{"id":"https://openalex.org/W4415489836","doi":"https://doi.org/10.3390/make7040127","title":"A Comprehensive Study on Short-Term Oil Price Forecasting Using Econometric and Machine Learning Techniques","display_name":"A Comprehensive Study on Short-Term Oil Price Forecasting Using Econometric and Machine Learning Techniques","publication_year":2025,"publication_date":"2025-10-23","ids":{"openalex":"https://openalex.org/W4415489836","doi":"https://doi.org/10.3390/make7040127"},"language":"en","primary_location":{"id":"doi:10.3390/make7040127","is_oa":true,"landing_page_url":"https://doi.org/10.3390/make7040127","pdf_url":"https://www.mdpi.com/2504-4990/7/4/127/pdf?version=1761222533","source":{"id":"https://openalex.org/S4210213891","display_name":"Machine Learning and Knowledge Extraction","issn_l":"2504-4990","issn":["2504-4990"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Learning and Knowledge Extraction","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/2504-4990/7/4/127/pdf?version=1761222533","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Gil Cohen","orcid":"https://orcid.org/0000-0002-1173-0338"},"institutions":[{"id":"https://openalex.org/I23973178","display_name":"Western Galilee College","ror":"https://ror.org/00ajd9b21","country_code":"IL","type":"education","lineage":["https://openalex.org/I23973178"]}],"countries":["IL"],"is_corresponding":true,"raw_author_name":"Gil Cohen","raw_affiliation_strings":["Management Department, Western Galilee Academic College, Acre 2412101, Israel"],"affiliations":[{"raw_affiliation_string":"Management Department, Western Galilee Academic College, Acre 2412101, Israel","institution_ids":["https://openalex.org/I23973178"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I23973178"],"apc_list":{"value":1400,"currency":"CHF","value_usd":1515},"apc_paid":{"value":1400,"currency":"CHF","value_usd":1515},"fwci":9.4105,"has_fulltext":true,"cited_by_count":3,"citation_normalized_percentile":{"value":0.97644628,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":91,"max":99},"biblio":{"volume":"7","issue":"4","first_page":"127","last_page":"127"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11059","display_name":"Market Dynamics and Volatility","score":0.9993000030517578,"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"}},"topics":[{"id":"https://openalex.org/T11059","display_name":"Market Dynamics and Volatility","score":0.9993000030517578,"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"}},{"id":"https://openalex.org/T12617","display_name":"Energy, Environment, and Transportation Policies","score":0.9811999797821045,"subfield":{"id":"https://openalex.org/subfields/2105","display_name":"Renewable Energy, Sustainability and the Environment"},"field":{"id":"https://openalex.org/fields/21","display_name":"Energy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10007","display_name":"Monetary Policy and Economic Impact","score":0.9789999723434448,"subfield":{"id":"https://openalex.org/subfields/2000","display_name":"General Economics, Econometrics and 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/volatility","display_name":"Volatility (finance)","score":0.710099995136261},{"id":"https://openalex.org/keywords/predictability","display_name":"Predictability","score":0.6312999725341797},{"id":"https://openalex.org/keywords/oil-price","display_name":"Oil price","score":0.508400022983551},{"id":"https://openalex.org/keywords/crude-oil","display_name":"Crude oil","score":0.48669999837875366},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.46389999985694885},{"id":"https://openalex.org/keywords/econometric-model","display_name":"Econometric model","score":0.444599986076355},{"id":"https://openalex.org/keywords/west-texas-intermediate","display_name":"West Texas Intermediate","score":0.4113999903202057}],"concepts":[{"id":"https://openalex.org/C91602232","wikidata":"https://www.wikidata.org/wiki/Q756115","display_name":"Volatility (finance)","level":2,"score":0.710099995136261},{"id":"https://openalex.org/C197640229","wikidata":"https://www.wikidata.org/wiki/Q2534066","display_name":"Predictability","level":2,"score":0.6312999725341797},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.609499990940094},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5145999789237976},{"id":"https://openalex.org/C2984556133","wikidata":"https://www.wikidata.org/wiki/Q297279","display_name":"Oil price","level":2,"score":0.508400022983551},{"id":"https://openalex.org/C2987168347","wikidata":"https://www.wikidata.org/wiki/Q22656","display_name":"Crude oil","level":2,"score":0.48669999837875366},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4803999960422516},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.4645000100135803},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.46389999985694885},{"id":"https://openalex.org/C180075932","wikidata":"https://www.wikidata.org/wiki/Q5333342","display_name":"Econometric model","level":2,"score":0.444599986076355},{"id":"https://openalex.org/C70784835","wikidata":"https://www.wikidata.org/wiki/Q1049071","display_name":"West Texas Intermediate","level":3,"score":0.4113999903202057},{"id":"https://openalex.org/C2779439359","wikidata":"https://www.wikidata.org/wiki/Q317088","display_name":"Commodity","level":2,"score":0.41130000352859497},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4018999934196472},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.4002000093460083},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3521000146865845},{"id":"https://openalex.org/C163068380","wikidata":"https://www.wikidata.org/wiki/Q3409313","display_name":"Economic forecasting","level":2,"score":0.3190000057220459},{"id":"https://openalex.org/C19244329","wikidata":"https://www.wikidata.org/wiki/Q208697","display_name":"Financial