{"id":"https://openalex.org/W7117108941","doi":"https://doi.org/10.48550/arxiv.2512.19484","title":"Structured Event Representation and Stock Return Predictability","display_name":"Structured Event Representation and Stock Return Predictability","publication_year":2025,"publication_date":"2025-12-22","ids":{"openalex":"https://openalex.org/W7117108941","doi":"https://doi.org/10.48550/arxiv.2512.19484"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2512.19484","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2512.19484","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2512.19484","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5121210264","display_name":"Gang Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Gang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5121194545","display_name":"Dandan Qiao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Qiao, Dandan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5121124239","display_name":"Mingxuan Zheng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zheng, Mingxuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.8777999877929688,"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":0.8777999877929688,"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":0.03519999980926514,"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/T11653","display_name":"Financial Distress and Bankruptcy Prediction","score":0.008999999612569809,"subfield":{"id":"https://openalex.org/subfields/1402","display_name":"Accounting"},"field":{"id":"https://openalex.org/fields/14","display_name":"Business, Management and Accounting"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/predictability","display_name":"Predictability","score":0.7846999764442444},{"id":"https://openalex.org/keywords/stock","display_name":"Stock (firearms)","score":0.6262999773025513},{"id":"https://openalex.org/keywords/event-study","display_name":"Event study","score":0.4016999900341034},{"id":"https://openalex.org/keywords/event","display_name":"Event (particle physics)","score":0.3709000051021576},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.3255000114440918}],"concepts":[{"id":"https://openalex.org/C197640229","wikidata":"https://www.wikidata.org/wiki/Q2534066","display_name":"Predictability","level":2,"score":0.7846999764442444},{"id":"https://openalex.org/C204036174","wikidata":"https://www.wikidata.org/wiki/Q909380","display_name":"Stock (firearms)","level":2,"score":0.6262999773025513},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5999000072479248},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.47360000014305115},{"id":"https://openalex.org/C12958728","wikidata":"https://www.wikidata.org/wiki/Q1002530","display_name":"Event study","level":3,"score":0.4016999900341034},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4004000127315521},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.38519999384880066},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.3709000051021576},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.3255000114440918},{"id":"https://openalex.org/C2780299701","wikidata":"https://www.wikidata.org/wiki/Q475000","display_name":"Stock market","level":3,"score":0.2849999964237213},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2815999984741211}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2512.19484","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2512.19484","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2512.19484","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2512.19484","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"We":[0,86],"find":[1],"that":[2],"event":[3,24,39],"features":[4,25],"extracted":[5],"by":[6],"large":[7],"language":[8],"models":[9,63],"(LLMs)":[10],"are":[11],"effective":[12],"for":[13],"text-based":[14],"stock":[15,47,66,83,104],"return":[16,84,105],"prediction.":[17],"Using":[18],"a":[19,31],"pre-trained":[20],"LLM":[21],"to":[22,45,64,77],"extract":[23],"from":[26],"news":[27],"articles,":[28],"we":[29],"propose":[30],"novel":[32],"deep":[33],"learning":[34],"model":[35,54,101],"based":[36,91],"on":[37,92],"structured":[38,100],"representation":[40],"(SER)":[41],"and":[42,71,94],"attention":[43],"mechanisms":[44,80],"predict":[46],"returns":[48,67],"in":[49,103],"the":[50,79,82,96],"cross-section.":[51],"Our":[52],"SER-based":[53],"provides":[55],"superior":[56],"performance":[57],"compared":[58],"with":[59],"other":[60],"existing":[61],"text-driven":[62],"forecast":[65],"out":[68],"of":[69,99],"sample":[70],"offers":[72],"highly":[73],"interpretable":[74],"feature":[75],"structures":[76],"examine":[78],"underlying":[81],"predictability.":[85,106],"further":[87],"provide":[88],"various":[89],"implications":[90],"SER":[93],"highlight":[95],"crucial":[97],"benefit":[98],"inputs":[102]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2025-12-24T00:00:00"}
