{"id":"https://openalex.org/W2803388957","doi":"https://doi.org/10.1007/s40595-018-0108-x","title":"Analyzing predictive performance of linear models on high-frequency currency exchange rates","display_name":"Analyzing predictive performance of linear models on high-frequency currency exchange rates","publication_year":2018,"publication_date":"2018-05-01","ids":{"openalex":"https://openalex.org/W2803388957","doi":"https://doi.org/10.1007/s40595-018-0108-x","mag":"2803388957"},"language":"en","primary_location":{"id":"doi:10.1007/s40595-018-0108-x","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s40595-018-0108-x","pdf_url":"https://link.springer.com/content/pdf/10.1007/s40595-018-0108-x.pdf","source":{"id":"https://openalex.org/S2486636169","display_name":"Vietnam Journal of Computer Science","issn_l":"2196-8888","issn":["2196-8888","2196-8896"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319815","host_organization_name":"World Scientific","host_organization_lineage":["https://openalex.org/P4310319815"],"host_organization_lineage_names":["World Scientific"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Vietnam Journal of Computer Science","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://link.springer.com/content/pdf/10.1007/s40595-018-0108-x.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5032281191","display_name":"Chanakya Serjam","orcid":"https://orcid.org/0000-0002-1610-2023"},"institutions":[{"id":"https://openalex.org/I203951103","display_name":"Keio University","ror":"https://ror.org/02kn6nx58","country_code":"JP","type":"education","lineage":["https://openalex.org/I203951103"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Chanakya Serjam","raw_affiliation_strings":["Graduate School of Science and Technology, Keio University, Yokohama, 223-8522, Japan"],"raw_orcid":"https://orcid.org/0000-0002-1610-2023","affiliations":[{"raw_affiliation_string":"Graduate School of Science and Technology, Keio University, Yokohama, 223-8522, Japan","institution_ids":["https://openalex.org/I203951103"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5113910315","display_name":"Akito Sakurai","orcid":null},"institutions":[{"id":"https://openalex.org/I203951103","display_name":"Keio University","ror":"https://ror.org/02kn6nx58","country_code":"JP","type":"education","lineage":["https://openalex.org/I203951103"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Akito Sakurai","raw_affiliation_strings":["Graduate School of Science and Technology, Keio University, Yokohama, 223-8522, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Graduate School of Science and Technology, Keio University, Yokohama, 223-8522, Japan","institution_ids":["https://openalex.org/I203951103"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5032281191"],"corresponding_institution_ids":["https://openalex.org/I203951103"],"apc_list":null,"apc_paid":null,"fwci":1.1006,"has_fulltext":true,"cited_by_count":10,"citation_normalized_percentile":{"value":0.80269411,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":"5","issue":"2","first_page":"123","last_page":"132"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.9997000098228455,"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":0.9997000098228455,"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":0.993399977684021,"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/T10007","display_name":"Monetary Policy and Economic Impact","score":0.9873999953269958,"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/currency","display_name":"Currency","score":0.7412675619125366},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.680671751499176},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.5773797035217285},{"id":"https://openalex.org/keywords/high-frequency-trading","display_name":"High-frequency trading","score":0.5615428686141968},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.334962397813797},{"id":"https://openalex.org/keywords/algorithmic-trading","display_name":"Algorithmic trading","score":0.32869231700897217},{"id":"https://openalex.org/keywords/economics","display_name":"Economics","score":0.18276727199554443},{"id":"https://openalex.org/keywords/financial-economics","display_name":"Financial economics","score":0.18006399273872375}],"concepts":[{"id":"https://openalex.org/C141121606","wikidata":"https://www.wikidata.org/wiki/Q8142","display_name":"Currency","level":2,"score":0.7412675619125366},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.680671751499176},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.5773797035217285},{"id":"https://openalex.org/C24683644","wikidata":"https://www.wikidata.org/wiki/Q138372","display_name":"High-frequency trading","level":3,"score":0.5615428686141968},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.334962397813797},{"id":"https://openalex.org/C78508483","wikidata":"https://www.wikidata.org/wiki/Q139445","display_name":"Algorithmic trading","level":2,"score":0.32869231700897217},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.18276727199554443},{"id":"https://openalex.org/C106159729","wikidata":"https://www.wikidata.org/wiki/Q2294553","display_name":"Financial economics","level":1,"score":0.18006399273872375},{"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:10.1007/s40595-018-0108-x","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s40595-018-0108-x","pdf_url":"https://link.springer.com/content/pdf/10.1007/s40595-018-0108-x.pdf","source":{"id":"https://openalex.org/S2486636169","display_name":"Vietnam