market","level":2,"score":0.31790000200271606},{"id":"https://openalex.org/C106306483","wikidata":"https://www.wikidata.org/wiki/Q183984","display_name":"Futures contract","level":2,"score":0.2660999894142151},{"id":"https://openalex.org/C24189920","wikidata":"https://www.wikidata.org/wiki/Q1660345","display_name":"Implied volatility","level":3,"score":0.2587999999523163},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.25290000438690186}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.3390/make7040127","is_oa":true,"landing_page_url":"https://doi.org/10.3390/make7040127","pdf_url":"https://www.mdpi.com/2504-4990/7/4/127/pdf?version=1761222533","source":{"id":"https://openalex.org/S4210213891","display_name":"Machine Learning and Knowledge Extraction","issn_l":"2504-4990","issn":["2504-4990"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Learning and Knowledge Extraction","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:f92c365cde3847db9441798b16e2a005","is_oa":true,"landing_page_url":"https://doaj.org/article/f92c365cde3847db9441798b16e2a005","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":"Machine Learning and Knowledge Extraction, Vol 7, Iss 4, p 127 (2025)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.3390/make7040127","is_oa":true,"landing_page_url":"https://doi.org/10.3390/make7040127","pdf_url":"https://www.mdpi.com/2504-4990/7/4/127/pdf?version=1761222533","source":{"id":"https://openalex.org/S4210213891","display_name":"Machine Learning and Knowledge Extraction","issn_l":"2504-4990","issn":["2504-4990"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Learning and Knowledge Extraction","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4415489836.pdf","grobid_xml":"https://content.openalex.org/works/W4415489836.grobid-xml"},"referenced_works_count":43,"referenced_works":["https://openalex.org/W388323479","https://openalex.org/W2018404189","https://openalex.org/W2033343256","https://openalex.org/W2051331659","https://openalex.org/W2139116473","https://openalex.org/W2617434451","https://openalex.org/W2782085727","https://openalex.org/W2883109097","https://openalex.org/W2899771208","https://openalex.org/W2899903193","https://openalex.org/W2912032242","https://openalex.org/W2941851814","https://openalex.org/W2942246517","https://openalex.org/W2974308494","https://openalex.org/W3021299754","https://openalex.org/W3027940452","https://openalex.org/W3135312874","https://openalex.org/W3164724734","https://openalex.org/W3174043185","https://openalex.org/W3211208493","https://openalex.org/W4220736439","https://openalex.org/W4292856630","https://openalex.org/W4294959624","https://openalex.org/W4313203700","https://openalex.org/W4317906235","https://openalex.org/W4319459192","https://openalex.org/W4319977956","https://openalex.org/W4376651878","https://openalex.org/W4384072061","https://openalex.org/W4385521746","https://openalex.org/W4386097686","https://openalex.org/W4386239484","https://openalex.org/W4387035576","https://openalex.org/W4389213206","https://openalex.org/W4390883410","https://openalex.org/W4391217845","https://openalex.org/W4395482222","https://openalex.org/W4396658127","https://openalex.org/W4400698552","https://openalex.org/W4404829429","https://openalex.org/W4406617780","https://openalex.org/W4406662394","https://openalex.org/W4409889533"],"related_works":[],"abstract_inverted_index":{"This":[0,99],"paper":[1],"investigates":[2],"the":[3,45,56,81,103,131],"short-term":[4],"predictability":[5],"of":[6,29,39,47,68,85,107,134],"daily":[7],"crude":[8],"oil":[9,48,109,126,142],"price":[10,49],"movements":[11],"by":[12],"employing":[13],"a":[14,27,166],"multi-method":[15],"analytical":[16],"framework":[17],"that":[18,89,102,159],"incorporates":[19],"both":[20],"econometric":[21],"and":[22,32,51,76,114,121,124,137,144,152],"machine":[23,162],"learning":[24,163],"techniques.":[25],"Utilizing":[26],"dataset":[28],"21":[30],"financial":[31],"commodity":[33],"time":[34],"series":[35],"spanning":[36],"ten":[37],"years":[38],"trading":[40],"days":[41],"(2015\u20132024),":[42],"we":[43,59,157],"explore":[44],"dynamics":[46],"volatility":[50,135,154],"its":[52],"key":[53],"determinants.":[54],"In":[55],"forecasting":[57,141],"phase,":[58],"applied":[60],"seven":[61],"models.":[62,155],"The":[63,128],"meta-learner":[64],"model,":[65],"which":[66],"consists":[67],"three":[69],"base":[70],"learners":[71],"(Random":[72],"Forest,":[73],"gradient":[74],"boosting,":[75],"support":[77],"vector":[78],"regression),":[79],"achieved":[80],"highest":[82],"R2":[83],"value":[84],"0.532,":[86],"providing":[87],"evidence":[88],"our":[90],"complex":[91],"model":[92],"structure":[93],"can":[94],"successfully":[95],"outperform":[96],"existing":[97],"approaches.":[98],"ensemble":[100],"demonstrated":[101],"most":[104],"influential":[105],"predictors":[106],"next-day":[108],"prices":[110],"are":[111],"VIX,":[112],"OVX,":[113],"MOVE":[115],"(volatility":[116],"indices":[117],"for":[118,148],"equities,":[119],"oil,":[120],"bonds,":[122],"respectively),":[123],"lagged":[125],"returns.":[127],"results":[129],"underscore":[130],"critical":[132],"role":[133],"spillovers":[136],"nonlinear":[138],"dependencies":[139],"in":[140],"returns":[143],"suggest":[145],"future":[146],"directions":[147],"integrating":[149],"macroeconomic":[150],"signals":[151],"advanced":[153],"Moreover,":[156],"show":[158],"combining":[160],"multiple":[161],"procedures":[164],"into":[165],"single":[167],"meta-model":[168],"yields":[169],"superior":[170],"predictive":[171],"performance.":[172]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":1}],"updated_date":"2026-03-29T08:15:47.926485","created_date":"2025-10-24T00:00:00"}
