{"id":"https://openalex.org/W4415913064","doi":"https://doi.org/10.1007/s44163-025-00519-y","title":"Machine learning approaches to cryptocurrency trading optimization: a comparative analysis of predictive models","display_name":"Machine learning approaches to cryptocurrency trading optimization: a comparative analysis of predictive models","publication_year":2025,"publication_date":"2025-11-05","ids":{"openalex":"https://openalex.org/W4415913064","doi":"https://doi.org/10.1007/s44163-025-00519-y"},"language":"en","primary_location":{"id":"doi:10.1007/s44163-025-00519-y","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s44163-025-00519-y","pdf_url":"https://link.springer.com/content/pdf/10.1007/s44163-025-00519-y.pdf","source":{"id":"https://openalex.org/S4210220416","display_name":"Discover Artificial Intelligence","issn_l":"2731-0809","issn":["2731-0809"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319965","host_organization_name":"Springer Nature","host_organization_lineage":["https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Nature"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Discover Artificial Intelligence","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://link.springer.com/content/pdf/10.1007/s44163-025-00519-y.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5116213731","display_name":"Deborah Adedigba","orcid":null},"institutions":[{"id":"https://openalex.org/I156118397","display_name":"Southampton Solent University","ror":"https://ror.org/05xydav19","country_code":"GB","type":"education","lineage":["https://openalex.org/I156118397"]}],"countries":["GB"],"is_corresponding":true,"raw_author_name":"Deborah Adedigba","raw_affiliation_strings":["Department of Science and Engineering, Southampton Solent University, E Park Terrace Southampton, Hampshire, SO14 0YN, UK"],"affiliations":[{"raw_affiliation_string":"Department of Science and Engineering, Southampton Solent University, E Park Terrace Southampton, Hampshire, SO14 0YN, UK","institution_ids":["https://openalex.org/I156118397"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5120264797","display_name":"David Agbolade","orcid":null},"institutions":[{"id":"https://openalex.org/I156118397","display_name":"Southampton Solent University","ror":"https://ror.org/05xydav19","country_code":"GB","type":"education","lineage":["https://openalex.org/I156118397"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"David Agbolade","raw_affiliation_strings":["Department of Science and Engineering, Southampton Solent University, E Park Terrace Southampton, Hampshire, SO14 0YN, UK"],"affiliations":[{"raw_affiliation_string":"Department of Science and Engineering, Southampton Solent University, E Park Terrace Southampton, Hampshire, SO14 0YN, UK","institution_ids":["https://openalex.org/I156118397"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5065521530","display_name":"Raza Hasan","orcid":"https://orcid.org/0000-0002-8089-837X"},"institutions":[{"id":"https://openalex.org/I156118397","display_name":"Southampton Solent University","ror":"https://ror.org/05xydav19","country_code":"GB","type":"education","lineage":["https://openalex.org/I156118397"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Raza Hasan","raw_affiliation_strings":["Department of Science and Engineering, Southampton Solent University, E Park Terrace Southampton, Hampshire, SO14 0YN, UK"],"affiliations":[{"raw_affiliation_string":"Department of Science and Engineering, Southampton Solent University, E Park Terrace Southampton, Hampshire, SO14 0YN, UK","institution_ids":["https://openalex.org/I156118397"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5116213731"],"corresponding_institution_ids":["https://openalex.org/I156118397"],"apc_list":{"value":990,"currency":"EUR","value_usd":1067},"apc_paid":{"value":990,"currency":"EUR","value_usd":1067},"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.47045017,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"5","issue":"1","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10270","display_name":"Blockchain Technology Applications and Security","score":0.8593000173568726,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T10270","display_name":"Blockchain Technology Applications and Security","score":0.8593000173568726,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T11326","display_name":"Stock Market Forecasting Methods","score":0.07739999890327454,"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":0.008799999952316284,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/cryptocurrency","display_name":"Cryptocurrency","score":0.8959000110626221},{"id":"https://openalex.org/keywords/gradient-boosting","display_name":"Gradient boosting","score":0.7099999785423279},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.5817000269889832},{"id":"https://openalex.org/keywords/algorithmic-trading","display_name":"Algorithmic trading","score":0.5691999793052673},{"id":"https://openalex.org/keywords/trading-strategy","display_name":"Trading