{"id":"https://openalex.org/W7127151052","doi":"https://doi.org/10.1186/s40537-026-01372-1","title":"AI crypto trading: multi-class multi-granular analysis for boosting high-frequency trade predictions with fibonacci and hybrid convolutional neural networks","display_name":"AI crypto trading: multi-class multi-granular analysis for boosting high-frequency trade predictions with fibonacci and hybrid convolutional neural networks","publication_year":2026,"publication_date":"2026-02-03","ids":{"openalex":"https://openalex.org/W7127151052","doi":"https://doi.org/10.1186/s40537-026-01372-1"},"language":"en","primary_location":{"id":"doi:10.1186/s40537-026-01372-1","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s40537-026-01372-1","pdf_url":null,"source":{"id":"https://openalex.org/S2737955091","display_name":"Journal Of Big Data","issn_l":"2196-1115","issn":["2196-1115"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","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":"Journal of Big Data","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1186/s40537-026-01372-1","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5093208572","display_name":"Bilal Hassan Ahmed Khattak","orcid":"https://orcid.org/0009-0005-1753-7028"},"institutions":[{"id":"https://openalex.org/I53831458","display_name":"Abasyn University","ror":"https://ror.org/05ws11813","country_code":"PK","type":"education","lineage":["https://openalex.org/I53831458"]}],"countries":["PK"],"is_corresponding":false,"raw_author_name":"Bilal Hassan Ahmed Khattak","raw_affiliation_strings":["Department of Computer Science, Abasyn University, Islamabad Campus, Islamabad, 44000, Pakistan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Abasyn University, Islamabad Campus, Islamabad, 44000, Pakistan","institution_ids":["https://openalex.org/I53831458"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124832804","display_name":"Chaudhry Hamza Rashid","orcid":null},"institutions":[{"id":"https://openalex.org/I53831458","display_name":"Abasyn University","ror":"https://ror.org/05ws11813","country_code":"PK","type":"education","lineage":["https://openalex.org/I53831458"]}],"countries":["PK"],"is_corresponding":false,"raw_author_name":"Chaudhry Hamza Rashid","raw_affiliation_strings":["Department of Computer Science, Abasyn University, Islamabad Campus, Islamabad, 44000, Pakistan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Abasyn University, Islamabad Campus, Islamabad, 44000, Pakistan","institution_ids":["https://openalex.org/I53831458"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124830847","display_name":"Imran Shafi","orcid":null},"institutions":[{"id":"https://openalex.org/I929597975","display_name":"National University of Sciences and Technology","ror":"https://ror.org/03w2j5y17","country_code":"PK","type":"education","lineage":["https://openalex.org/I929597975"]}],"countries":["PK"],"is_corresponding":false,"raw_author_name":"Imran Shafi","raw_affiliation_strings":["College of Electrical and Mechanical Engineering, National University of Sciences and Technology (NUST), Islamabad, 44000, Pakistan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Electrical and Mechanical Engineering, National University of Sciences and Technology (NUST), Islamabad, 44000, Pakistan","institution_ids":["https://openalex.org/I929597975"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124859766","display_name":"Sultan Alfarhood","orcid":null},"institutions":[{"id":"https://openalex.org/I28022161","display_name":"King Saud University","ror":"https://ror.org/02f81g417","country_code":"SA","type":"education","lineage":["https://openalex.org/I28022161"]}],"countries":["SA"],"is_corresponding":true,"raw_author_name":"Sultan Alfarhood","raw_affiliation_strings":["Department of Computer Science, College of Computer and Information Sciences, King Saud University, P.O.Box 51178, Riyadh, 11543, Saudi Arabia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, College of Computer and Information Sciences, King Saud University, P.O.Box 51178, Riyadh, 11543, Saudi Arabia","institution_ids":["https://openalex.org/I28022161"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5009412723","display_name":"Mejdl Safran","orcid":"https://orcid.org/0000-0002-7445-7121"},"institutions":[{"id":"https://openalex.org/I28022161","display_name":"King Saud University","ror":"https://ror.org/02f81g417","country_code":"SA","type":"education","lineage":["https://openalex.org/I28022161"]}],"countries":["SA"],"is_corresponding":false,"raw_author_name":"Mejdl Safran","raw_affiliation_strings":["Department