{"id":"https://openalex.org/W4318350632","doi":"https://doi.org/10.1186/s40537-022-00676-2","title":"Forex market forecasting using machine learning: Systematic Literature Review and meta-analysis","display_name":"Forex market forecasting using machine learning: Systematic Literature Review and meta-analysis","publication_year":2023,"publication_date":"2023-01-28","ids":{"openalex":"https://openalex.org/W4318350632","doi":"https://doi.org/10.1186/s40537-022-00676-2"},"language":"en","primary_location":{"id":"doi:10.1186/s40537-022-00676-2","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s40537-022-00676-2","pdf_url":"https://journalofbigdata.springeropen.com/counter/pdf/10.1186/s40537-022-00676-2","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","license_id":"https://openalex.org/licenses/cc-by","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://journalofbigdata.springeropen.com/counter/pdf/10.1186/s40537-022-00676-2","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5070540750","display_name":"Michael Ayitey","orcid":"https://orcid.org/0000-0002-6573-1977"},"institutions":[{"id":"https://openalex.org/I291863076","display_name":"University of Energy and Natural Resources","ror":"https://ror.org/05r9rzb75","country_code":"GH","type":"education","lineage":["https://openalex.org/I291863076"]}],"countries":["GH"],"is_corresponding":true,"raw_author_name":"Michael Ayitey Junior","raw_affiliation_strings":["University of Energy and Natural Resources, Sunyani, Ghana"],"affiliations":[{"raw_affiliation_string":"University of Energy and Natural Resources, Sunyani, Ghana","institution_ids":["https://openalex.org/I291863076"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5089844476","display_name":"Peter Appiahene","orcid":"https://orcid.org/0000-0002-6098-4537"},"institutions":[{"id":"https://openalex.org/I291863076","display_name":"University of Energy and Natural Resources","ror":"https://ror.org/05r9rzb75","country_code":"GH","type":"education","lineage":["https://openalex.org/I291863076"]}],"countries":["GH"],"is_corresponding":false,"raw_author_name":"Peter Appiahene","raw_affiliation_strings":["University of Energy and Natural Resources, Sunyani, Ghana"],"affiliations":[{"raw_affiliation_string":"University of Energy and Natural Resources, Sunyani, Ghana","institution_ids":["https://openalex.org/I291863076"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5007815734","display_name":"Obed Appiah","orcid":"https://orcid.org/0000-0002-4680-3015"},"institutions":[{"id":"https://openalex.org/I291863076","display_name":"University of Energy and Natural Resources","ror":"https://ror.org/05r9rzb75","country_code":"GH","type":"education","lineage":["https://openalex.org/I291863076"]}],"countries":["GH"],"is_corresponding":false,"raw_author_name":"Obed Appiah","raw_affiliation_strings":["University of Energy and Natural Resources, Sunyani, Ghana"],"affiliations":[{"raw_affiliation_string":"University of Energy and Natural Resources, Sunyani, Ghana","institution_ids":["https://openalex.org/I291863076"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5033475778","display_name":"Christopher Ninfaakang Bombie","orcid":null},"institutions":[{"id":"https://openalex.org/I291863076","display_name":"University of Energy and Natural Resources","ror":"https://ror.org/05r9rzb75","country_code":"GH","type":"education","lineage":["https://openalex.org/I291863076"]}],"countries":["GH"],"is_corresponding":false,"raw_author_name":"Christopher Ninfaakang Bombie","raw_affiliation_strings":["University of Energy and Natural Resources, Sunyani, Ghana"],"affiliations":[{"raw_affiliation_string":"University of Energy and Natural Resources, Sunyani, Ghana","institution_ids":["https://openalex.org/I291863076"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5070540750"],"corresponding_institution_ids":["https://openalex.org/I291863076"],"apc_list":{"value":1060,"currency":"GBP","value_usd":1300},"apc_paid":{"value":1060,"currency":"GBP","value_usd":1300},"fwci":16.3576,"has_fulltext":false,"cited_by_count":66,"citation_normalized_percentile":{"value":0.99464453,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":"10","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":0.9995999932289124,"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":0.9995999932289124,"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/T11059","display_name":"Market Dynamics and Volatility","score":0.9994000196456909,"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/T10047","display_name":"Financial Markets and Investment Strategies","score":0.9919000267982483,"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/foreign-exchange-market","display_name":"Foreign exchange