{"id":"https://openalex.org/W4388994191","doi":"https://doi.org/10.1145/3604237.3626862","title":"Modeling Momentum Spillover with Economic Links Discovered from Financial Documents","display_name":"Modeling Momentum Spillover with Economic Links Discovered from Financial Documents","publication_year":2023,"publication_date":"2023-11-25","ids":{"openalex":"https://openalex.org/W4388994191","doi":"https://doi.org/10.1145/3604237.3626862"},"language":"en","primary_location":{"id":"doi:10.1145/3604237.3626862","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3604237.3626862","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3604237.3626862","source":null,"license":"cc-by-nc-sa","license_id":"https://openalex.org/licenses/cc-by-nc-sa","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"4th ACM International Conference on AI in Finance","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3604237.3626862","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5018674878","display_name":"Andy Chung","orcid":"https://orcid.org/0009-0009-2446-7506"},"institutions":[{"id":"https://openalex.org/I74801974","display_name":"The University of Tokyo","ror":"https://ror.org/057zh3y96","country_code":"JP","type":"education","lineage":["https://openalex.org/I74801974"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Andy Chung","raw_affiliation_strings":["Department of Advanced Interdisciplinary Studies, Graduate School of Engineering, The University of Tokyo, JP"],"raw_orcid":"https://orcid.org/0009-0009-2446-7506","affiliations":[{"raw_affiliation_string":"Department of Advanced Interdisciplinary Studies, Graduate School of Engineering, The University of Tokyo, JP","institution_ids":["https://openalex.org/I74801974"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5073822077","display_name":"Kumiko Tanaka\u2010Ishii","orcid":"https://orcid.org/0000-0003-1752-3951"},"institutions":[{"id":"https://openalex.org/I150744194","display_name":"Waseda University","ror":"https://ror.org/00ntfnx83","country_code":"JP","type":"education","lineage":["https://openalex.org/I150744194"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Kumiko Tanaka-Ishii","raw_affiliation_strings":["Department of Computer Science and Engineering, School of Fundamental Science and Engineering, Waseda University, JP"],"raw_orcid":"https://orcid.org/0000-0003-1752-3951","affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, School of Fundamental Science and Engineering, Waseda University, JP","institution_ids":["https://openalex.org/I150744194"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":2,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"490","last_page":"497"},"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/T10047","display_name":"Financial Markets and Investment Strategies","score":0.9991999864578247,"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/T11270","display_name":"Complex Systems and Time Series Analysis","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"}}],"keywords":[{"id":"https://openalex.org/keywords/spillover-effect","display_name":"Spillover effect","score":0.6261650323867798},{"id":"https://openalex.org/keywords/momentum","display_name":"Momentum (technical analysis)","score":0.5206702947616577},{"id":"https://openalex.org/keywords/business","display_name":"Business","score":0.36715593934059143},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.35065045952796936},{"id":"https://openalex.org/keywords/economics","display_name":"Economics","score":0.33378055691719055},{"id":"https://openalex.org/keywords/macroeconomics","display_name":"Macroeconomics","score":0.17150157690048218},{"id":"https://openalex.org/keywords/finance","display_name":"Finance","score":0.171202152967453}],"concepts":[{"id":"https://openalex.org/C55527203","wikidata":"https://www.wikidata.org/wiki/Q334194","display_name":"Spillover effect","level":2,"score":0.6261650323867798},{"id":"https://openalex.org/C60718061","wikidata":"https://www.wikidata.org/wiki/Q1414747","display_name":"Momentum (technical