{"id":"https://openalex.org/W3000530599","doi":"https://doi.org/10.1145/3341161.3344822","title":"Artificial intelligence for ETF market prediction and portfolio optimization","display_name":"Artificial intelligence for ETF market prediction and portfolio optimization","publication_year":2019,"publication_date":"2019-08-27","ids":{"openalex":"https://openalex.org/W3000530599","doi":"https://doi.org/10.1145/3341161.3344822","mag":"3000530599"},"language":"en","primary_location":{"id":"doi:10.1145/3341161.3344822","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3341161.3344822","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining","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/A5055245450","display_name":"Min-Yuh Day","orcid":"https://orcid.org/0000-0001-6213-5646"},"institutions":[{"id":"https://openalex.org/I107470533","display_name":"Tamkang University","ror":"https://ror.org/04tft4718","country_code":"TW","type":"education","lineage":["https://openalex.org/I107470533"]}],"countries":["TW"],"is_corresponding":true,"raw_author_name":"Min-Yuh Day","raw_affiliation_strings":["Tamkang University, Taiwan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tamkang University, Taiwan","institution_ids":["https://openalex.org/I107470533"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5045125397","display_name":"Jian-Ting Lin","orcid":null},"institutions":[{"id":"https://openalex.org/I107470533","display_name":"Tamkang University","ror":"https://ror.org/04tft4718","country_code":"TW","type":"education","lineage":["https://openalex.org/I107470533"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Jian-Ting Lin","raw_affiliation_strings":["Tamkang University, Taiwan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tamkang University, Taiwan","institution_ids":["https://openalex.org/I107470533"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5055245450"],"corresponding_institution_ids":["https://openalex.org/I107470533"],"apc_list":null,"apc_paid":null,"fwci":1.6462,"has_fulltext":false,"cited_by_count":30,"citation_normalized_percentile":{"value":0.85793498,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1026","last_page":"1033"},"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/T10047","display_name":"Financial Markets and Investment Strategies","score":0.9954000115394592,"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/T11059","display_name":"Market Dynamics and Volatility","score":0.9876999855041504,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/portfolio-optimization","display_name":"Portfolio optimization","score":0.7664008736610413},{"id":"https://openalex.org/keywords/portfolio","display_name":"Portfolio","score":0.7112387418746948},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6726068258285522},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6684190034866333},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6594036817550659},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6504453420639038},{"id":"https://openalex.org/keywords/asset-allocation","display_name":"Asset allocation","score":0.5851789116859436},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5777710676193237},{"id":"https://openalex.org/keywords/black\u2013litterman-model","display_name":"Black\u2013Litterman model","score":0.5206719636917114},{"id":"https://openalex.org/keywords/modern-portfolio-theory","display_name":"Modern portfolio theory","score":0.42355653643608093},{"id":"https://openalex.org/keywords/replicating-portfolio","display_name":"Replicating portfolio","score":0.18327605724334717},{"id":"https://openalex.org/keywords/finance","display_name":"Finance","score":0.15971529483795166},{"id":"https://openalex.org/keywords/economics","display_name":"Economics","score":0.155195951461792}],"concepts":[{"id":"https://openalex.org/C202655437","wikidata":"https://www.wikidata.org/wiki/Q7231728","display_name":"Portfolio optimization","level":3,"score":0.7664008736610413},{"id":"https://openalex.org/C2780821815","wikidata":"https://www.wikidata.org/wiki/Q5340806","display_name":"Portfolio","level":2,"score":0.7112387418746948},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6726068258285522},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6684190034866333},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6594036817550659},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6504453420639038},{"id":"https://openalex.org/C139819358","wikidata":"https://www.wikidata.org/wiki/Q462748","display_name":"Asset