market","score":0.8395276069641113},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7077555060386658},{"id":"https://openalex.org/keywords/currency","display_name":"Currency","score":0.6948347687721252},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.69476318359375},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.666675865650177},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4247209429740906},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4246969521045685},{"id":"https://openalex.org/keywords/technical-analysis","display_name":"Technical analysis","score":0.4202408194541931},{"id":"https://openalex.org/keywords/cryptocurrency","display_name":"Cryptocurrency","score":0.4169452488422394},{"id":"https://openalex.org/keywords/economics","display_name":"Economics","score":0.18789106607437134},{"id":"https://openalex.org/keywords/world-wide-web","display_name":"World Wide Web","score":0.14388984441757202},{"id":"https://openalex.org/keywords/financial-economics","display_name":"Financial economics","score":0.13816022872924805}],"concepts":[{"id":"https://openalex.org/C536366893","wikidata":"https://www.wikidata.org/wiki/Q66076","display_name":"Foreign exchange market","level":3,"score":0.8395276069641113},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7077555060386658},{"id":"https://openalex.org/C141121606","wikidata":"https://www.wikidata.org/wiki/Q8142","display_name":"Currency","level":2,"score":0.6948347687721252},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.69476318359375},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.666675865650177},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4247209429740906},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4246969521045685},{"id":"https://openalex.org/C117245426","wikidata":"https://www.wikidata.org/wiki/Q235038","display_name":"Technical analysis","level":2,"score":0.4202408194541931},{"id":"https://openalex.org/C180706569","wikidata":"https://www.wikidata.org/wiki/Q13479982","display_name":"Cryptocurrency","level":2,"score":0.4169452488422394},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.18789106607437134},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.14388984441757202},{"id":"https://openalex.org/C106159729","wikidata":"https://www.wikidata.org/wiki/Q2294553","display_name":"Financial economics","level":1,"score":0.13816022872924805},{"id":"https://openalex.org/C556758197","wikidata":"https://www.wikidata.org/wiki/Q580018","display_name":"Monetary economics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1186/s40537-022-00676-2","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s40537-022-00676-2","pdf_url":"https://journalofbigdata.springeropen.com/counter/pdf/10.1186/s40537-022-00676-2","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","license_id":"https://openalex.org/licenses/cc-by","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:7c3e4bd5a1804f14a200ed579b84ca82","is_oa":true,"landing_page_url":"https://doaj.org/article/7c3e4bd5a1804f14a200ed579b84ca82","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":"Journal of Big Data, Vol 10, Iss 1, Pp 1-40 (2023)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1186/s40537-022-00676-2","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s40537-022-00676-2","pdf_url":"https://journalofbigdata.springeropen.com/counter/pdf/10.1186/s40537-022-00676-2","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","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Big Data","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Partnerships for the goals","score":0.47999998927116394,"id":"https://metadata.un.org/sdg/17"}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4318350632.pdf"},"referenced_works_count":59,"referenced_works":["https://openalex.org/W55667492","https://openalex.org/W180909952","https://openalex.org/W1552239503","https://openalex.org/W1588927458","https://openalex.org/W1975675278","https://openalex.org/W1980089755","https://openalex.org/W2022006693","https://openalex.org/W2032860265","https://openalex.org/W2039465255","https://openalex.org/W2040648670","https://openalex.org/W2084072817","https://openalex.org/W2226829922","https://openalex.org/W2291266225","https://openalex.org/W2477674601","https://openalex.org/W2498149140","https://openalex.org/W2522623928","https://openalex.org/W2548244941","https://openalex.org/W2594444301","https://openalex.org/W2597372336","https://openalex.org/W2757471651","https://openalex.org/W2781562785","https://openalex.org/W2802320760","https://openalex.org/W2802785059","https://openalex.org/W2884603773","https://openalex.org/W2892412430","https://openalex.org/W2908058835","https://openalex.org/W2910401125","https://openalex.org/W2912577845","https://openalex.org/W2913797286","https://openalex.org/W2958786696","https://openalex.org/W2966757765","https://openalex.org/W2982109826","https://openalex.org/W2993085240","https://openalex.org/W2995227017","https://openalex.org/W2999124956","https://openalex.org/W2999145386","https://openalex.org/W3007066689","https://openalex.org/W3