{"id":"https://openalex.org/W4405272567","doi":"https://doi.org/10.1109/cifer62890.2024.10772910","title":"Semantic Graph Learning for Trend Prediction from Long Financial Documents","display_name":"Semantic Graph Learning for Trend Prediction from Long Financial Documents","publication_year":2024,"publication_date":"2024-10-22","ids":{"openalex":"https://openalex.org/W4405272567","doi":"https://doi.org/10.1109/cifer62890.2024.10772910"},"language":"en","primary_location":{"id":"doi:10.1109/cifer62890.2024.10772910","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cifer62890.2024.10772910","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE Symposium on Computational Intelligence for Financial Engineering and Economics (CIFEr)","raw_type":"proceedings-article"},"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/A5046237209","display_name":"Bolun Xia","orcid":"https://orcid.org/0000-0001-6841-9017"},"institutions":[{"id":"https://openalex.org/I165799507","display_name":"Rensselaer Polytechnic Institute","ror":"https://ror.org/01rtyzb94","country_code":"US","type":"education","lineage":["https://openalex.org/I165799507"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Bolun Namir Xia","raw_affiliation_strings":["Rensselaer Polytechnic Institute,Troy,NY,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Rensselaer Polytechnic Institute,Troy,NY,USA","institution_ids":["https://openalex.org/I165799507"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079035190","display_name":"Aparna Gupta","orcid":"https://orcid.org/0000-0002-5275-7756"},"institutions":[{"id":"https://openalex.org/I165799507","display_name":"Rensselaer Polytechnic Institute","ror":"https://ror.org/01rtyzb94","country_code":"US","type":"education","lineage":["https://openalex.org/I165799507"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Aparna Gupta","raw_affiliation_strings":["Rensselaer Polytechnic Institute,Troy,NY,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Rensselaer Polytechnic Institute,Troy,NY,USA","institution_ids":["https://openalex.org/I165799507"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5019559411","display_name":"Mohammed J. Zaki","orcid":"https://orcid.org/0000-0003-4711-0234"},"institutions":[{"id":"https://openalex.org/I165799507","display_name":"Rensselaer Polytechnic Institute","ror":"https://ror.org/01rtyzb94","country_code":"US","type":"education","lineage":["https://openalex.org/I165799507"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mohammed J. Zaki","raw_affiliation_strings":["Rensselaer Polytechnic Institute,Troy,NY,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Rensselaer Polytechnic Institute,Troy,NY,USA","institution_ids":["https://openalex.org/I165799507"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I165799507"],"apc_list":null,"apc_paid":null,"fwci":2.0344,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.88680821,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.9908999800682068,"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":0.9908999800682068,"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/T13083","display_name":"Advanced Text Analysis Techniques","score":0.9502000212669373,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.923799991607666,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/computer-science","display_name":"Computer science","score":0.7053436040878296},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5353273749351501},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3621061444282532},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.32673823833465576},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.1676267683506012}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7053436040878296},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5353273749351501},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3621061444282532},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.32673823833465576},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.1676267683506012}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cifer62890.2024.10772910","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cifer62890.2024.10772910","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE Symposium on Computational Intelligence for Financial Engineering and Economics (CIFEr)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W4391913857","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W3204019825"],"abstract_inverted_index":{"The":[0],"advent":[1],"of":[2,56,157,177,182,187,196,201],"large":[3],"language":[4],"models":[5],"(LLMs)":[6],"has":[7,43],"initiated":[8],"much":[9],"research":[10],"into":[11],"their":[12],"various":[13,185],"financial":[14,99,122,142,168],"applications.":[15],"However,":[16],"in":[17,46,97,140,161,184,203,221,231],"applying":[18],"LLMs":[19,217],"on":[20,174,193,219],"long":[21,121,167],"documents,":[22],"semantic":[23,61,67],"relations":[24],"are":[25,172],"not":[26],"explicitly":[27],"incorporated,":[28],"and":[29,153,243],"a":[30,53,70,145,151,175,194],"full":[31],"or":[32],"arbitrarily":[33],"sparse":[34],"attention":[35],"operation":[36],"is":[37,52],"employed.":[38],"In":[39],"recent":[40,198],"years,":[41],"progress":[42],"been":[44],"made":[45],"Abstract":[47],"Meaning":[48],"Representation":[49],"(AMR),":[50],"which":[51],"graph-based":[54],"representation":[55],"text":[57,220,246],"to":[58,93,116],"preserve":[59],"its":[60],"relations.":[62],"Since":[63],"AMR":[64,112,131],"can":[65,74],"represent":[66],"relationships":[68],"at":[69,227],"deeper":[71],"level,":[72],"it":[73],"be":[75],"beneficially":[76],"utilized":[77],"by":[78],"graph":[79,87,113],"neural":[80],"networks":[81],"(GNNs)":[82],"for":[83,120,245],"constructing":[84],"effective":[85],"document-level":[86,118,127],"representations":[88],"built":[89],"upon":[90],"LLM":[91,137],"embeddings":[92,119,139],"predict":[94],"target":[95,164],"metrics":[96],"the":[98,141,155,188,204],"domain.":[100],"We":[101,125,209],"propose":[102],"FLAG:":[103],"Financial":[104],"Long":[105],"document":[106,123,241],"classification":[107],"via":[108],"AMR-based":[109,159,213],"GNN,":[110,152],"an":[111],"based":[114],"framework":[115],"generate":[117],"classification.":[124,247],"construct":[126],"graphs":[128,242],"from":[129,166],"sentence-level":[130],"graphs,":[132],"endow":[133],"them":[134],"with":[135],"specialized":[136],"word":[138],"domain,":[143],"apply":[144],"deep":[146],"learning":[147],"mechanism":[148],"that":[149,211],"utilizes":[150],"examine":[154],"efficacy":[156],"our":[158,212],"approach":[160,214],"predicting":[162,222],"labeled":[163],"data":[165],"documents.":[169],"Extensive":[170],"experiments":[171],"conducted":[173],"dataset":[176],"quarterly":[178],"earnings":[179,199],"calls":[180,200],"transcripts":[181],"companies":[183,202],"sectors":[186],"economy,":[189],"as":[190,192],"well":[191],"corpus":[195],"more":[197],"S&P":[205],"1500":[206],"Composite":[207],"Index.":[208],"find":[210],"outperforms":[215,237],"fine-tuning":[216],"directly":[218],"stock":[223],"price":[224],"movement":[225],"trends":[226],"different":[228],"time":[229],"horizons":[230],"both":[232],"datasets.":[233],"Our":[234],"work":[235,239],"also":[236],"previous":[238],"utilizing":[240],"GNNs":[244]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}