{"id":"https://openalex.org/W4413684635","doi":"https://doi.org/10.63125/z8qq6h36","title":"DEPLOYMENT AND PERFORMANCE EVALUATION OF HYBRID MACHINE LEARNING MODELS FOR STOCK PRICE FORECASTING AND RISK PREDICTION IN VOLATILE MARKETS","display_name":"DEPLOYMENT AND PERFORMANCE EVALUATION OF HYBRID MACHINE LEARNING MODELS FOR STOCK PRICE FORECASTING AND RISK PREDICTION IN VOLATILE MARKETS","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4413684635","doi":"https://doi.org/10.63125/z8qq6h36"},"language":"en","primary_location":{"id":"doi:10.63125/z8qq6h36","doi":"https://doi.org/10.63125/z8qq6h36","is_oa":true,"landing_page_url":"https://doi.org/10.63125/z8qq6h36","pdf_url":"https://researchinnovationjournal.com/index.php/AJSRI/article/download/60/49","source":{"id":"https://openalex.org/S5407048865","display_name":"American Journal of Scholarly Research and Innovation","issn_l":"3067-2163","issn":["3067-2163"],"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":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"American Journal of Scholarly Research and Innovation","raw_type":"journal-article"},"volume":null,"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"bronze","oa_url":"https://researchinnovationjournal.com/index.php/AJSRI/article/download/60/49","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5114493520","display_name":"Md Shah Ali Dolon","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I40253897","display_name":"University of New Haven","ror":"https://ror.org/00zm4rq24","country_code":"US","type":"education","lineage":["https://openalex.org/I40253897"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Md Shah Ali Dolon","raw_affiliation_strings":["MS in Finance and Financial Analytics, University of New Haven, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"MS in Finance and Financial Analytics, University of New Haven, USA","institution_ids":["https://openalex.org/I40253897"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5114493520"],"corresponding_institution_ids":["https://openalex.org/I40253897"],"apc_list":null,"apc_paid":null,"fwci":0.8189,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":{"value":0.77447692,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":93,"max":96},"biblio":{"volume":"04","issue":"01","first_page":"287","last_page":"319"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":1.0,"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":1.0,"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":0.0,"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/T11918","display_name":"Forecasting Techniques and Applications","score":0.0,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/hybrid-machine-learning","display_name":"hybrid machine learning","score":0.8169999718666077},{"id":"https://openalex.org/keywords/stock-price-prediction","display_name":"stock price prediction","score":0.9990000128746033},{"id":"https://openalex.org/keywords/risk-prediction","display_name":"risk prediction","score":0.8019999861717224},{"id":"https://openalex.org/keywords/value-at-risk","display_name":"value at risk","score":0.8349999785423279},{"id":"https://openalex.org/keywords/conditional-value-at-risk","display_name":"conditional value at risk","score":0.8949999809265137},{"id":"https://openalex.org/keywords/mlops","display_name":"MLOps","score":0.7639999985694885},{"id":"https://openalex.org/keywords/model-deployment","display_name":"model deployment","score":0.8899999856948853},{"id":"https://openalex.org/keywords/volatility","display_name":"volatility","score":0.37400001287460327},{"id":"https://openalex.org/keywords/attention-models","display_name":"attention-based models","score":0.5789999961853027},{"id":"https://openalex.org/keywords/model-drift-monitoring","display_name":"model drift monitoring","score":0.5429999828338623},{"id":"https://openalex.org/keywords/probability-calibration","display_name":"probability calibration","score":0.621999979019165}],"concepts":[{"id":"https://openalex.org/C105339364","wikidata":"https://www.wikidata.org/wiki/Q2297740","display_name":"Software deployment","level":2,"score":0.6655689477920532},{"id":"https://openalex.org/C204036174","wikidata":"https://www.wikidata.org/wiki/Q909380","display_name":"Stock (firearms)","level":2,"score":0.5417149662971497},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4812636077404022},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.47029730677604675},{"id":"https://openalex.org/C2988984586","wikidata":"https://www.wikidata.org/wiki/Q1020013","display_name":"Stock price","level":3,"score":0.4519094228744507},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4111321270465851},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3536742925643921},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.17370694875717163},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.07404986023902893},{"id":"https://openalex.org/C115903868","wikidata":"https://www.wikidata.org/wiki/Q80993","display_name":"Software engineering","level":1,"score":0.0618249773979187},{"id":"https://openalex.org/C78519656","wikidata":"https://www.wikidata.org/wiki/Q101333","display_name":"Mechanical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.63125/z8qq6h36","doi":"https://doi.org/10.63125/z8qq6h36","is_oa":true,"landing_page_url":"https://doi.org/10.63125/z8qq6h36","pdf_url":"https://researchinnovationjournal.com/index.php/AJSRI/article/download/60/49","source":{"id":"https://openalex.org/S5407048865","display_name":"American