{"id":"https://openalex.org/W2092543866","doi":"https://doi.org/10.1002/(sici)1099-1174(199709)6:3<177::aid-isaf134>3.0.co;2-d","title":"Predicting the outcome following bankruptcy filing: a three-state classification using neural networks","display_name":"Predicting the outcome following bankruptcy filing: a three-state classification using neural networks","publication_year":1997,"publication_date":"1997-09-01","ids":{"openalex":"https://openalex.org/W2092543866","doi":"https://doi.org/10.1002/(sici)1099-1174(199709)6:3<177::aid-isaf134>3.0.co;2-d","mag":"2092543866"},"language":"en","primary_location":{"id":"doi:10.1002/(sici)1099-1174(199709)6:3<177::aid-isaf134>3.0.co;2-d","is_oa":false,"landing_page_url":"https://doi.org/10.1002/(sici)1099-1174(199709)6:3<177::aid-isaf134>3.0.co;2-d","pdf_url":null,"source":{"id":"https://openalex.org/S67471091","display_name":"Intelligent Systems in Accounting Finance & Management","issn_l":"1055-615X","issn":["1055-615X","1099-1174"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320595","host_organization_name":"Wiley","host_organization_lineage":["https://openalex.org/P4310320595"],"host_organization_lineage_names":["Wiley"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Intelligent Systems in Accounting, Finance &amp; Management","raw_type":"journal-article"},"type":"article","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/A5026820461","display_name":"Ran Barniv","orcid":null},"institutions":[{"id":"https://openalex.org/I149910238","display_name":"Kent State University","ror":"https://ror.org/049pfb863","country_code":"US","type":"education","lineage":["https://openalex.org/I149910238"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ran Barniv","raw_affiliation_strings":["Kent State University, Kent, OH, USA"],"affiliations":[{"raw_affiliation_string":"Kent State University, Kent, OH, USA","institution_ids":["https://openalex.org/I149910238"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5115588021","display_name":"Anurag Agarwal","orcid":null},"institutions":[{"id":"https://openalex.org/I149910238","display_name":"Kent State University","ror":"https://ror.org/049pfb863","country_code":"US","type":"education","lineage":["https://openalex.org/I149910238"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Anurag Agarwal","raw_affiliation_strings":["Kent State University, Kent, OH, USA","Kent State University, College of Business Administration, Department of Accounting, P.O. Box 5790, Kent, Ohio 44242\u20100001, USA"],"affiliations":[{"raw_affiliation_string":"Kent State University, Kent, OH, USA","institution_ids":["https://openalex.org/I149910238"]},{"raw_affiliation_string":"Kent State University, College of Business Administration, Department of Accounting, P.O. Box 5790, Kent, Ohio 44242\u20100001, USA","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5043118574","display_name":"Robert Leach","orcid":null},"institutions":[{"id":"https://openalex.org/I149910238","display_name":"Kent State University","ror":"https://ror.org/049pfb863","country_code":"US","type":"education","lineage":["https://openalex.org/I149910238"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Robert Leach","raw_affiliation_strings":["Kent State University, Kent, OH, USA"],"affiliations":[{"raw_affiliation_string":"Kent State University, Kent, OH, USA","institution_ids":["https://openalex.org/I149910238"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5115588021"],"corresponding_institution_ids":["https://openalex.org/I149910238"],"apc_list":{"value":2630,"currency":"USD","value_usd":2630},"apc_paid":null,"fwci":2.8414,"has_fulltext":false,"cited_by_count":60,"citation_normalized_percentile":{"value":0.93121065,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":"6","issue":"3","first_page":"177","last_page":"194"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11653","display_name":"Financial Distress and Bankruptcy Prediction","score":0.9970999956130981,"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/T11653","display_name":"Financial Distress and Bankruptcy Prediction","score":0.9970999956130981,"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/bankruptcy","display_name":"Bankruptcy","score":0.8900843262672424},{"id":"https://openalex.org/keywords/logit","display_name":"Logit","score":0.767329216003418},{"id":"https://openalex.org/keywords/bankruptcy-prediction","display_name":"Bankruptcy prediction","score":0.705513060092926},{"id":"https://openalex.org/keywords/financial-distress","display_name":"Financial distress","score":0.6226549744606018},{"id":"https://openalex.org/keywords/logistic-regression","display_name":"Logistic regression","score":0.5880112648010254},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.5651279091835022},{"id":"https://openalex.org/keywords/outcome","display_name":"Outcome (game theory)","score":0.5475091934204102},{"id":"https://openalex.org/keywords/linear-discriminant-analysis","display_name":"Linear discriminant analysis","score":0.5234969258308411},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4556068480014801},{"id":"https://openalex.org/keywords/distress","display_name":"Distress","score":0.4454112648963928},{"id":"https://openalex.org/keywords/nonparametric-statistics","display_name":"Nonparametric statistics","score":0.4230022132396698},{"id":"https://openalex.org/keywords/actuarial-science","display_name":"Actuarial science","score":0.4211902618408203},{"id":"https://openalex.org/keywords/financial-ratio","display_name":"Financial ratio","score":0.4208303987979889},{"id":"https://openalex.org/keywords/state","display_name":"State (computer