strategy","score":0.5404999852180481},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.44350001215934753},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4307999908924103},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.400299996137619},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.3984000086784363},{"id":"https://openalex.org/keywords/volatility","display_name":"Volatility (finance)","score":0.39579999446868896}],"concepts":[{"id":"https://openalex.org/C180706569","wikidata":"https://www.wikidata.org/wiki/Q13479982","display_name":"Cryptocurrency","level":2,"score":0.8959000110626221},{"id":"https://openalex.org/C70153297","wikidata":"https://www.wikidata.org/wiki/Q5591907","display_name":"Gradient boosting","level":3,"score":0.7099999785423279},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6995000243186951},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.670199990272522},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6697999835014343},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.5817000269889832},{"id":"https://openalex.org/C78508483","wikidata":"https://www.wikidata.org/wiki/Q139445","display_name":"Algorithmic trading","level":2,"score":0.5691999793052673},{"id":"https://openalex.org/C131562839","wikidata":"https://www.wikidata.org/wiki/Q1574928","display_name":"Trading strategy","level":2,"score":0.5404999852180481},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.44350001215934753},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4307999908924103},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.400299996137619},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.3984000086784363},{"id":"https://openalex.org/C91602232","wikidata":"https://www.wikidata.org/wiki/Q756115","display_name":"Volatility (finance)","level":2,"score":0.39579999446868896},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.3950999975204468},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.37860000133514404},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.3517000079154968},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.351500004529953},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3472999930381775},{"id":"https://openalex.org/C27438332","wikidata":"https://www.wikidata.org/wiki/Q2873","display_name":"Principal component analysis","level":2,"score":0.34119999408721924},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.3165999948978424},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.3149999976158142},{"id":"https://openalex.org/C129824826","wikidata":"https://www.wikidata.org/wiki/Q2630107","display_name":"Granger causality","level":2,"score":0.3009999990463257},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.29679998755455017},{"id":"https://openalex.org/C117245426","wikidata":"https://www.wikidata.org/wiki/Q235038","display_name":"Technical analysis","level":2,"score":0.29429998993873596},{"id":"https://openalex.org/C148524875","wikidata":"https://www.wikidata.org/wiki/Q6975395","display_name":"F1 score","level":2,"score":0.2847000062465668},{"id":"https://openalex.org/C150217764","wikidata":"https://www.wikidata.org/wiki/Q6803607","display_name":"Mean absolute percentage error","level":3,"score":0.2797999978065491},{"id":"https://openalex.org/C2776142675","wikidata":"https://www.wikidata.org/wiki/Q7939999","display_name":"Volatility clustering","level":4,"score":0.2791000008583069},{"id":"https://openalex.org/C115903097","wikidata":"https://www.wikidata.org/wiki/Q7094097","display_name":"Online machine learning","level":3,"score":0.26930001378059387},{"id":"https://openalex.org/C110083411","wikidata":"https://www.wikidata.org/wiki/Q1744628","display_name":"Statistical classification","level":2,"score":0.2660999894142151},{"id":"https://openalex.org/C24683644","wikidata":"https://www.wikidata.org/wiki/Q138372","display_name":"High-frequency trading","level":3,"score":0.2653999924659729},{"id":"https://openalex.org/C158876240","wikidata":"https://www.wikidata.org/wiki/Q17105131","display_name":"Pairs trade","level":4,"score":0.2619999945163727},{"id":"https://openalex.org/C60777511","wikidata":"https://www.wikidata.org/wiki/Q3045002","display_name":"Concept drift","level":3,"score":0.25679999589920044}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1007/s44163-025-00519-y","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s44163-025-00519-y","pdf_url":"https://link.springer.com/content/pdf/10.1007/s44163-025-00519-y.pdf","source":{"id":"https://openalex.org/S4210220416","display_name":"Discover Artificial Intelligence","issn_l":"2731-0809","issn":["2731-0809"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319965","host_organization_name":"Springer Nature","host_organization_lineage":["https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Nature"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Discover Artificial Intelligence","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:6f501e95dad24dffaf236d0a3df8dbdf","is_oa":true,"landing_page_url":"https://doaj.org/article/6f501e95dad24dffaf236d0a3df8dbdf","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Discover Artificial Intelligence, Vol 5, Iss 1, Pp 1-28 (2025)","raw_type":"article"},{"id":"pmh:oai:pure.atira.dk:publications/59325e70-45e9-41d7-b0fc-3e410f52e345","is_oa":true,"landing_page_url":"https://pure.solent.ac.uk/en/publications/59325e70-45e9-41d7-b0fc-3e410f52e345","pdf_url":null,"source":{"id":"https://openalex.org/S4306402589","display_name":"Solent University Research Portal (Solent University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I156118397","host_organization_name":"Southampton Solent University","host_organization_lineage":["https://openalex.org/I156118397"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Adedigba, D, Agbolade, D & Hasan, R 2025, 'Machine learning approaches to cryptocurrency trading optimization: a comparative analysis of predictive models', Discover Artificial Intelligence, vol. 5, no. 1, 310. https://doi.org/10.1007/s44163-025-00519-y","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1007/s44163-025-00519-y","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s44163-025-00519-y","pdf_url":"https://link.springer.com/content/pdf/10.1007/s44163-025-00519-y.pdf","source":{"id":"https://openalex.org/S4210220416","display_name":"Discover Artificial Intelligence","issn_l":"2731-0809","issn":["2731-0809"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319965","host_organization_name":"Springer Nature","host_organization_lineage":["https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Nature"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Discover Artificial Intelligence","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4415913064.pdf","grobid_xml":"https://content.openalex.org/works/W4415913064.grobid-xml"},"referenced_works_count":26,"referenced_works":["https://openalex.org/W1979575715","https://openalex.org/W2295598076","https://openalex.org/W2344786740","https://openalex.org/W2624385633","https://openalex.org/W2790958077","https://openalex.org/W2912614928","https://openalex.org/W2913555449","https://openalex.org/W2930793690","https://openalex.org/W2993958139","https://openalex.org/W2998669639","https://openalex.org/W3014864347","https://openalex.org/W3021511960","https://openalex.org/W3040379296","https://openalex.org/W3207276325","https://openalex.org/W4210404112","https://openalex.org/W4280558612","https://openalex.org/W4309212331","https://openalex.org/W4311770733","https://openalex.org/W4403616295","https://openalex.org/W4405257557","https://openalex.org/W4406146381","https://openalex.org/W4407363390","https://openalex.org/W4408500686","https://openalex.org/W4410006510","https://openalex.org/W4410220405","https://openalex.org/W4411142526"],"related_works":[],"abstract_inverted_index":{"Cryptocurrency":[0],"markets":[1],"are":[2],"characterized":[3],"by":[4],"high":[5],"volatility":[6],"and":[7,13,17,61,78,107,112,117,143,177],"complex":[8],"patterns,":[9],"creating":[10],"both":[11,139],"challenges":[12],"opportunities":[14],"for":[15,26,43,129,150,162,185],"traders":[16],"investors.":[18],"This":[19],"study":[20],"introduces":[21],"a":[22,47,182],"machine":[23,69,144],"learning":[24,70,145],"framework":[25,133],"cryptocurrency":[27,87,151,163,187],"trading":[28,37,136,178,188],"optimization":[29],"that":[30,156],"leverages":[31],"advanced":[32,174],"analytical":[33],"techniques":[34],"to":[35,64,85,167],"enhance":[36],"decisions.":[38,189],"We":[39],"extracted":[40],"historical":[41],"data":[42],"30":[44],"cryptocurrencies":[45],"over":[46],"four-year":[48],"period":[49],"from":[50],"Yahoo":[51],"Finance.":[52],"After":[53],"preprocessing,":[54],"we":[55],"applied":[56],"Principal":[57],"Component":[58],"Analysis":[59],"(PCA)":[60],"K-means":[62],"clustering":[63],"select":[65],"representative":[66],"coins.":[67,131],"Four":[68],"models":[71,119,158],"(Gradient":[72],"Boosting,":[73],"XGBoost,":[74],"Support":[75],"Vector":[76],"Regression,":[77],"Long":[79],"Short-Term":[80],"Memory":[81],"networks)":[82],"were":[83],"trained":[84],"predict":[86],"price":[88,164],"movements.":[89],"Model":[90],"performance":[91,161],"was":[92],"evaluated":[93],"using":[94],"multiple":[95],"metrics,":[96],"including":[97],"Mean":[98,103],"Absolute":[99],"Error":[100,105],"(MAE),":[101],"Root":[102],"Squared":[104],"(RMSE),":[106],"R-squared":[108],"(R2).":[109],"Gradient":[110],"Boosting":[111],"XGBoost":[113],"consistently":[114],"outperformed":[115],"SVR":[116],"LSTM":[118],"across":[120],"all":[121],"cryptocurrencies,":[122],"with":[123],"R2":[124],"values":[125],"of":[126,173],"approximately":[127],"0.98":[128],"most":[130],"The":[132,171],"successfully":[134],"identified":[135],"signals":[137],"through":[138],"moving":[140],"average":[141],"strategies":[142],"predictions,":[146],"providing":[147],"actionable":[148],"insights":[149],"traders.":[152],"Our":[153],"analysis":[154],"demonstrates":[155],"ensemble-based":[157],"offer":[159],"superior":[160],"prediction":[165],"compared":[166],"neural":[168],"network":[169],"approaches.":[170],"integration":[172],"visualization":[175],"tools":[176],"signal":[179],"generation":[180],"creates":[181],"comprehensive":[183],"system":[184],"data-driven":[186]},"counts_by_year":[],"updated_date":"2026-03-18T14:38:29.013473","created_date":"2025-11-05T00:00:00"}