Journal of Scholarly Research and Innovation","issn_l":"3067-2163","issn":["3067-2163"],"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":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"American Journal of Scholarly Research and Innovation","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.63125/z8qq6h36","doi":"https://doi.org/10.63125/z8qq6h36","is_oa":true,"landing_page_url":"https://doi.org/10.63125/z8qq6h36","pdf_url":"https://researchinnovationjournal.com/index.php/AJSRI/article/download/60/49","source":{"id":"https://openalex.org/S5407048865","display_name":"American Journal of Scholarly Research and Innovation","issn_l":"3067-2163","issn":["3067-2163"],"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":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"American Journal of Scholarly Research and Innovation","raw_type":"journal-article"},"sustainable_development_goals":[],"sustainable_development_goals_aurora":[],"study_designs":[{"id":"https://openalex.org/study-designs/systematic-review","display_name":"Systematic Review"}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4413684635.pdf","grobid_xml":"https://content.openalex.org/works/W4413684635.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W247222457","https://openalex.org/W1488120909","https://openalex.org/W3124131549","https://openalex.org/W4318612353","https://openalex.org/W2152348935","https://openalex.org/W2887069341","https://openalex.org/W2554106722","https://openalex.org/W1797892342","https://openalex.org/W4240248738","https://openalex.org/W3008476150"],"abstract_inverted_index":{"This":[0],"study":[1],"investigates":[2],"how":[3],"hybrid":[4],"machine":[5],"learning":[6,114],"systems":[7],"can":[8],"be":[9],"implemented":[10],"and":[11,19,42,50,74,84,107,139,142,149,158,175,212,218,225],"deployed":[12],"to":[13,95,215],"deliver":[14],"reliable":[15],"stock":[16],"price":[17],"forecasting":[18,217],"risk":[20,219],"prediction":[21],"in":[22,63],"volatile,":[23],"internationally":[24],"integrated":[25],"markets.":[26],"Using":[27],"a":[28,99,104,108,201],"PRISMA":[29],"protocol,":[30],"we":[31],"reviewed":[32],"120":[33],"peer-reviewed":[34],"studies":[35,125],"with":[36,71,103],"deployment-relevant":[37],"detail,":[38],"harmonized":[39],"their":[40],"metrics,":[41],"synthesized":[43],"evidence":[44,199],"across":[45],"two":[46],"layers:":[47],"predictive":[48],"performance":[49,163],"MLOps":[51],"operations.":[52],"The":[53],"analysis":[54],"shows":[55],"that":[56,78,90,126,154,204,221],"hybrids":[57],"consistently":[58],"convert":[59],"modest":[60],"single-digit":[61],"reductions":[62],"point":[64],"error":[65],"into":[66,233],"materially":[67],"better":[68],"probabilistic":[69],"calibration,":[70],"tighter":[72],"Value-at-Risk":[73],"Expected":[75],"Shortfall":[76],"coverage":[77],"holds":[79],"up":[80],"under":[81,227],"walk-forward":[82],"evaluation":[83],"during":[85],"high-volatility":[86],"regimes.":[87],"Design":[88],"patterns":[89],"travel":[91],"well":[92],"from":[93],"lab":[94],"production":[96],"include":[97],"combining":[98],"decomposable":[100],"statistical":[101],"baseline":[102],"tabular":[105],"learner":[106],"sequence":[109],"or":[110,131,135],"attention":[111],"model,":[112],"then":[113],"dynamic,":[115],"regime-aware":[116],"weights":[117],"on":[118],"rolling":[119],"residuals.":[120],"On":[121],"the":[122,198],"engineering":[123],"side,":[124],"report":[127],"model":[128],"registries,":[129],"CI":[130],"CD":[132],"gates,":[133],"canary":[134],"shadow":[136],"rollouts,":[137],"drift":[138],"exceedance":[140],"monitoring,":[141],"rollback":[143],"playbooks":[144],"exhibit":[145],"smaller":[146],"backtest-to-live":[147],"gaps":[148],"lower":[150],"reversal":[151],"rates,":[152],"highlighting":[153],"disciplined":[155],"data":[156],"contracts":[157],"promotion":[159],"controls":[160],"function":[161],"as":[162,173],"multipliers":[164],"rather":[165],"than":[166],"overhead.":[167],"Measurement":[168],"choices":[169],"further":[170],"amplify":[171],"deployability,":[172],"realized-volatility":[174],"lightweight":[176],"range-based":[177],"estimators":[178],"improve":[179],"distributional":[180],"sharpness":[181],"at":[182],"low":[183],"computational":[184],"cost,":[185],"while":[186],"portable":[187],"microstructure":[188],"features":[189],"strengthen":[190],"short-horizon":[191],"direction":[192],"without":[193],"violating":[194],"latency":[195],"budgets.":[196],"Overall,":[197],"supports":[200],"practical":[202],"blueprint":[203],"integrates":[205],"feature":[206],"stores,":[207],"reproducible":[208],"pipelines,":[209],"dynamic":[210],"hybridization,":[211],"risk-aware":[213],"monitoring":[214],"produce":[216],"services":[220],"are":[222],"auditable,":[223],"explainable,":[224],"resilient":[226],"market":[228],"stress,":[229],"turning":[230],"incremental":[231],"accuracy":[232],"dependable":[234],"tail":[235],"behavior":[236],"suitable":[237],"for":[238],"real-world":[239],"deployment.":[240]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-09-26T08:19:03.415552","created_date":"2025-08-27T00:00:00"}
