{"id":"https://openalex.org/W3093733835","doi":"https://doi.org/10.1145/3340531.3412738","title":"Price Forecast with High-Frequency Finance Data","display_name":"Price Forecast with High-Frequency Finance Data","publication_year":2020,"publication_date":"2020-10-19","ids":{"openalex":"https://openalex.org/W3093733835","doi":"https://doi.org/10.1145/3340531.3412738","mag":"3093733835"},"language":"en","primary_location":{"id":"doi:10.1145/3340531.3412738","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3340531.3412738","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 29th ACM International Conference on Information & Knowledge Management","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5009417833","display_name":"Yuechun Gu","orcid":null},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yuechun Gu","raw_affiliation_strings":["University of Electronic Science and Technology of China, Chengdu, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Electronic Science and Technology of China, Chengdu, China","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5070582509","display_name":"Da Yan","orcid":"https://orcid.org/0000-0002-4653-0408"},"institutions":[{"id":"https://openalex.org/I32389192","display_name":"University of Alabama at Birmingham","ror":"https://ror.org/008s83205","country_code":"US","type":"education","lineage":["https://openalex.org/I32389192"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Da Yan","raw_affiliation_strings":["University of Alabama at Birmingham, Birmingham, AL, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Alabama at Birmingham, Birmingham, AL, USA","institution_ids":["https://openalex.org/I32389192"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5020660716","display_name":"Sibo Yan","orcid":null},"institutions":[{"id":"https://openalex.org/I27103197","display_name":"Freddie Mac (United States)","ror":"https://ror.org/02ga7kh69","country_code":"US","type":"company","lineage":["https://openalex.org/I27103197"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sibo Yan","raw_affiliation_strings":["Freddie Mac, Washington, DC, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Freddie Mac, Washington, DC, USA","institution_ids":["https://openalex.org/I27103197"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5021681759","display_name":"Zhe Jiang","orcid":"https://orcid.org/0000-0002-3576-6976"},"institutions":[{"id":"https://openalex.org/I17301866","display_name":"University of Alabama","ror":"https://ror.org/03xrrjk67","country_code":"US","type":"education","lineage":["https://openalex.org/I17301866"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zhe Jiang","raw_affiliation_strings":["University of Alabama, Tuscaloosa, AL, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Alabama, Tuscaloosa, AL, USA","institution_ids":["https://openalex.org/I17301866"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5009417833"],"corresponding_institution_ids":["https://openalex.org/I150229711"],"apc_list":null,"apc_paid":null,"fwci":2.416,"has_fulltext":false,"cited_by_count":20,"citation_normalized_percentile":{"value":0.89669953,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"2485","last_page":"2492"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.9994999766349792,"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":0.9994999766349792,"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":0.9944000244140625,"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":0.9940000176429749,"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/margin","display_name":"Margin (machine learning)","score":0.7692952752113342},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6482388973236084},{"id":"https://openalex.org/keywords/autoregressive-conditional-heteroskedasticity","display_name":"Autoregressive conditional heteroskedasticity","score":0.5187347531318665},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.46922099590301514},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.46628236770629883},{"id":"https://openalex.org/keywords/binary-number","display_name":"Binary number","score":0.45318174362182617},{"id":"https://openalex.org/keywords/binary-classification","display_name":"Binary classification","score":0.4510015845298767},{"id":"https://openalex.org/keywords/big-data","display_name":"Big data","score":0.4303593933582306},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.4280076324939728},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.41557013988494873},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.39892908930778503},{"id":"https://openalex.org/keywords/finance","display_name":"Finance","score":0.33596715331077576},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.29251736402511597},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.22263574600219727},{"id":"https://openalex.org/keywords/economics","display_name":"Economics","score":0.20762771368026733},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.11145490407943726},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.09612253308296204},{"id":"https://openalex.org/keywords/volatility","display_name":"Volatility (finance)","score":0.09388050436973572}],"concepts":[{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.7692952752113342},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6482388973236084},{"id":"https://openalex.org/C23922673","wikidata":"https://www.wikidata.org/wiki/Q180752","display_name":"Autoregressive conditional heteroskedasticity","level":3,"score":0.5187347531318665},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.46922099590301514},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.46628236770629883},{"id":"https://openalex.org/C48372109","wikidata":"https://www.wikidata.org/wiki/Q3913","display_name":"Binary number","level":2,"score":0.45318174362182617},{"id":"https://openalex.org/C66905080","wikidata":"https://www.wikidata.org/wiki/Q17005494","display_name":"Binary classification","level":3,"score":0.4510015845298767},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.4303593933582306},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.4280076324939728},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.41557013988494873},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.39892908930778503},{"id":"https://openalex.org/C10138342","wikidata":"https://www.wikidata.org/wiki/Q43015","display_name":"Finance","level":1,"score":0.33596715331077576},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.29251736402511597},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.22263574600219727},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.20762771368026733},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.11145490407943726},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.09612253308296204},{"id":"https://openalex.org/C91602232","wikidata":"https://www.wikidata.org/wiki/Q756115","display_name":"Volatility (finance)","level":2,"score":0.09388050436973572},{"id":"https://openalex.org/C94375191","wikidata":"https://www.wikidata.org/wiki/Q11205","display_name":"Arithmetic","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3340531.3412738","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3340531.3412738","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 29th ACM International Conference on Information & Knowledge Management","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Partnerships for the goals","score":0.46000000834465027,"id":"https://metadata.un.org/sdg/17"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1836465849","https://openalex.org/W2064675550","https://openalex.org/W2095617175","https://openalex.org/W2607045400","https://openalex.org/W2734777338","https://openalex.org/W2845688424","https://openalex.org/W2885054548","https://openalex.org/W2889928394","https://openalex.org/W2903501216","https://openalex.org/W2912036663","https://openalex.org/W2921829327","https://openalex.org/W2964199361","https://openalex.org/W2974142446","https://openalex.org/W3037932933","https://openalex.org/W3095114851","https://openalex.org/W3124263521","https://openalex.org/W3207029408","https://openalex.org/W4247950230","https://openalex.org/W6802956262"],"related_works":["https://openalex.org/W4386259002","https://openalex.org/W3171520305","https://openalex.org/W1546989560","https://openalex.org/W3193043704","https://openalex.org/W3125011624","https://openalex.org/W1508631387","https://openalex.org/W2370917603","https://openalex.org/W2952760143","https://openalex.org/W3011239835","https://openalex.org/W3161989282"],"abstract_inverted_index":{"The":[0],"availability":[1],"of":[2,14,20],"high-frequency":[3],"trade":[4],"data":[5],"has":[6],"made":[7],"it":[8],"possible":[9],"for":[10],"the":[11,18,64],"intraday":[12],"forecast":[13],"price":[15,37],"patterns.":[16],"With":[17],"help":[19],"technical":[21],"indicators,":[22],"recent":[23],"studies":[24],"have":[25],"shown":[26],"that":[27,73],"LSTM":[28,77],"based":[29],"deep":[30],"learning":[31],"models":[32,78],"are":[33],"able":[34],"to":[35],"predict":[36],"directions":[38],"(a":[39],"binary":[40],"classification":[41],"problem)":[42],"with":[43,63],"performance":[44],"better":[45],"than":[46],"a":[47,80],"random":[48],"guess.":[49],"However,":[50],"only":[51],"naive":[52],"recurrent":[53],"networks":[54],"were":[55],"adopted,":[56],"and":[57],"these":[58],"works":[59],"did":[60],"not":[61],"compare":[62],"tools":[65],"used":[66],"by":[67,79],"finance":[68],"practitioners.":[69],"Our":[70],"experiments":[71],"show":[72],"GARCH":[74],"beats":[75],"their":[76],"large":[81],"margin.":[82]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":8},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":1}],"updated_date":"2026-05-05T08:41:31.759640","created_date":"2025-10-10T00:00:00"}