_name":"Asset allocation","score":0.46756061911582947},{"id":"https://openalex.org/keywords/portfolio-optimization","display_name":"Portfolio optimization","score":0.4592890441417694},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.4325462579727173},{"id":"https://openalex.org/keywords/margin","display_name":"Margin (machine learning)","score":0.42021432518959045},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3841296434402466},{"id":"https://openalex.org/keywords/finance","display_name":"Finance","score":0.3050716519355774},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.26802945137023926},{"id":"https://openalex.org/keywords/economics","display_name":"Economics","score":0.18157222867012024}],"concepts":[{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.6752001643180847},{"id":"https://openalex.org/C2780821815","wikidata":"https://www.wikidata.org/wiki/Q5340806","display_name":"Portfolio","level":2,"score":0.6220269203186035},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6147195100784302},{"id":"https://openalex.org/C139819358","wikidata":"https://www.wikidata.org/wiki/Q462748","display_name":"Asset allocation","level":3,"score":0.46756061911582947},{"id":"https://openalex.org/C202655437","wikidata":"https://www.wikidata.org/wiki/Q7231728","display_name":"Portfolio optimization","level":3,"score":0.4592890441417694},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.4325462579727173},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.42021432518959045},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3841296434402466},{"id":"https://openalex.org/C10138342","wikidata":"https://www.wikidata.org/wiki/Q43015","display_name":"Finance","level":1,"score":0.3050716519355774},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.26802945137023926},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.18157222867012024}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/cifer52523.2022.9776121","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cifer52523.2022.9776121","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE Symposium on Computational Intelligence for Financial Engineering and Economics (CIFEr)","raw_type":"proceedings-article"},{"id":"pmh:oai:RePEc:arx:papers:2112.04755","is_oa":false,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4306401271","display_name":"RePEc: Research Papers in Economics","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I77793887","host_organization_name":"Federal Reserve Bank of St. Louis","host_organization_lineage":["https://openalex.org/I77793887"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"preprint"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/17","display_name":"Partnerships for the goals","score":0.47999998927116394}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":36,"referenced_works":["https://openalex.org/W41554520","https://openalex.org/W52153049","https://openalex.org/W1522301498","https://openalex.org/W1797070008","https://openalex.org/W1977145788","https://openalex.org/W2025053102","https://openalex.org/W2163605009","https://openalex.org/W2194775991","https://openalex.org/W2624385633","https://openalex.org/W2625101268","https://openalex.org/W2997497843","https://openalex.org/W3012223895","https://openalex.org/W3024896014","https://openalex.org/W3037648816","https://openalex.org/W3091882082","https://openalex.org/W3121583564","https://openalex.org/W3121933628","https://openalex.org/W3122827811","https://openalex.org/W3126577088","https://openalex.org/W3172840697","https://openalex.org/W3203680104","https://openalex.org/W4205539948","https://openalex.org/W4237239309","https://openalex.org/W4287630192","https://openalex.org/W4289285046","https://openalex.org/W4298857966","https://openalex.org/W6631190155","https://openalex.org/W6637967152","https://openalex.org/W6638183220","https://openalex.org/W6684191040","https://openalex.org/W6740961973","https://openalex.org/W6753199568","https://openalex.org/W6756384632","https://openalex.org/W6772332861","https://openalex.org/W6784869795","https://openalex.org/W6795393705"],"related_works":["https://openalex.org/W3122676037","https://openalex.org/W3126040400","https://openalex.org/W129685485","https://openalex.org/W2316467541","https://openalex.org/W3125179415","https://openalex.org/W4294612823","https://openalex.org/W2350212927","https://openalex.org/W2126494677","https://openalex.org/W3037177608","https://openalex.org/W3149920777"],"abstract_inverted_index":{"This":[0,75],"paper":[1],"proposes":[2],"a":[3,142],"Deep":[4,14],"Reinforcement":[5],"Learning":[6],"algorithm":[7,18,129],"for":[8,52,150],"financial":[9],"portfolio":[10,103],"trading":[11,22],"based":[12],"on":[13,130],"Q-learning":[15],"[1].":[16],"The":[17,128],"is":[19],"capable":[20],"of":[21,28,70,73,109,125],"high-dimensional":[23],"portfolios":[24],"from":[25,111],"cross-sectional":[26],"datasets":[27],"any":[29],"size":[30],"which":[31],"may":[32],"include":[33],"data":[34],"gaps":[35],"and":[36,63,121,136],"non-unique":[37],"history":[38],"lengths":[39],"in":[40,95,106,117,122],"the":[41,59,67,71,77,107,118,123],"assets.":[42,74],"We":[43,91],"sequentially":[44],"set":[45,72],"up":[46,113],"environments":[47],"by":[48,141],"sampling":[49],"one":[50,147],"asset":[51],"each":[53],"environment":[54],"while":[55],"rewarding":[56],"investments":[57],"with":[58,66],"resulting":[60],"asset\u2019s":[61],"return":[62,69],"cash":[64],"reservation":[65],"average":[68,131],"enforces":[76],"agent":[78],"to":[79,83,88,99,114],"strategically":[80],"assign":[81],"capital":[82],"assets":[84],"that":[85],"it":[86],"predicts":[87],"perform":[89],"above-average.":[90],"apply":[92],"our":[93],"methodology":[94],"an":[96],"out-of-sample":[97],"analysis":[98],"48":[100],"US":[101],"stock":[102],"setups,":[104],"varying":[105],"number":[108],"stocks":[110],"ten":[112],"500":[115],"stocks,":[116],"selection":[119],"criteria":[120],"level":[124],"transaction":[126],"costs.":[127],"outperforms":[132],"all":[133,151],"considered":[134],"passive":[135],"active":[137],"benchmark":[138],"investment":[139],"strategies":[140],"large":[143],"margin":[144],"using":[145],"only":[146],"hyperparameter":[148],"setup":[149],"portfolios.":[152]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":3}],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}