{"id":"https://openalex.org/W3161865545","doi":"https://doi.org/10.1109/icassp39728.2021.9414893","title":"Stock Movement Prediction and Portfolio Management via Multimodal Learning with Transformer","display_name":"Stock Movement Prediction and Portfolio Management via Multimodal Learning with Transformer","publication_year":2021,"publication_date":"2021-05-13","ids":{"openalex":"https://openalex.org/W3161865545","doi":"https://doi.org/10.1109/icassp39728.2021.9414893","mag":"3161865545"},"language":"en","primary_location":{"id":"doi:10.1109/icassp39728.2021.9414893","doi":"https://doi.org/10.1109/icassp39728.2021.9414893","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp39728.2021.9414893","pdf_url":null,"source":{"id":"https://openalex.org/S4363607879","display_name":"IEEE International Conference on Acoustics Speech and Signal Processing","issn_l":"1520-6149","issn":["1520-6149","2379-190X"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":["jufo-2","norway-1"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","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/A5041936413","display_name":"Divyanshu Daiya","orcid":"https://orcid.org/0000-0002-3725-0527","observed_orcids":["https://orcid.org/0000-0002-3725-0527"]},"institutions":[{"id":"https://openalex.org/I33552525","display_name":"LNM Institute of Information Technology","ror":"https://ror.org/03jp7rg16","country_code":"IN","type":"education","lineage":["https://openalex.org/I33552525"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Divyanshu Daiya","raw_affiliation_strings":["The LNM Institute of Information Technology, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The LNM Institute of Information Technology, India","institution_ids":["https://openalex.org/I33552525"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101909683","display_name":"Che Lin","orcid":"https://orcid.org/0000-0002-8564-5793","observed_orcids":["https://orcid.org/0000-0002-8564-5793","https://orcid.org/0000-0002-4986-311X"]},"institutions":[{"id":"https://openalex.org/I16733864","display_name":"National Taiwan University","ror":"https://ror.org/05bqach95","country_code":"TW","type":"education","lineage":["https://openalex.org/I16733864"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Che Lin","raw_affiliation_strings":["National Taiwan University, Taiwan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National Taiwan University, Taiwan","institution_ids":["https://openalex.org/I16733864"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":8.0096,"has_fulltext":false,"cited_by_count":36,"citation_normalized_percentile":{"value":0.9826458,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":88,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"3305","last_page":"3309"},"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.0,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/stock-movement-prediction","display_name":"stock movement prediction","score":0.8420000076293945},{"id":"https://openalex.org/keywords/portfolio-management","display_name":"portfolio management","score":0.8830000162124634},{"id":"https://openalex.org/keywords/multimodal-learning","display_name":"multimodal learning","score":0.8880000114440918},{"id":"https://openalex.org/keywords/financial-indicators","display_name":"financial indicators","score":0.746999979019165},{"id":"https://openalex.org/keywords/news-data","display_name":"news data","score":0.7390000224113464},{"id":"https://openalex.org/keywords/transformer","display_name":"transformer models","score":0.6520000100135803},{"id":"https://openalex.org/keywords/dilated-causal-convolution","display_name":"dilated causal convolution","score":0.972000002861023},{"id":"https://openalex.org/keywords/deep-reinforcement-learning","display_name":"deep reinforcement learning","score":0.7820000052452087},{"id":"https://openalex.org/keywords/portfolio-optimization","display_name":"portfolio optimization","score":0.9929999709129333},{"id":"https://openalex.org/keywords/s-p-500","display_name":"S&P 500","score":0.8270000219345093},{"id":"https://openalex.org/keywords/sharpe-ratio","display_name":"Sharpe