of Computer Science, College of Computer and Information Sciences, King Saud University, P.O.Box 51178, Riyadh, 11543, Saudi Arabia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, College of Computer and Information Sciences, King Saud University, P.O.Box 51178, Riyadh, 11543, Saudi Arabia","institution_ids":["https://openalex.org/I28022161"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5124751942","display_name":"Imran Ashraf","orcid":null},"institutions":[{"id":"https://openalex.org/I55240360","display_name":"Yeungnam University","ror":"https://ror.org/05yc6p159","country_code":"KR","type":"education","lineage":["https://openalex.org/I55240360"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Imran Ashraf","raw_affiliation_strings":["Department of Information and Communication Engineering, Yeungnam University, Gyeongsan, 38541, Republic of Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Information and Communication Engineering, Yeungnam University, Gyeongsan, 38541, Republic of Korea","institution_ids":["https://openalex.org/I55240360"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5124859766"],"corresponding_institution_ids":["https://openalex.org/I28022161"],"apc_list":{"value":1060,"currency":"GBP","value_usd":1300},"apc_paid":{"value":1060,"currency":"GBP","value_usd":1300},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.17721072,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"13","issue":"1","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.6833000183105469,"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":0.6833000183105469,"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/T10270","display_name":"Blockchain Technology Applications and Security","score":0.20990000665187836,"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/T10047","display_name":"Financial Markets and Investment Strategies","score":0.022199999541044235,"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/profitability-index","display_name":"Profitability index","score":0.8019000291824341},{"id":"https://openalex.org/keywords/cryptocurrency","display_name":"Cryptocurrency","score":0.7423999905586243},{"id":"https://openalex.org/keywords/fibonacci-number","display_name":"Fibonacci number","score":0.6510000228881836},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.5138999819755554},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.47130000591278076},{"id":"https://openalex.org/keywords/financial-market","display_name":"Financial market","score":0.4413999915122986},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.42579999566078186},{"id":"https://openalex.org/keywords/investment","display_name":"Investment (military)","score":0.39899998903274536},{"id":"https://openalex.org/keywords/currency","display_name":"Currency","score":0.3946000039577484},{"id":"https://openalex.org/keywords/gradient-boosting","display_name":"Gradient boosting","score":0.36309999227523804}],"concepts":[{"id":"https://openalex.org/C129361004","wikidata":"https://www.wikidata.org/wiki/Q2470236","display_name":"Profitability index","level":2,"score":0.8019000291824341},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8016999959945679},{"id":"https://openalex.org/C180706569","wikidata":"https://www.wikidata.org/wiki/Q13479982","display_name":"Cryptocurrency","level":2,"score":0.7423999905586243},{"id":"https://openalex.org/C173734053","wikidata":"https://www.wikidata.org/wiki/Q47577","display_name":"Fibonacci number","level":2,"score":0.6510000228881836},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.5138999819755554},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5008999705314636},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.47130000591278076},{"id":"https://openalex.org/C19244329","wikidata":"https://www.wikidata.org/wiki/Q208697","display_name":"Financial market","level":2,"score":0.4413999915122986},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4359000027179718},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.42579999566078186},{"id":"https://openalex.org/C27548731","wikidata":"https://www.wikidata.org/wiki/Q88272","display_name":"Investment (military)","level":3,"score":0.39899998903274536},{"id":"https://openalex.org/C141121606","wikidata":"https://www.wikidata.org/wiki/Q8142","display_name":"Currency","level":2,"score":0.3946000039577484},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.39410001039505005},{"id":"https://openalex.org/C70153297","wikidata":"https://www.wikidata.org/wiki/Q5591907","display_name":"Gradient