allocation","level":3,"score":0.5851789116859436},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5777710676193237},{"id":"https://openalex.org/C77913304","wikidata":"https://www.wikidata.org/wiki/Q879967","display_name":"Black\u2013Litterman model","level":5,"score":0.5206719636917114},{"id":"https://openalex.org/C9725762","wikidata":"https://www.wikidata.org/wiki/Q1072885","display_name":"Modern portfolio theory","level":3,"score":0.42355653643608093},{"id":"https://openalex.org/C21099588","wikidata":"https://www.wikidata.org/wiki/Q7314068","display_name":"Replicating portfolio","level":4,"score":0.18327605724334717},{"id":"https://openalex.org/C10138342","wikidata":"https://www.wikidata.org/wiki/Q43015","display_name":"Finance","level":1,"score":0.15971529483795166},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.155195951461792},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3341161.3344822","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3341161.3344822","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure","score":0.47999998927116394}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W1550206324","https://openalex.org/W1930624869","https://openalex.org/W1979575715","https://openalex.org/W1989285461","https://openalex.org/W2064675550","https://openalex.org/W2119821739","https://openalex.org/W2131426928","https://openalex.org/W2149706766","https://openalex.org/W2156387975","https://openalex.org/W2158342889","https://openalex.org/W2161608691","https://openalex.org/W2163605009","https://openalex.org/W2295598076","https://openalex.org/W2313953460","https://openalex.org/W2557283755","https://openalex.org/W2618530766","https://openalex.org/W2762041969","https://openalex.org/W2798058877","https://openalex.org/W2883692332","https://openalex.org/W2919115771","https://openalex.org/W3102476541","https://openalex.org/W3139831614","https://openalex.org/W4236137412","https://openalex.org/W4239510810","https://openalex.org/W4251212826"],"related_works":["https://openalex.org/W4242666684","https://openalex.org/W3144753570","https://openalex.org/W2221832056","https://openalex.org/W2342245948","https://openalex.org/W2111724235","https://openalex.org/W2316055288","https://openalex.org/W4327779378","https://openalex.org/W4206877017","https://openalex.org/W4225528185","https://openalex.org/W2367990330"],"abstract_inverted_index":{"In":[0],"asset":[1,33],"allocation":[2],"and":[3,17,31,48,53,83,92,99,113,120,144,154,183,205],"time-series":[4],"forecasting":[5,51,56],"studies,":[6],"few":[7],"have":[8],"shed":[9],"light":[10],"on":[11,88],"using":[12,109],"the":[13,23,26,55,59,104,117,125,133,141,157,161,166,169,179,185,189,206,210],"different":[14,45,152],"machine":[15,46,79,97],"learning":[16,19,47,50,98,101,176],"deep":[18,49,100,175],"models":[20],"to":[21,63,115,128,193],"verify":[22],"difference":[24],"in":[25,67],"result":[27,57,167,187],"of":[28,58,75,160,168,196,212],"investment":[29,142],"returns":[30],"optimal":[32],"allocation.":[34],"To":[35],"fill":[36],"this":[37],"research":[38,71],"gap,":[39],"we":[40,138],"develop":[41],"a":[42,146],"robo-advisor":[43,90],"with":[44,103,178,188,209],"methodologies":[52],"utilize":[54],"portfolio":[60,84,105,190,208],"optimization":[61,106,191],"model":[62,162,177],"support":[64],"our":[65,110,130,174,201],"investors":[66],"making":[68],"decisions.":[69],"This":[70],"integrated":[72],"several":[73],"dimensions":[74],"technologies,":[76],"which":[77,199],"contain":[78],"learning,":[80],"data":[81,119],"analytics,":[82],"optimization.":[85],"We":[86,123,150,172],"focused":[87],"developing":[89],"framework":[91],"utilized":[93],"algorithms":[94,153],"by":[95,108],"integrating":[96],"approaches":[102],"algorithm":[107,192],"predicted":[111],"trends":[112],"results":[114],"replace":[116],"historical":[118,213],"investor":[121],"views.":[122],"eliminate":[124],"extreme":[126],"fluctuation":[127],"maintain":[129],"trading":[131],"within":[132],"acceptable":[134],"risk":[135,143],"coefficient.":[136],"Accordingly,":[137],"can":[139],"minimize":[140],"reach":[145,194],"relatively":[147],"stable":[148],"return.":[149],"compared":[151],"found":[155],"that":[156],"F1":[158],"score":[159],"prediction":[163,186],"significantly":[164],"affects":[165],"optimized":[170,207],"portfolio.":[171],"used":[173],"highest":[180],"winning":[181],"rate":[182],"leveraged":[184],"12%":[195],"annual":[197],"return,":[198],"outperform":[200],"benchmark":[202],"index":[203],"0050.TW":[204],"integration":[211],"data.":[214]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":7},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":2}],"updated_date":"2026-05-13T08:25:38.343686","created_date":"2025-10-10T00:00:00"}