analysis)","level":2,"score":0.5206702947616577},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.36715593934059143},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.35065045952796936},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.33378055691719055},{"id":"https://openalex.org/C139719470","wikidata":"https://www.wikidata.org/wiki/Q39680","display_name":"Macroeconomics","level":1,"score":0.17150157690048218},{"id":"https://openalex.org/C10138342","wikidata":"https://www.wikidata.org/wiki/Q43015","display_name":"Finance","level":1,"score":0.171202152967453}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3604237.3626862","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3604237.3626862","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3604237.3626862","source":null,"license":"cc-by-nc-sa","license_id":"https://openalex.org/licenses/cc-by-nc-sa","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"4th ACM International Conference on AI in Finance","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3604237.3626862","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3604237.3626862","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3604237.3626862","source":null,"license":"cc-by-nc-sa","license_id":"https://openalex.org/licenses/cc-by-nc-sa","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"4th ACM International Conference on AI in Finance","raw_type":"proceedings-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","score":0.49000000953674316,"display_name":"Industry, innovation and infrastructure"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4388994191.pdf"},"referenced_works_count":15,"referenced_works":["https://openalex.org/W1998677466","https://openalex.org/W2125520394","https://openalex.org/W2164320228","https://openalex.org/W2250539671","https://openalex.org/W2332262482","https://openalex.org/W2898654780","https://openalex.org/W2963026768","https://openalex.org/W3016473712","https://openalex.org/W3122311160","https://openalex.org/W3123897291","https://openalex.org/W3175058599","https://openalex.org/W3186237585","https://openalex.org/W4229911693","https://openalex.org/W4288253152","https://openalex.org/W4385245566"],"related_works":["https://openalex.org/W2748952813","https://openalex.org/W4391375266","https://openalex.org/W3174705069","https://openalex.org/W2364548092","https://openalex.org/W2014368031","https://openalex.org/W2144168794","https://openalex.org/W3122379543","https://openalex.org/W2361421218","https://openalex.org/W2351205649","https://openalex.org/W2349149235"],"abstract_inverted_index":{"Momentum":[0],"spillover":[1,24],"is":[2],"a":[3,22],"market":[4],"anomaly":[5],"well-acknowledged":[6,165],"in":[7,75,126],"finance":[8],"literature.":[9],"This":[10],"paper":[11],"proposes":[12],"using":[13,39],"novel":[14],"economic":[15,35,123,146],"links":[16,36,124],"discovered":[17,125],"from":[18,47,71,149,154],"financial":[19],"documents":[20],"as":[21],"momentum":[23,92,119,141],"channel,":[25],"followed":[26],"by":[27],"modeling":[28,140],"with":[29,43],"graph":[30,110],"attention":[31],"networks.":[32],"These":[33],"text-based":[34],"are":[37,137],"constructed":[38,148],"contextual":[40],"embeddings":[41],"extracted":[42],"pre-trained":[44],"language":[45],"models":[46],"various":[48],"sections":[49,128],"of":[50,62,89,129],"company":[51,130],"annual":[52,131],"reports":[53,132],"and":[54,94,133,152],"earnings":[55,134,155],"call":[56,135,156],"transcripts.":[57],"We":[58,80],"examine":[59],"the":[60,76,86,116,145,159,164],"effectiveness":[61],"our":[63,82,108],"proposed":[64,83,109],"methods":[65],"based":[66,98],"on":[67,99],"point-in-time":[68],"S&P500":[69],"constituents":[70],"2010/01/01":[72],"to":[73],"2022/12/31":[74],"US":[77],"stock":[78],"market.":[79],"compare":[81],"model":[84,113],"against":[85],"mean":[87],"aggregator":[88],"peer":[90,117],"firms\u2019":[91],"baseline":[93],"Monte":[95],"Carlo":[96],"experiments":[97],"randomized":[100],"nodes":[101],"or":[102],"edges.":[103],"Our":[104],"results":[105],"show":[106],"that":[107],"neural":[111],"network":[112],"significantly":[114],"outperforms":[115,158],"firm\u2019s":[118],"aggregation":[120],"baseline.":[121],"Furthermore,":[122],"some":[127],"transcripts":[136,157],"useful":[138],"for":[139],"spillover.":[142],"In":[143],"particular,":[144],"link":[147],"management":[150],"discussion":[151],"analysis":[153],"industry":[160,166],"link,":[161],"which":[162],"represents":[163],"momentum.":[167]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}