{"id":"https://openalex.org/W7106021735","doi":"https://doi.org/10.1109/access.2025.3634551","title":"Interpretable Fair Value Hierarchy Classification via Hybrid Transformer-GNN Architecture","display_name":"Interpretable Fair Value Hierarchy Classification via Hybrid Transformer-GNN Architecture","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W7106021735","doi":"https://doi.org/10.1109/access.2025.3634551"},"language":"en","primary_location":{"id":"doi:10.1109/access.2025.3634551","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3634551","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1109/access.2025.3634551","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Bingying Jiang","orcid":"https://orcid.org/0009-0007-8678-1598"},"institutions":[{"id":"https://openalex.org/I135310074","display_name":"University of Wisconsin\u2013Madison","ror":"https://ror.org/01y2jtd41","country_code":"US","type":"education","lineage":["https://openalex.org/I135310074"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Bingying Jiang","raw_affiliation_strings":["University of Wisconsin–Madison, Madison, WI, USA"],"raw_orcid":"https://orcid.org/0009-0007-8678-1598","affiliations":[{"raw_affiliation_string":"University of Wisconsin–Madison, Madison, WI, USA","institution_ids":["https://openalex.org/I135310074"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Bi Wu","orcid":"https://orcid.org/0009-0002-4503-8259"},"institutions":[{"id":"https://openalex.org/I161318765","display_name":"University of California, Los Angeles","ror":"https://ror.org/046rm7j60","country_code":"US","type":"education","lineage":["https://openalex.org/I161318765"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Bi Wu","raw_affiliation_strings":["University of California, Los Angeles, CA, USA"],"raw_orcid":"https://orcid.org/0009-0002-4503-8259","affiliations":[{"raw_affiliation_string":"University of California, Los Angeles, CA, USA","institution_ids":["https://openalex.org/I161318765"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Jialei Cao","orcid":"https://orcid.org/0009-0001-9451-7494"},"institutions":[{"id":"https://openalex.org/I79576946","display_name":"University of Pennsylvania","ror":"https://ror.org/00b30xv10","country_code":"US","type":"education","lineage":["https://openalex.org/I79576946"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jialei Cao","raw_affiliation_strings":["University of Pennsylvania, Philadelphia, PA, USA"],"raw_orcid":"https://orcid.org/0009-0001-9451-7494","affiliations":[{"raw_affiliation_string":"University of Pennsylvania, Philadelphia, PA, USA","institution_ids":["https://openalex.org/I79576946"]}]},{"author_position":"last","author":{"id":null,"display_name":"Yutong Tan","orcid":"https://orcid.org/0009-0006-4535-4736"},"institutions":[{"id":"https://openalex.org/I47251452","display_name":"Wake Forest University","ror":"https://ror.org/0207ad724","country_code":"US","type":"education","lineage":["https://openalex.org/I47251452"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yutong Tan","raw_affiliation_strings":["Wake Forest University, Winston-Salem, NC, USA"],"raw_orcid":"https://orcid.org/0009-0006-4535-4736","affiliations":[{"raw_affiliation_string":"Wake Forest University, Winston-Salem, NC, USA","institution_ids":["https://openalex.org/I47251452"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I135310074"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":6.6048,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.96762686,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":99},"biblio":{"volume":"13","issue":null,"first_page":"198142","last_page":"198163"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10081","display_name":"Auditing, Earnings Management, Governance","score":0.35989999771118164,"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"}},"topics":[{"id":"https://openalex.org/T10081","display_name":"Auditing, Earnings Management, Governance","score":0.35989999771118164,"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"}},{"id":"https://openalex.org/T11653","display_name":"Financial Distress and Bankruptcy Prediction","score":0.21539999544620514,"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"}},{"id":"https://openalex.org/T13794","display_name":"Financial Reporting and XBRL","score":0.11289999634027481,"subfield":{"id":"https://openalex.org/subfields/1404","display_name":"Management Information Systems"},"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/interpretability","display_name":"Interpretability","score":0.8607000112533569},{"id":"https://openalex.org/keywords/valuation","display_name":"Valuation (finance)","score":0.5440999865531921},{"id":"https://openalex.org/keywords/categorization","display_name":"Categorization","score":0.5109999775886536},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.5},{"id":"https://openalex.org/keywords/generality","display_name":"Generality","score":0.4510999917984009},{"id":"https://openalex.org/keywords/architecture","display_name":"Architecture","score":0.397599995136261},{"id":"https://openalex.org/keywords/audit","display_name":"Audit","score":0.3910999894142151},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.39010000228881836},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.38580000400543213}],"concepts":[{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.8607000112533569},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7218000292778015},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6144000291824341},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5651000142097473},{"id":"https://openalex.org/C186027771","wikidata":"https://www.wikidata.org/wiki/Q4008379","display_name":"Valuation (finance)","level":2,"score":0.5440999865531921},{"id":"https://openalex.org/C94124525","wikidata":"https://www.wikidata.org/wiki/Q912550","display_name":"Categorization","level":2,"score":0.5109999775886536},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.5},{"id":"https://openalex.org/C2780767217","wikidata":"https://www.wikidata.org/wiki/Q5532421","display_name":"Generality","level":2,"score":0.4510999917984009},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.42800000309944153},{"id":"https://openalex.org/C123657996","wikidata":"https://www.wikidata.org/wiki/Q12271","display_name":"Architecture","level":2,"score":0.397599995136261},{"id":"https://openalex.org/C199521495","wikidata":"https://www.wikidata.org/wiki/Q181487","display_name":"Audit","level":2,"score":0.3910999894142151},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.39010000228881836},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.38580000400543213},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.36059999465942383},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.35760000348091125},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.33649998903274536},{"id":"https://openalex.org/C202552767","wikidata":"https://www.wikidata.org/wiki/Q1901350","display_name":"Market value","level":2,"score":0.3276999890804291},{"id":"https://openalex.org/C191654821","wikidata":"https://www.wikidata.org/wiki/Q247506","display_name":"Financial instrument","level":2,"score":0.2874999940395355},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.2856000065803528},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.28189998865127563},{"id":"https://openalex.org/C58166","wikidata":"https://www.wikidata.org/wiki/Q224821","display_name":"Fuzzy logic","level":2,"score":0.2791999876499176},{"id":"https://openalex.org/C19244329","wikidata":"https://www.wikidata.org/wiki/Q208697","display_name":"Financial market","level":2,"score":0.2718000113964081},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.2694999873638153},{"id":"https://openalex.org/C106937863","wikidata":"https://www.wikidata.org/wiki/Q7236518","display_name":"Power graph analysis","level":3,"score":0.2671000063419342},{"id":"https://openalex.org/C177774035","wikidata":"https://www.wikidata.org/wiki/Q1246948","display_name":"Granularity","level":2,"score":0.2667999863624573},{"id":"https://openalex.org/C2781121602","wikidata":"https://www.wikidata.org/wiki/Q3504403","display_name":"Modular neural network","level":4,"score":0.2563999891281128},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.25519999861717224},{"id":"https://openalex.org/C114118609","wikidata":"https://www.wikidata.org/wiki/Q3036837","display_name":"Market data","level":2,"score":0.25450000166893005},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.25209999084472656}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2025.3634551","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3634551","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:300900340ed245a7acd82c635aca88b2","is_oa":true,"landing_page_url":"https://doaj.org/article/300900340ed245a7acd82c635aca88b2","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 13, Pp 198142-198163 (2025)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2025.3634551","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3634551","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Fair":[0],"value":[1],"hierarchy":[2],"classification":[3,37,141],"under":[4],"IFRS":[5],"13":[6],"presents":[7],"a":[8,51],"significant":[9],"challenge":[10],"in":[11,82],"financial":[12,18,199],"reporting,":[13],"requiring":[14],"accurate":[15],"categorization":[16],"of":[17,130,186],"instruments":[19,162],"into":[20],"Level":[21,26,32,160],"1":[22],"(directly":[23],"observable":[24,29],"prices),":[25],"2":[27],"(indirectly":[28],"inputs),":[30],"or":[31],"3":[33,161],"(unobservable":[34],"inputs).":[35],"This":[36,192],"critically":[38],"impacts":[39],"perceived":[40],"valuation":[41,208],"reliability,":[42],"yet":[43],"remains":[44],"inconsistent":[45],"when":[46],"performed":[47],"manually.":[48],"We":[49],"propose":[50],"novel":[52],"hybrid":[53],"architecture":[54],"combining":[55],"transformer-based":[56],"attention":[57,115],"with":[58,198,212],"graph":[59,87],"neural":[60,88],"networks":[61,89],"(GNNs)":[62],"that":[63,136,171,210],"jointly":[64],"exploits":[65],"temporal":[66,174],"market":[67,83,108],"patterns":[68],"and":[69,123,132,151,176,189,206],"inter-instrument":[70],"relational":[71],"dependencies.":[72],"Our":[73],"approach":[74],"leverages":[75],"transformer":[76,188],"encoders":[77],"to":[78,90],"capture":[79],"time-series":[80],"dynamics":[81],"data":[84,100],"while":[85,165],"utilizing":[86],"model":[91,111,138,156],"structural":[92],"relationships":[93],"between":[94],"instruments.":[95],"To":[96],"address":[97],"limited":[98],"labeled":[99],"availability,":[101],"we":[102],"implement":[103],"self-supervised":[104],"pretraining":[105],"on":[106,127],"unlabeled":[107],"data,":[109],"enhancing":[110],"efficiency.":[112],"The":[113],"architecture\u2019s":[114],"mechanisms":[116],"provide":[117],"interpretable":[118],"explanations":[119,170],"crucial":[120],"for":[121],"audit":[122],"regulatory":[124,213],"compliance.":[125],"Experiments":[126],"open-source":[128],"datasets":[129],"derivatives":[131],"structured":[133],"products":[134],"demonstrate":[135],"our":[137,155],"achieves":[139],"88%":[140],"accuracy,":[142],"significantly":[143],"outperforming":[144],"traditional":[145],"methods":[146],"including":[147],"rule-based":[148],"classifiers":[149],"(73%)":[150],"XGBoost":[152],"(80%).":[153],"Notably,":[154],"excels":[157],"at":[158],"identifying":[159],"(F1-score":[163],"0.90)":[164],"maintaining":[166],"interpretability":[167],"through":[168],"attention-based":[169],"highlight":[172],"key":[173],"periods":[175],"influential":[177],"instrument":[178],"relationships.":[179],"Ablation":[180],"studies":[181],"confirm":[182],"the":[183],"complementary":[184],"contributions":[185],"both":[187],"GNN":[190],"components.":[191],"work":[193],"bridges":[194],"advanced":[195],"deep":[196],"learning":[197],"compliance":[200],"requirements,":[201],"facilitating":[202],"more":[203],"consistent,":[204],"accurate,":[205],"transparent":[207],"procedures":[209],"align":[211],"standards.":[214]},"counts_by_year":[{"year":2026,"cited_by_count":2}],"updated_date":"2026-05-06T08:25:59.206177","created_date":"2025-11-19T00:00:00"}