{"id":"https://openalex.org/W7083025501","doi":"https://doi.org/10.54364/aaiml.2025.53236","title":"Detecting Social Stock Pumping in the Russian Equity Market Using Machine Learning","display_name":"Detecting Social Stock Pumping in the Russian Equity Market Using Machine Learning","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W7083025501","doi":"https://doi.org/10.54364/aaiml.2025.53236"},"language":"en","primary_location":{"id":"doi:10.54364/aaiml.2025.53236","is_oa":true,"landing_page_url":"https://doi.org/10.54364/aaiml.2025.53236","pdf_url":"https://doi.org/10.54364/aaiml.2025.53236","source":{"id":"https://openalex.org/S4210238872","display_name":"Advances in Artificial Intelligence and Machine Learning","issn_l":"2582-9793","issn":["2582-9793"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Advances in Artificial Intelligence and Machine Learning","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"bronze","oa_url":"https://doi.org/10.54364/aaiml.2025.53236","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Gleb Khaziev","orcid":null},"institutions":[{"id":"https://openalex.org/I118501908","display_name":"National Research University Higher School of Economics","ror":"https://ror.org/055f7t516","country_code":"RU","type":"education","lineage":["https://openalex.org/I118501908"]}],"countries":["RU"],"is_corresponding":true,"raw_author_name":"Gleb Khaziev","raw_affiliation_strings":["Department of Financial Markets Infrastructure , National Research University Higher School of Economics (HSE) , 11 Pokrovsky Bulvar , Moscow , 109028 Russia ,"],"affiliations":[{"raw_affiliation_string":"Department of Financial Markets Infrastructure , National Research University Higher School of Economics (HSE) , 11 Pokrovsky Bulvar , Moscow , 109028 Russia ,","institution_ids":["https://openalex.org/I118501908"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I118501908"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.56082455,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"05","issue":"03","first_page":"4242","last_page":"4259"},"is_retracted":false,"is_paratext":false,"is_xpac":true,"primary_topic":{"id":"https://openalex.org/T14339","display_name":"Image Processing and 3D Reconstruction","score":0.12359999865293503,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T14339","display_name":"Image Processing and 3D Reconstruction","score":0.12359999865293503,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T10421","display_name":"Pleistocene-Era Hominins and Archaeology","score":0.07980000227689743,"subfield":{"id":"https://openalex.org/subfields/3314","display_name":"Anthropology"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10992","display_name":"Forensic Anthropology and Bioarchaeology Studies","score":0.07880000025033951,"subfield":{"id":"https://openalex.org/subfields/1204","display_name":"Archeology"},"field":{"id":"https://openalex.org/fields/12","display_name":"Arts and Humanities"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/stock-market","display_name":"Stock market","score":0.5914999842643738},{"id":"https://openalex.org/keywords/equity","display_name":"Equity (law)","score":0.5094000101089478},{"id":"https://openalex.org/keywords/logistic-regression","display_name":"Logistic regression","score":0.4932999908924103},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.46630001068115234},{"id":"https://openalex.org/keywords/f1-score","display_name":"F1 score","score":0.4408999979496002},{"id":"https://openalex.org/keywords/stock-market-index","display_name":"Stock market index","score":0.424699991941452},{"id":"https://openalex.org/keywords/stock","display_name":"Stock (firearms)","score":0.42100000381469727}],"concepts":[{"id":"https://openalex.org/C2780299701","wikidata":"https://www.wikidata.org/wiki/Q475000","display_name":"Stock market","level":3,"score":0.5914999842643738},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5885999798774719},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5705999732017517},{"id":"https://openalex.org/C199728807","wikidata":"https://www.wikidata.org/wiki/Q2578557","display_name":"Equity (law)","level":2,"score":0.5094000101089478},{"id":"https://openalex.org/C151956035","wikidata":"https://www.wikidata.org/wiki/Q1132755","display_name":"Logistic regression","level":2,"score":0.4932999908924103},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.46630001068115234},{"id":"https://openalex.org/C148524875","wikidata":"https://www.wikidata.org/wiki/Q6975395","display_name":"F1 score","level":2,"score":0.4408999979496002},{"id":"https://openalex.org/C88389905","wikidata":"https://www.wikidata.org/wiki/Q223371","display_name":"Stock market index","level":4,"score":0.424699991941452},{"id":"https://openalex.org/C204036174","wikidata":"https://www.wikidata.org/wiki/Q909380","display_name":"Stock (firearms)","level":2,"score":0.42100000381469727},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.4115000069141388},{"id":"https://openalex.org/C2777382242","wikidata":"https://www.wikidata.org/wiki/Q6017816","display_name":"Index (typography)","level":2,"score":0.40540000796318054},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.39959999918937683},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.3720000088214874},{"id":"https://openalex.org/C200870193","wikidata":"https://www.wikidata.org/wiki/Q11691","display_name":"Stock exchange","level":2,"score":0.36970001459121704},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.326200008392334},{"id":"https://openalex.org/C518677369","wikidata":"https://www.wikidata.org/wiki/Q202833","display_name":"Social media","level":2,"score":0.31630000472068787},{"id":"https://openalex.org/C25287524","wikidata":"https://www.wikidata.org/wiki/Q13416790","display_name":"Social equality","level":2,"score":0.27810001373291016},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.27790001034736633},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.26739999651908875},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.26499998569488525}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.54364/aaiml.2025.53236","is_oa":true,"landing_page_url":"https://doi.org/10.54364/aaiml.2025.53236","pdf_url":"https://doi.org/10.54364/aaiml.2025.53236","source":{"id":"https://openalex.org/S4210238872","display_name":"Advances in Artificial Intelligence and Machine Learning","issn_l":"2582-9793","issn":["2582-9793"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Advances in Artificial Intelligence and Machine Learning","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.54364/aaiml.2025.53236","is_oa":true,"landing_page_url":"https://doi.org/10.54364/aaiml.2025.53236","pdf_url":"https://doi.org/10.54364/aaiml.2025.53236","source":{"id":"https://openalex.org/S4210238872","display_name":"Advances in Artificial Intelligence and Machine Learning","issn_l":"2582-9793","issn":["2582-9793"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Advances in Artificial Intelligence and Machine Learning","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7083025501.pdf","grobid_xml":"https://content.openalex.org/works/W7083025501.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"This":[0],"study":[1],"investigates":[2],"the":[3,10,81,88,98,106,134,151],"phenomenon":[4],"of":[5,64,71,90,153,184],"social":[6,37,73,91,162,190],"stock":[7,24,43],"pumping":[8,25,74,191],"in":[9,32,42,157,182],"Russian":[11,78,99,107],"equity":[12],"market":[13,30,67,202],"and":[14,45,66,105,128,161,176,186,201,208],"explores":[15],"effective":[16],"machine":[17,171],"learning":[18,172],"models":[19,115,173],"for":[20,56],"its":[21],"detection.":[22],"Social":[23,100,108],"is":[26],"defined":[27],"as":[28],"a":[29,54,62],"anomaly":[31],"which":[33],"coordinated":[34],"publications":[35],"on":[36,61,164],"media":[38],"trigger":[39],"abnormal":[40],"increases":[41],"prices":[44],"trading":[46,148,159],"volumes":[47],"without":[48],"fundamental":[49],"justification.":[50],"The":[51,130,167,193],"paper":[52],"proposes":[53],"methodology":[55,195],"identifying":[57],"such":[58],"events":[59],"based":[60],"combination":[63],"behavioral":[65],"indicators.":[68],"A":[69,144],"dataset":[70],"615":[72],"episodes":[75],"across":[76],"104":[77],"companies":[79],"over":[80],"period":[82],"2019\u20132025":[83],"was":[84],"constructed.":[85],"To":[86],"assess":[87],"impact":[89],"media,":[92],"two":[93],"proprietary":[94],"indices":[95],"were":[96,116],"developed:":[97],"Media":[101,109],"Intensive":[102],"Index":[103,111],"(RSMII)":[104],"Sentiment":[110],"(RSMSI).":[112],"Five":[113],"classification":[114],"trained":[117],"to":[118,204],"detect":[119],"manipulation":[120],"events:":[121],"logistic":[122,180],"regression,":[123],"KNN,":[124],"random":[125],"forest,":[126],"SVM":[127],"CatBoost.":[129],"CatBoost":[131,175],"model":[132],"showed":[133],"best":[135],"performance":[136],"(AUC-ROC":[137],"=":[138,142],"0.97,":[139],"F1":[140],"score":[141],"0.91).":[143],"comparison":[145],"with":[146],"normal":[147],"days":[149],"confirmed":[150],"presence":[152],"statistically":[154],"significant":[155],"anomalies":[156],"prices,":[158],"volumes,":[160],"indicators":[163],"pump":[165],"days.":[166],"results":[168],"demonstrate":[169],"that":[170],"(particularly":[174],"KNN)":[177],"substantially":[178],"outperform":[179],"regression":[181],"terms":[183],"accuracy":[185],"recall":[187],"when":[188],"detecting":[189],"cases.":[192],"proposed":[194],"can":[196],"be":[197],"applied":[198],"by":[199],"regulators":[200],"participants":[203],"monitor":[205],"informational":[206],"influence":[207],"manage":[209],"associated":[210],"risks.":[211]},"counts_by_year":[],"updated_date":"2026-03-09T07:00:12.390032","created_date":"2025-10-10T00:00:00"}