Journal of Computer Science","issn_l":"2196-8888","issn":["2196-8888","2196-8896"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319815","host_organization_name":"World Scientific","host_organization_lineage":["https://openalex.org/P4310319815"],"host_organization_lineage_names":["World Scientific"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Vietnam Journal of Computer Science","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:dad2591684f04eeaa98fd4cf1962063e","is_oa":true,"landing_page_url":"https://doaj.org/article/dad2591684f04eeaa98fd4cf1962063e","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":"Vietnam Journal of Computer Science, Vol 5, Iss 2, Pp 123-132 (2018)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1007/s40595-018-0108-x","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s40595-018-0108-x","pdf_url":"https://link.springer.com/content/pdf/10.1007/s40595-018-0108-x.pdf","source":{"id":"https://openalex.org/S2486636169","display_name":"Vietnam Journal of Computer Science","issn_l":"2196-8888","issn":["2196-8888","2196-8896"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319815","host_organization_name":"World Scientific","host_organization_lineage":["https://openalex.org/P4310319815"],"host_organization_lineage_names":["World Scientific"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Vietnam Journal of Computer Science","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2803388957.pdf","grobid_xml":"https://content.openalex.org/works/W2803388957.grobid-xml"},"referenced_works_count":17,"referenced_works":["https://openalex.org/W160122124","https://openalex.org/W1505330957","https://openalex.org/W1988518729","https://openalex.org/W2012079387","https://openalex.org/W2018135333","https://openalex.org/W2031762450","https://openalex.org/W2064961783","https://openalex.org/W2074713408","https://openalex.org/W2084876223","https://openalex.org/W2119821739","https://openalex.org/W2134918899","https://openalex.org/W2137226992","https://openalex.org/W2139212933","https://openalex.org/W2158960338","https://openalex.org/W2166890233","https://openalex.org/W2170329425","https://openalex.org/W3125796404"],"related_works":["https://openalex.org/W2553031590","https://openalex.org/W2772541354","https://openalex.org/W2947561447","https://openalex.org/W2491678934","https://openalex.org/W2944959808","https://openalex.org/W2149348000","https://openalex.org/W2504476032","https://openalex.org/W2461261851","https://openalex.org/W3015595311","https://openalex.org/W4224329202"],"abstract_inverted_index":{"We":[0],"generate":[1],"a":[2,189,203],"large":[3],"number":[4,68,72,124],"of":[5,40,55,57,69,73,78,110,125,166,177],"predictive":[6],"models":[7,88,130,143,154,168,225],"by":[8,222],"applying":[9],"linear":[10],"kernel":[11],"SVR":[12],"to":[13,51,91,141,215],"historical":[14,146,213],"currency":[15,21,94],"rates\u2019":[16],"bid":[17,28,115],"data":[18,30,147,214],"for":[19,66,218],"three":[20],"pairs":[22],"obtained":[23],"from":[24],"high-frequency":[25],"trading.":[26],"The":[27,133],"tick":[29],"are":[31,47,81,89,131,153],"converted":[32],"into":[33],"equally":[34],"spaced":[35],"(1":[36],"min)":[37],"data.":[38],"Differences":[39],"price":[41,59],"between":[42],"the":[43,53,58,61,67,76,79,85,97,100,104,175,212,219,223,228],"previous":[44],"successive":[45],"timeframes":[46,80],"used":[48,82,90],"as":[49],"features":[50],"predict":[52],"direction":[54],"movement":[56],"in":[60,96,102,113],"next":[62],"timeframe.":[63],"Different":[64],"values":[65],"training":[70],"samples,":[71],"features,":[74],"and":[75,123,163,188,197,226],"length":[77],"when":[83,117],"learning":[84],"models.":[86],"These":[87],"conduct":[92],"simulated":[93],"trading":[95,207],"year":[98],"following":[99],"one":[101],"which":[103],"model":[105],"was":[106],"learned.":[107],"Profits":[108],"(sum":[109],"realized":[111],"differences":[112],"best":[114],"prices":[116],"order":[118],"is":[119,139],"executed),":[120],"hit":[121,199],"ratios,":[122],"trades":[126],"executed":[127],"using":[128,144],"these":[129,167,178],"recorded.":[132],"experiments":[134],"indicate":[135],"that":[136,148,155,164,182],"while":[137,193],"it":[138],"difficult":[140],"construct":[142],"only":[145],"consistently":[149],"perform":[150],"well,":[151],"there":[152],"show":[156],"good":[157,198],"performance":[158],"under":[159],"certain":[160],"pre-defined":[161],"conditions,":[162],"many":[165],"have":[169,184],"an":[170],"interesting":[171],"property.":[172],"Upon":[173],"examining":[174],"parameters":[176],"models,":[179],"we":[180,210],"discover":[181],"they":[183],"all":[185],"negative":[186],"coefficients":[187],"negligibly":[190],"small":[191],"intercept,":[192],"having":[194],"positive":[195],"profits":[196],"ratio.":[200],"This":[201],"suggests":[202],"simple":[204],"yet":[205],"effective":[206],"strategy.":[208],"Finally,":[209],"examine":[211],"find":[216],"corroboration":[217],"pattern":[220],"suggested":[221],"generated":[224],"present":[227],"results.":[229]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2023,"cited_by_count":3},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":3}],"updated_date":"2026-05-06T08:25:59.206177","created_date":"2025-10-10T00:00:00"}