boosting","level":3,"score":0.36309999227523804},{"id":"https://openalex.org/C131562839","wikidata":"https://www.wikidata.org/wiki/Q1574928","display_name":"Trading strategy","level":2,"score":0.350600004196167},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3467000126838684},{"id":"https://openalex.org/C132964779","wikidata":"https://www.wikidata.org/wiki/Q2110223","display_name":"Raw data","level":2,"score":0.34619998931884766},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.32269999384880066},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.3208000063896179},{"id":"https://openalex.org/C103144560","wikidata":"https://www.wikidata.org/wiki/Q2670999","display_name":"Investment strategy","level":3,"score":0.3199999928474426},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.3138999938964844},{"id":"https://openalex.org/C10551718","wikidata":"https://www.wikidata.org/wiki/Q5227332","display_name":"Data pre-processing","level":2,"score":0.3012000024318695},{"id":"https://openalex.org/C117245426","wikidata":"https://www.wikidata.org/wiki/Q235038","display_name":"Technical analysis","level":2,"score":0.2964000105857849},{"id":"https://openalex.org/C2778827112","wikidata":"https://www.wikidata.org/wiki/Q22245680","display_name":"Feature engineering","level":3,"score":0.29179999232292175},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.29170000553131104},{"id":"https://openalex.org/C29368100","wikidata":"https://www.wikidata.org/wiki/Q724931","display_name":"Efficient-market hypothesis","level":4,"score":0.28949999809265137},{"id":"https://openalex.org/C113138325","wikidata":"https://www.wikidata.org/wiki/Q864457","display_name":"Knapsack problem","level":2,"score":0.2824999988079071},{"id":"https://openalex.org/C2776256503","wikidata":"https://www.wikidata.org/wiki/Q7617906","display_name":"Stock market prediction","level":4,"score":0.28029999136924744},{"id":"https://openalex.org/C78508483","wikidata":"https://www.wikidata.org/wiki/Q139445","display_name":"Algorithmic trading","level":2,"score":0.2793999910354614},{"id":"https://openalex.org/C114118609","wikidata":"https://www.wikidata.org/wiki/Q3036837","display_name":"Market data","level":2,"score":0.2727000117301941},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.26589998602867126},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.2597000002861023},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.25529998540878296},{"id":"https://openalex.org/C148524875","wikidata":"https://www.wikidata.org/wiki/Q6975395","display_name":"F1 score","level":2,"score":0.250900000333786},{"id":"https://openalex.org/C66402592","wikidata":"https://www.wikidata.org/wiki/Q2271421","display_name":"Sentiment analysis","level":2,"score":0.2506999969482422},{"id":"https://openalex.org/C2778738651","wikidata":"https://www.wikidata.org/wiki/Q16546687","display_name":"Novelty","level":2,"score":0.2506999969482422}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1186/s40537-026-01372-1","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s40537-026-01372-1","pdf_url":null,"source":{"id":"https://openalex.org/S2737955091","display_name":"Journal Of Big Data","issn_l":"2196-1115","issn":["2196-1115"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","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":"Journal of Big Data","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:65f5aad89ad9421c92873495be9213e4","is_oa":true,"landing_page_url":"https://doaj.org/article/65f5aad89ad9421c92873495be9213e4","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Journal of Big Data, Vol 13, Iss 1 (2026)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1186/s40537-026-01372-1","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s40537-026-01372-1","pdf_url":null,"source":{"id":"https://openalex.org/S2737955091","display_name":"Journal Of Big Data","issn_l":"2196-1115","issn":["2196-1115"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","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":"Journal of Big