012955798","https://openalex.org/W3015163914","https://openalex.org/W3015898022","https://openalex.org/W3045663048","https://openalex.org/W3080346373","https://openalex.org/W3082640602","https://openalex.org/W3094452610","https://openalex.org/W3095388897","https://openalex.org/W3111055353","https://openalex.org/W3114898294","https://openalex.org/W3120130732","https://openalex.org/W3130713566","https://openalex.org/W3130860156","https://openalex.org/W3151864675","https://openalex.org/W3152348570","https://openalex.org/W3158129830","https://openalex.org/W3174944207","https://openalex.org/W3181153667","https://openalex.org/W4224226335","https://openalex.org/W4226356174","https://openalex.org/W4235035264","https://openalex.org/W4283765503"],"related_works":["https://openalex.org/W4388915157","https://openalex.org/W2801380751","https://openalex.org/W2460119088","https://openalex.org/W659185264","https://openalex.org/W3214162940","https://openalex.org/W2297410367","https://openalex.org/W2373042598","https://openalex.org/W4366832486","https://openalex.org/W2909342716","https://openalex.org/W2587576795"],"abstract_inverted_index":{"Abstract":[0],"Background":[1],"When":[2],"you":[3,8,18,24,26,29],"make":[4],"a":[5,33,36,43,88,113,170,220],"forex":[6,61,108,131],"transaction,":[7],"sell":[9],"one":[10,65],"currency":[11,17,23,57,315],"and":[12,28,73,121,133,159,193,218,252,265,289],"buy":[13,19],"another.":[14],"If":[15],"the":[16,22,40,60,67,94,100,107,130,163,212,215,223,238,242,257,262,270,292,298,312,331],"increases":[20],"against":[21],"sell,":[25],"profit,":[27],"do":[30],"this":[31,336],"through":[32],"broker":[34],"as":[35,46,338],"retail":[37,52],"trader":[38],"on":[39,261,302],"internet":[41],"using":[42],"platform":[44],"known":[45],"meta":[47],"trader.":[48],"Only":[49],"2%":[50],"of":[51,66,155,175,197,214,222,225,237,314],"traders":[53],"can":[54,140],"successfully":[55],"predict":[56,129],"movement":[58],"in":[59,83,162,297,311,324,335],"market,":[62,109],"making":[63],"it":[64],"most":[68,243,258,271],"challenging":[69],"tasks.":[70],"Machine":[71],"learning":[72,120,157,177,227,275,309],"its":[74],"derivatives":[75],"or":[76],"hybrid":[77],"models":[78,158,228],"are":[79,136,200,248,269],"becoming":[80],"increasingly":[81],"popular":[82],"market":[84,132,181,279],"forecasting,":[85],"which":[86],"is":[87,111,150],"rapidly":[89],"developing":[90],"field.":[91],"Objective":[92],"While":[93],"research":[95,184],"community":[96,294],"has":[97],"looked":[98,205],"into":[99,117,202],"methodologies":[101],"used":[102,127,273],"by":[103],"researchers":[104],"to":[105,115,128,143,151,285],"forecast":[106],"there":[110,135],"still":[112,317],"need":[114],"look":[116],"how":[118],"machine":[119,156,176,226,274,308],"artificial":[122],"intelligence":[123],"approaches":[124,310],"have":[125,318],"been":[126],"whether":[134],"any":[137],"areas":[138],"that":[139,188,241,291,307],"be":[141],"improved":[142],"allow":[144],"for":[145,179,277,320],"better":[146],"predictions.":[147],"Our":[148,183],"objective":[149],"give":[152],"an":[153],"overview":[154],"their":[160],"application":[161],"FX":[164,180,278],"market.":[165],"Method":[166],"This":[167],"study":[168],"provides":[169],"Systematic":[171],"Literature":[172],"Review":[173],"(SLR)":[174],"algorithms":[178,276],"forecasting.":[182],"looks":[185],"at":[186,206],"publications":[187],"were":[189],"published":[190],"between":[191],"2010":[192],"2021.":[194],"A":[195],"total":[196],"60":[198],"papers":[199],"taken":[201],"consideration.":[203],"We":[204],"them":[207],"from":[208],"two":[209],"angles:":[210],"I":[211],"design":[213],"evaluation":[216,230],"techniques,":[217],"(ii)":[219],"meta-analysis":[221],"performance":[224],"utilizing":[229],"metrics":[231,247],"thus":[232],"far.":[233],"Results":[234],"The":[235,281],"results":[236],"analysis":[239],"suggest":[240],"commonly":[244,272],"utilized":[245],"assessment":[246],"MAE,":[249],"RMSE,":[250],"MAPE,":[251],"MSE,":[253],"with":[254],"EURUSD":[255],"being":[256],"traded":[259],"pair":[260],"planet.":[263],"LSTM":[264],"Artificial":[266],"Neural":[267],"Network":[268],"prediction.":[280],"findings":[282],"also":[283],"point":[284],"many":[286],"unresolved":[287],"concerns":[288,333],"difficulties":[290],"scientific":[293],"should":[295],"address":[296],"future.":[299],"Conclusion":[300],"Based":[301],"our":[303],"findings,":[304],"we":[305],"believe":[306],"area":[313],"prediction":[316],"room":[319],"development.":[321],"Researchers":[322],"interested":[323],"creating":[325],"more":[326],"advanced":[327],"strategies":[328],"might":[329],"use":[330],"open":[332],"raised":[334],"work":[337],"input.":[339]},"counts_by_year":[{"year":2026,"cited_by_count":7},{"year":2025,"cited_by_count":31},{"year":2024,"cited_by_count":19},{"year":2023,"cited_by_count":9}],"updated_date":"2026-04-18T07:56:08.524223","created_date":"2025-10-10T00:00:00"}
