{"id":"https://openalex.org/W4415314098","doi":"https://doi.org/10.1145/3768292.3770408","title":"ACT-Tensor: Tensor Completion Framework for Financial Dataset Imputation","display_name":"ACT-Tensor: Tensor Completion Framework for Financial Dataset Imputation","publication_year":2025,"publication_date":"2025-11-14","ids":{"openalex":"https://openalex.org/W4415314098","doi":"https://doi.org/10.1145/3768292.3770408"},"language":"en","primary_location":{"id":"doi:10.1145/3768292.3770408","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3768292.3770408","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 6th ACM International Conference on AI in Finance","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2508.01861","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5109687297","display_name":"J. Mo","orcid":"https://orcid.org/0009-0001-2285-7019"},"institutions":[{"id":"https://openalex.org/I57206974","display_name":"New York University","ror":"https://ror.org/0190ak572","country_code":"US","type":"education","lineage":["https://openalex.org/I57206974"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Junyi Mo","raw_affiliation_strings":["Stern School of Business, New York University, New York, NY, USA"],"raw_orcid":"https://orcid.org/0009-0001-2285-7019","affiliations":[{"raw_affiliation_string":"Stern School of Business, New York University, New York, NY, USA","institution_ids":["https://openalex.org/I57206974"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Jiayu Li","orcid":"https://orcid.org/0009-0002-6625-6167"},"institutions":[{"id":"https://openalex.org/I57206974","display_name":"New York University","ror":"https://ror.org/0190ak572","country_code":"US","type":"education","lineage":["https://openalex.org/I57206974"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jiayu Li","raw_affiliation_strings":["Stern School of Business, New York University, New York, NY, USA"],"raw_orcid":"https://orcid.org/0009-0002-6625-6167","affiliations":[{"raw_affiliation_string":"Stern School of Business, New York University, New York, NY, USA","institution_ids":["https://openalex.org/I57206974"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Duo Zhang","orcid":"https://orcid.org/0009-0005-5951-0851"},"institutions":[{"id":"https://openalex.org/I57206974","display_name":"New York University","ror":"https://ror.org/0190ak572","country_code":"US","type":"education","lineage":["https://openalex.org/I57206974"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Duo Zhang","raw_affiliation_strings":["College of Arts and Science, New York University, New York, NY, USA"],"raw_orcid":"https://orcid.org/0009-0005-5951-0851","affiliations":[{"raw_affiliation_string":"College of Arts and Science, New York University, New York, NY, USA","institution_ids":["https://openalex.org/I57206974"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5003135555","display_name":"Elynn Chen","orcid":"https://orcid.org/0000-0002-7599-1828"},"institutions":[{"id":"https://openalex.org/I57206974","display_name":"New York University","ror":"https://ror.org/0190ak572","country_code":"US","type":"education","lineage":["https://openalex.org/I57206974"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Elynn Chen","raw_affiliation_strings":["Stern School of Business, New York University, New York, NY, USA"],"raw_orcid":"https://orcid.org/0000-0002-7599-1828","affiliations":[{"raw_affiliation_string":"Stern School of Business, New York University, New York, NY, USA","institution_ids":["https://openalex.org/I57206974"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5109687297"],"corresponding_institution_ids":["https://openalex.org/I57206974"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.26758148,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"265","last_page":"273"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12303","display_name":"Tensor decomposition and applications","score":0.9621999859809875,"subfield":{"id":"https://openalex.org/subfields/2605","display_name":"Computational Mathematics"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12303","display_name":"Tensor decomposition and applications","score":0.9621999859809875,"subfield":{"id":"https://openalex.org/subfields/2605","display_name":"Computational Mathematics"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T13650","display_name":"Computational Physics and Python Applications","score":0.9614999890327454,"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/missing-data","display_name":"Missing data","score":0.8136000037193298},{"id":"https://openalex.org/keywords/imputation","display_name":"Imputation (statistics)","score":0.788100004196167},{"id":"https://openalex.org/keywords/smoothing","display_name":"Smoothing","score":0.6438000202178955},{"id":"https://openalex.org/keywords/overfitting","display_name":"Overfitting","score":0.554099977016449},{"id":"https://openalex.org/keywords/synthetic-data","display_name":"Synthetic data","score":0.35440000891685486},{"id":"https://openalex.org/keywords/financial-services","display_name":"Financial services","score":0.31859999895095825},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.3086000084877014}],"concepts":[{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.8136000037193298},{"id":"https://openalex.org/C58041806","wikidata":"https://www.wikidata.org/wiki/Q1660484","display_name":"Imputation (statistics)","level":3,"score":0.788100004196167},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6880000233650208},{"id":"https://openalex.org/C3770464","wikidata":"https://www.wikidata.org/wiki/Q775963","display_name":"Smoothing","level":2,"score":0.6438000202178955},{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.554099977016449},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.45980000495910645},{"id":"https://openalex.org/C10138342","wikidata":"https://www.wikidata.org/wiki/Q43015","display_name":"Finance","level":1,"score":0.4023999869823456},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.3847000002861023},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.35440000891685486},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3384999930858612},{"id":"https://openalex.org/C139043278","wikidata":"https://www.wikidata.org/wiki/Q837171","display_name":"Financial services","level":2,"score":0.31859999895095825},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.3086000084877014},{"id":"https://openalex.org/C23925645","wikidata":"https://www.wikidata.org/wiki/Q5449731","display_name":"Financial modeling","level":2,"score":0.30090001225471497},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.299699991941452},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.29829999804496765},{"id":"https://openalex.org/C119043178","wikidata":"https://www.wikidata.org/wiki/Q320723","display_name":"Covariate","level":2,"score":0.2930999994277954},{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.289000004529953},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.2872999906539917},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.28459998965263367},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.2694000005722046}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3768292.3770408","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3768292.3770408","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 6th ACM International Conference on AI in Finance","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2508.01861","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2508.01861","pdf_url":"https://arxiv.org/pdf/2508.01861","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2508.01861","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2508.01861","pdf_url":"https://arxiv.org/pdf/2508.01861","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2329207489","display_name":null,"funder_award_id":"2412577","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Missing":[0],"data":[1,61,86,140,156],"in":[2,56,130],"financial":[3,29,85,150,164,201],"panels":[4,20],"presents":[5],"a":[6,93,107,136],"critical":[7],"obstacle,":[8],"undermining":[9],"asset-pricing":[10],"models":[11],"and":[12,28,81,106,194],"reducing":[13],"the":[14,35,45,57,154,182],"effectiveness":[15],"of":[16,59,132,138,181],"investment":[17],"strategies.":[18],"Such":[19],"are":[21],"often":[22,41],"inherently":[23],"multi-dimensional,":[24],"spanning":[25],"firms,":[26],"time,":[27],"variables,":[30],"which":[31],"adds":[32],"complexity":[33],"to":[34],"imputation":[36,39,133],"task.":[37],"Conventional":[38],"methods":[40],"fail":[42],"by":[43,101],"flattening":[44],"data\u2019s":[46],"multidimensional":[47],"structure,":[48],"struggling":[49],"with":[50,157],"heterogeneous":[51],"missingness":[52],"patterns,":[53],"or":[54],"overfitting":[55],"face":[58],"extreme":[60,143],"sparsity.":[62],"To":[63,146],"address":[64],"these":[65],"limitations,":[66],"we":[67,152],"introduce":[68],"an":[69,158],"Adaptive,":[70],"Cluster-based":[71],"Temporal":[72],"smoothing":[73,109],"tensor":[74],"completion":[75,95],"framework":[76],"(ACT\u2011Tensor)":[77],"tailored":[78,161],"for":[79,162,200],"severely":[80],"heterogeneously":[82],"missing":[83,139],"multi-dimensional":[84],"panels.":[87],"ACT-Tensor":[88,169],"incorporates":[89],"two":[90],"key":[91],"innovations:":[92],"cluster\u2011based":[94],"module":[96,110],"that":[97,111,124,168,188],"captures":[98],"cross\u2011sectional":[99],"heterogeneity":[100],"learning":[102],"group-specific":[103],"latent":[104],"structures;":[105],"temporal":[108],"proactively":[112],"removes":[113],"short\u2011lived":[114],"noise":[115],"while":[116],"preserving":[117],"slow\u2011moving":[118],"fundamental":[119],"trends.":[120],"Extensive":[121],"experiments":[122],"show":[123,167],"ACT\u2011Tensor":[125],"consistently":[126],"outperforms":[127],"state-of-the-art":[128],"benchmarks":[129],"terms":[131],"accuracy":[134],"across":[135],"range":[137],"regimes,":[141],"including":[142],"sparsity":[144],"scenarios.":[145],"assess":[147],"its":[148],"practical":[149],"utility,":[151],"evaluate":[153],"imputed":[155],"asset\u2010pricing":[159],"pipeline":[160],"tensor-structured":[163],"data.":[165],"Results":[166],"not":[170],"only":[171],"reduces":[172],"pricing":[173],"errors":[174],"but":[175],"also":[176],"significantly":[177],"improves":[178],"risk-adjusted":[179],"returns":[180],"constructed":[183],"portfolio.":[184],"These":[185],"findings":[186],"confirm":[187],"our":[189],"method":[190],"delivers":[191],"highly":[192],"accurate":[193],"informative":[195],"imputations,":[196],"offering":[197],"substantial":[198],"value":[199],"decision-making.":[202]},"counts_by_year":[],"updated_date":"2026-05-07T13:39:58.223016","created_date":"2025-10-18T00:00:00"}