Data","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":36,"referenced_works":["https://openalex.org/W1980836123","https://openalex.org/W3003975888","https://openalex.org/W3008066307","https://openalex.org/W3064683854","https://openalex.org/W3096832064","https://openalex.org/W3108742976","https://openalex.org/W3115553521","https://openalex.org/W3122305330","https://openalex.org/W3160451326","https://openalex.org/W4225526767","https://openalex.org/W4225852977","https://openalex.org/W4307951690","https://openalex.org/W4309938213","https://openalex.org/W4312036111","https://openalex.org/W4313643148","https://openalex.org/W4316012734","https://openalex.org/W4321510612","https://openalex.org/W4322754479","https://openalex.org/W4327922504","https://openalex.org/W4327981843","https://openalex.org/W4328096194","https://openalex.org/W4365503767","https://openalex.org/W4366224461","https://openalex.org/W4366763643","https://openalex.org/W4379374978","https://openalex.org/W4379381494","https://openalex.org/W4379470225","https://openalex.org/W4382203927","https://openalex.org/W4383501369","https://openalex.org/W4385679813","https://openalex.org/W4390481960","https://openalex.org/W4391107069","https://openalex.org/W4392253748","https://openalex.org/W4401069733","https://openalex.org/W4407918659","https://openalex.org/W4410000204"],"related_works":[],"abstract_inverted_index":{"Cryptocurrency":[0],"markets":[1,43],"focus":[2],"on":[3,96,117,148,210,226,284],"digital":[4],"currency":[5],"trading":[6,51,306],"mechanisms,":[7],"price":[8,35],"prediction,":[9,172],"and":[10,18,31,63,121,137,173,191,196,216,220,229,233,268,280,298],"market":[11,149],"dynamics":[12],"through":[13,37],"financial":[14,119],"analysis,":[15],"risk":[16],"management,":[17],"regulatory":[19],"frameworks.":[20],"Investment":[21],"interest":[22],"in":[23,41,49,125,160,244,304],"cryptocurrencies":[24],"has":[25,200],"increased":[26],"due":[27],"to":[28,65,81,246],"their":[29],"anonymous":[30],"decentralized":[32],"nature,":[33],"but":[34],"prediction":[36,64,116,227],"artificial":[38],"intelligence":[39],"(AI)":[40],"volatile":[42],"remains":[44],"challenging,":[45],"with":[46],"scarce":[47],"research":[48,157],"high-frequency":[50,305],"(HFT).":[52],"This":[53,85,179],"is":[54,107,158],"caused":[55],"by":[56],"the":[57,76,108,112,290],"swiftness":[58],"required":[59],"for":[60,100,143,154,177,213],"data":[61,167,212],"retrieval":[62],"execute":[66,82],"a":[67,88,161,263,269],"successful":[68,72],"trade.":[69],"Thus,":[70],"achieving":[71],"HFT":[73],"necessitates":[74],"leveraging":[75],"full":[77],"potential":[78,270],"of":[79,111,114,131,248,266,272,292,301],"AI":[80,115],"timely":[83],"decisions.":[84],"study":[86,180,300],"proposes":[87],"hybrid":[89],"convolutional":[90],"neural":[91],"network":[92],"(CNN)":[93],"model":[94,189,296],"based":[95,147],"long":[97,252],"short-term":[98],"memory":[99],"multi-class":[101],"crypto":[102],"prediction.":[103],"The":[104,128,151,250],"main":[105],"contribution":[106],"statistical":[109],"analysis":[110,156],"impact":[113],"investors\u2019":[118],"decisions":[120],"profitability":[122,155,192],"improvements":[123,243],"achievable":[124],"multi-granular":[126],"models.":[127],"feature":[129],"engineering":[130],"Fibonacci":[132,184,238],"retracement":[133,185],"levels":[134,186],"captures":[135],"support":[136],"resistance":[138],"zones,":[139],"providing":[140],"additional":[141],"information":[142],"generating":[144],"trade":[145],"signals":[146],"trends.":[150],"methodology":[152],"applied":[153],"grounded":[159],"simulated":[162],"investment":[163,175],"environment,":[164],"which":[165],"involves":[166],"collection,":[168],"preprocessing,":[169],"sampling,":[170],"training,":[171],"finally":[174],"simulation":[176],"evaluation.":[178],"demonstrates":[181],"that":[182,203],"incorporating":[183],"significantly":[187],"enhances":[188],"performance":[190],"across":[193],"various":[194],"architectures":[195],"temporal":[197,294],"domains.":[198],"It":[199],"been":[201],"observed":[202],"C-LSTM":[204,277],"consistently":[205],"outperformed":[206],"other":[207],"models,":[208],"particularly":[209],"1-minute":[211,285],"both":[214],"BTC":[215],"ETH,":[217],"while":[218,257],"MLP":[219,261],"LSTM":[221],"showed":[222,262],"mixed":[223],"results":[224],"depending":[225],"type":[228],"time":[230],"granularity.":[231],"Trend":[232],"trend-strength":[234,258],"predictions":[235],"benefited":[236],"from":[237],"Retracement":[239],"Levels,":[240],"yielding":[241],"ROI":[242,253],"up":[245],"70%":[247],"configurations.":[249],"highest":[251],"gain":[254],"reached":[255],"45%,":[256],"classification":[259],"using":[260],"maximum":[264],"increase":[265],"31.71%":[267],"loss":[271],"-56.73%.":[273],"For":[274],"long-short":[275],"trading,":[276],"provides":[278],"stable":[279],"consistent":[281],"gains,":[282],"especially":[283],"data.":[286],"These":[287],"findings":[288],"highlight":[289],"importance":[291],"fine-grained":[293],"data,":[295],"selection,":[297],"further":[299],"reward-risk":[302],"management":[303],"scenarios.":[307]},"counts_by_year":[],"updated_date":"2026-05-06T08:25:59.206177","created_date":"2026-02-03T00:00:00"}