{"id":"https://openalex.org/W3034571736","doi":"https://doi.org/10.24963/ijcai.2020/638","title":"Market Manipulation: An Adversarial Learning Framework for Detection and Evasion","display_name":"Market Manipulation: An Adversarial Learning Framework for Detection and Evasion","publication_year":2020,"publication_date":"2020-07-01","ids":{"openalex":"https://openalex.org/W3034571736","doi":"https://doi.org/10.24963/ijcai.2020/638","mag":"3034571736"},"language":"en","primary_location":{"id":"doi:10.24963/ijcai.2020/638","doi":"https://doi.org/10.24963/ijcai.2020/638","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2020/638","pdf_url":"https://www.ijcai.org/proceedings/2020/0638.pdf","source":{"id":"https://openalex.org/S4306419999","display_name":"International Joint Conference on Artificial Intelligence","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","is_traditional":true,"indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"bronze","oa_url":"https://www.ijcai.org/proceedings/2020/0638.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100783580","display_name":"Xintong Wang","orcid":"https://orcid.org/0000-0002-0867-8807","observed_orcids":["https://orcid.org/0000-0002-0867-8807"]},"institutions":[{"id":"https://openalex.org/I27837315","display_name":"University of Michigan","ror":"https://ror.org/00jmfr291","country_code":"US","type":"education","lineage":["https://openalex.org/I27837315"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xintong Wang","raw_affiliation_strings":["University of Michigan","University of Michigan, Ann Arbor"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Michigan","institution_ids":["https://openalex.org/I27837315"]},{"raw_affiliation_string":"University of Michigan, Ann Arbor","institution_ids":["https://openalex.org/I27837315"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5002102744","display_name":"Michael P. Wellman","orcid":"https://orcid.org/0000-0002-1691-6844","observed_orcids":["https://orcid.org/0000-0002-1691-6844"]},"institutions":[{"id":"https://openalex.org/I27837315","display_name":"University of Michigan","ror":"https://ror.org/00jmfr291","country_code":"US","type":"education","lineage":["https://openalex.org/I27837315"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Michael P. Wellman","raw_affiliation_strings":["University of Michigan","University of Michigan, Ann Arbor"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Michigan","institution_ids":["https://openalex.org/I27837315"]},{"raw_affiliation_string":"University of Michigan, Ann Arbor","institution_ids":["https://openalex.org/I27837315"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I27837315"],"apc_list":null,"apc_paid":null,"fwci":2.663,"has_fulltext":true,"cited_by_count":14,"citation_normalized_percentile":{"value":0.91057018,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":88,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"4626","last_page":"4632"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.4171999990940094,"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"}},"topics":[{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.4171999990940094,"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/T10047","display_name":"Financial Markets and Investment Strategies","score":0.3882000148296356,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.0731000006198883,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/market-manipulation","display_name":"market manipulation","score":0.9419999718666077},{"id":"https://openalex.org/keywords/manipulation-detection","display_name":"manipulation detection","score":0.9200000166893005},{"id":"https://openalex.org/keywords/evasion","display_name":"evasion","score":0.6370000243186951},{"id":"https://openalex.org/keywords/adversarial-machine-learning","display_name":"adversarial learning","score":0.9570000171661377},{"id":"https://openalex.org/keywords/generative-adversarial-networks","display_name":"generative adversarial networks","score":0.8270000219345093},{"id":"https://openalex.org/keywords/market-making","display_name":"market making","score":0.6620000004768372},{"id":"https://openalex.org/keywords/trade-patterns","display_name":"trade patterns","score":0.5289999842643738},{"id":"https://openalex.org/keywords/financial-fraud-detection","display_name":"financial fraud detection","score":0.3790000081062317},{"id":"https://openalex.org/keywords/agent-based-modeling","display_name":"agent-based modeling","score":0.5070000290870667}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7720043063163757},{"id":"https://openalex.org/C2780992000","wikidata":"https://www.wikidata.org/wiki/Q17016113","display_name":"Generator (circuit theory)","level":3,"score":0.7360219955444336},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.6776504516601562},{"id":"https://openalex.org/C2779721758","wikidata":"https://www.wikidata.org/wiki/Q400707","display_name":"Market manipulation","level":2,"score":0.668262779712677},{"id":"https://openalex.org/C2781251061","wikidata":"https://www.wikidata.org/wiki/Q5416089","display_name":"Evasion (ethics)","level":3,"score":0.6335289478302002},{"id":"https://openalex.org/C2779803651","wikidata":"https://www.wikidata.org/wiki/Q5282088","display_name":"Discriminator","level":3,"score":0.6145578026771545},{"id":"https://openalex.org/C182306322","wikidata":"https://www.wikidata.org/wiki/Q1779371","display_name":"Order (exchange)","level":2,"score":0.5448616743087769},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5105994343757629},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4766198694705963},{"id":"https://openalex.org/C2775924081","wikidata":"https://www.wikidata.org/wiki/Q55608371","display_name":"Control (management)","level":2,"score":0.46157655119895935},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.37956589460372925},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.07807591557502747},{"id":"https://openalex.org/C8891405","wikidata":"https://www.wikidata.org/wiki/Q1059","display_name":"Immune system","level":2,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C203014093","wikidata":"https://www.wikidata.org/wiki/Q101929","display_name":"Immunology","level":1,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C10138342","wikidata":"https://www.wikidata.org/wiki/Q43015","display_name":"Finance","level":1,"score":0.0},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.24963/ijcai.2020/638","doi":"https://doi.org/10.24963/ijcai.2020/638","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2020/638","pdf_url":"https://www.ijcai.org/proceedings/2020/0638.pdf","source":{"id":"https://openalex.org/S4306419999","display_name":"International Joint Conference on Artificial Intelligence","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.24963/ijcai.2020/638","doi":"https://doi.org/10.24963/ijcai.2020/638","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2020/638","pdf_url":"https://www.ijcai.org/proceedings/2020/0638.pdf","source":{"id":"https://openalex.org/S4306419999","display_name":"International Joint Conference on Artificial Intelligence","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"},"sustainable_development_goals":[],"study_designs":[],"awards":[{"id":"https://openalex.org/G6809653480","display_name":"BIGDATA: IA: Collaborative Research:  Detecting Financial Market Manipulation: An Integrated Data- and Model-Driven Approach","funder_award_id":"1741190","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3034571736.pdf","grobid_xml":"https://content.openalex.org/works/W3034571736.grobid-xml"},"referenced_works_count":20,"referenced_works":["https://openalex.org/W1754742701","https://openalex.org/W1945616565","https://openalex.org/W2095577883","https://openalex.org/W2130942839","https://openalex.org/W2157331557","https://openalex.org/W2567101557","https://openalex.org/W2593768305","https://openalex.org/W2629647641","https://openalex.org/W2767075075","https://openalex.org/W2808100210","https://openalex.org/W2891348164","https://openalex.org/W2902634305","https://openalex.org/W2907584873","https://openalex.org/W2962935454","https://openalex.org/W2963207607","https://openalex.org/W2987434780","https://openalex.org/W3033666611","https://openalex.org/W3123038668","https://openalex.org/W3125817955","https://openalex.org/W4320013936"],"related_works":["https://openalex.org/W4409869035","https://openalex.org/W2913933677","https://openalex.org/W3119507137","https://openalex.org/W4393063518","https://openalex.org/W7163915273","https://openalex.org/W6929405988","https://openalex.org/W3084814978","https://openalex.org/W3214540909","https://openalex.org/W6964135305","https://openalex.org/W3092913532"],"abstract_inverted_index":{"We":[0,88,104],"propose":[1],"an":[2,77],"adversarial":[3],"learning":[4],"framework":[5],"to":[6,17,27,41,47],"capture":[7],"the":[8,57,65,82,125,132,137,152],"evolving":[9],"game":[10],"between":[11],"a":[12,22,37,52,61,97,100,114,142],"regulator":[13],"who":[14,24],"develops":[15],"tools":[16],"detect":[18],"market":[19,116],"manipulation":[20,44,58,68,83,109,127,147],"and":[21,75,99,120],"manipulator":[23,98],"obfuscates":[25],"actions":[26],"evade":[28],"detection.":[29],"The":[30],"model":[31],"includes":[32],"three":[33],"main":[34],"parts:":[35],"(1)":[36],"generator":[38],"that":[39,63,80,112,149],"learns":[40],"adapt":[42],"original":[43,126],"order":[45,69,93,110],"streams":[46,70,94,111],"resemble":[48],"trading":[49,73],"patterns":[50,119],"of":[51,85,107,139,145,154],"normal":[53,72],"trader":[54],"while":[55],"preserving":[56],"intent;":[59],"(2)":[60],"discriminator":[62],"differentiates":[64],"adversarially":[66],"adapted":[67,86,108],"from":[71,124],"activities;":[74],"(3)":[76],"agent-based":[78],"simulator":[79],"evaluates":[81],"effect":[84],"outputs.":[87],"conduct":[89],"experiments":[90],"on":[91],"simulated":[92],"associated":[95],"with":[96],"market-making":[101],"agent":[102],"respectively.":[103],"show":[105],"examples":[106],"mimic":[113],"specified":[115],"maker's":[117],"quoting":[118],"appear":[121],"qualitatively":[122],"different":[123],"strategy":[128],"we":[129],"implemented":[130],"in":[131],"simulator.":[133],"These":[134],"results":[135],"demonstrate":[136],"possibility":[138],"automatically":[140],"generating":[141],"diverse":[143],"set":[144],"(unseen)":[146],"strategies":[148],"can":[150],"facilitate":[151],"training":[153],"more":[155],"robust":[156],"detection":[157],"algorithms.":[158]},"counts_by_year":[{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1}],"updated_date":"2026-11-04T10:54:05.112534","created_date":"2020-06-19T00:00:00"}