ratio","score":0.9120000004768372}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7072722315788269},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.6527225375175476},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5771444439888},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.5672203898429871},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4461626410484314},{"id":"https://openalex.org/C2780821815","wikidata":"https://www.wikidata.org/wiki/Q5340806","display_name":"Portfolio","level":2,"score":0.43853065371513367},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4093171954154968},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.11728259921073914},{"id":"https://openalex.org/C106159729","wikidata":"https://www.wikidata.org/wiki/Q2294553","display_name":"Financial economics","level":1,"score":0.0},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp39728.2021.9414893","doi":"https://doi.org/10.1109/icassp39728.2021.9414893","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp39728.2021.9414893","pdf_url":null,"source":{"id":"https://openalex.org/S4363607879","display_name":"IEEE International Conference on Acoustics Speech and Signal Processing","issn_l":"1520-6149","issn":["1520-6149","2379-190X"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":["jufo-2","norway-1"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":30,"referenced_works":["https://openalex.org/W2009591769","https://openalex.org/W2064240901","https://openalex.org/W2126267628","https://openalex.org/W2209610041","https://openalex.org/W2296438605","https://openalex.org/W2510046892","https://openalex.org/W2519091744","https://openalex.org/W2575002693","https://openalex.org/W2613328025","https://openalex.org/W2626778328","https://openalex.org/W2731083990","https://openalex.org/W2741908740","https://openalex.org/W2749651610","https://openalex.org/W2774559076","https://openalex.org/W2786055572","https://openalex.org/W2802023636","https://openalex.org/W2888160216","https://openalex.org/W2892035503","https://openalex.org/W2896060389","https://openalex.org/W2900880305","https://openalex.org/W2901225480","https://openalex.org/W2954731415","https://openalex.org/W2962949934","https://openalex.org/W2964110616","https://openalex.org/W2974846344","https://openalex.org/W3012223895","https://openalex.org/W3016032431","https://openalex.org/W3088934411","https://openalex.org/W4245898856","https://openalex.org/W4300936792"],"related_works":["https://openalex.org/W7171134271","https://openalex.org/W4409949247","https://openalex.org/W7160180955","https://openalex.org/W4399597473","https://openalex.org/W4416677103","https://openalex.org/W4406950652","https://openalex.org/W4405468132","https://openalex.org/W4285402398","https://openalex.org/W4386655756","https://openalex.org/W7222474227"],"abstract_inverted_index":{"This":[0],"paper":[1],"introduces":[2],"a":[3,89,110],"novel":[4],"high":[5],"performing":[6],"multimodal":[7,22,58],"deep":[8],"learning":[9],"architecture(Trans-DiCE)":[10],"for":[11,31,79,103,117],"stock":[12,85],"movement":[13],"prediction":[14,86],"utilizing":[15,115],"financial":[16],"indicators":[17],"and":[18,28,47,83,87,126],"news":[19],"data.":[20],"Our":[21],"architecture":[23],"uses":[24],"dilated":[25],"causal":[26],"convolutions":[27],"Transformer":[29,64],"blocks":[30],"feature":[32],"extraction":[33],"from":[34,93],"both":[35],"data":[36],"sources.":[37],"The":[38],"masked":[39],"multi-head":[40],"self-attention":[41],"layers":[42],"inside":[43],"Transformers":[44],"preserve":[45],"causality":[46],"improve":[48],"features":[49],"based":[50],"on":[51,123],"contextual":[52],"information.":[53],"To":[54],"integrate":[55],"the":[56,104,133,137],"derived":[57],"model":[59,71],"representations,":[60],"we":[61],"use":[62],"stacked":[63],"blocks.":[65],"We":[66,97,108],"show":[67],"empirically":[68],"that":[69],"our":[70,100],"performs":[72],"best":[73],"compared":[74],"to":[75,95],"state-of-the-art":[76],"baseline":[77],"methods":[78],"S&P":[80],"500":[81],"index":[82],"individual":[84],"provides":[88],"significant":[90],"3.45%":[91],"improvement":[92],"74.29%":[94],"77.74%.":[96],"also":[98],"demonstrate":[99],"model\u2019s":[101],"utility":[102],"Portfolio":[105,118,130],"Management":[106],"task.":[107],"propose":[109],"Deep":[111],"Reinforcement":[112],"Learning":[113],"Framework":[114],"Trans-DiCE":[116],"Optimization,":[119],"providing":[120],"noticeable":[121],"gain":[122],"Sharpe":[124],"Ratio":[125],"7.9%":[127],"increase":[128],"in":[129],"Value":[131],"over":[132],"existing":[134],"state":[135],"of":[136],"art":[138],"Models.":[139]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":11},{"year":2024,"cited_by_count":12},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":1}],"updated_date":"2026-10-29T09:56:32.521200","created_date":"2021-05-24T00:00:00"}