science)","score":0.4203653335571289},{"id":"https://openalex.org/keywords/business","display_name":"Business","score":0.40680190920829773},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4012521803379059},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.30296313762664795},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.2849242687225342},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.27849626541137695},{"id":"https://openalex.org/keywords/economics","display_name":"Economics","score":0.26369714736938477},{"id":"https://openalex.org/keywords/finance","display_name":"Finance","score":0.23909679055213928},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.21700435876846313},{"id":"https://openalex.org/keywords/financial-system","display_name":"Financial system","score":0.0888013243675232}],"concepts":[{"id":"https://openalex.org/C504631918","wikidata":"https://www.wikidata.org/wiki/Q152074","display_name":"Bankruptcy","level":2,"score":0.8900843262672424},{"id":"https://openalex.org/C140331021","wikidata":"https://www.wikidata.org/wiki/Q1868104","display_name":"Logit","level":2,"score":0.767329216003418},{"id":"https://openalex.org/C2777388754","wikidata":"https://www.wikidata.org/wiki/Q1664594","display_name":"Bankruptcy prediction","level":3,"score":0.705513060092926},{"id":"https://openalex.org/C2984760201","wikidata":"https://www.wikidata.org/wiki/Q1785212","display_name":"Financial distress","level":2,"score":0.6226549744606018},{"id":"https://openalex.org/C151956035","wikidata":"https://www.wikidata.org/wiki/Q1132755","display_name":"Logistic regression","level":2,"score":0.5880112648010254},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.5651279091835022},{"id":"https://openalex.org/C148220186","wikidata":"https://www.wikidata.org/wiki/Q7111912","display_name":"Outcome (game theory)","level":2,"score":0.5475091934204102},{"id":"https://openalex.org/C69738355","wikidata":"https://www.wikidata.org/wiki/Q1228929","display_name":"Linear discriminant analysis","level":2,"score":0.5234969258308411},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4556068480014801},{"id":"https://openalex.org/C139265228","wikidata":"https://www.wikidata.org/wiki/Q5283089","display_name":"Distress","level":2,"score":0.4454112648963928},{"id":"https://openalex.org/C102366305","wikidata":"https://www.wikidata.org/wiki/Q1097688","display_name":"Nonparametric statistics","level":2,"score":0.4230022132396698},{"id":"https://openalex.org/C162118730","wikidata":"https://www.wikidata.org/wiki/Q1128453","display_name":"Actuarial science","level":1,"score":0.4211902618408203},{"id":"https://openalex.org/C98014903","wikidata":"https://www.wikidata.org/wiki/Q832161","display_name":"Financial ratio","level":2,"score":0.4208303987979889},{"id":"https://openalex.org/C48103436","wikidata":"https://www.wikidata.org/wiki/Q599031","display_name":"State (computer science)","level":2,"score":0.4203653335571289},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.40680190920829773},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4012521803379059},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.30296313762664795},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2849242687225342},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.27849626541137695},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.26369714736938477},{"id":"https://openalex.org/C10138342","wikidata":"https://www.wikidata.org/wiki/Q43015","display_name":"Finance","level":1,"score":0.23909679055213928},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.21700435876846313},{"id":"https://openalex.org/C73283319","wikidata":"https://www.wikidata.org/wiki/Q1416617","display_name":"Financial system","level":1,"score":0.0888013243675232},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C144237770","wikidata":"https://www.wikidata.org/wiki/Q747534","display_name":"Mathematical economics","level":1,"score":0.0},{"id":"https://openalex.org/C542102704","wikidata":"https://www.wikidata.org/wiki/Q183257","display_name":"Psychotherapist","level":1,"score":0.0},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1002/(sici)1099-1174(199709)6:3<177::aid-isaf134>3.0.co;2-d","is_oa":false,"landing_page_url":"https://doi.org/10.1002/(sici)1099-1174(199709)6:3<177::aid-isaf134>3.0.co;2-d","pdf_url":null,"source":{"id":"https://openalex.org/S67471091","display_name":"Intelligent Systems in Accounting Finance & Management","issn_l":"1055-615X","issn":["1055-615X","1099-1174"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320595","host_organization_name":"Wiley","host_organization_lineage":["https://openalex.org/P4310320595"],"host_organization_lineage_names":["Wiley"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Intelligent Systems in Accounting, Finance &amp; Management","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","score":0.5400000214576721,"id":"https://metadata.un.org/sdg/10"},{"display_name":"Peace, Justice and strong institutions","score":0.4099999964237213,"id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2285942202","https://openalex.org/W2241582670","https://openalex.org/W2179014274","https://openalex.org/W2347342407","https://openalex.org/W2920182980","https://openalex.org/W3159181423","https://openalex.org/W2002912083","https://openalex.org/W2270360991","https://openalex.org/W2940834140","https://openalex.org/W3027138908"],"abstract_inverted_index":{"This":[0],"paper":[1],"uses":[2],"artificial":[3],"neural":[4],"networks":[5],"(ANNs),":[6],"multi-state":[7],"ordered":[8],"logit":[9,158],"and":[10,57,63,97,148,159],"nonparametric":[11],"multiple":[12],"discriminant":[13],"analysis":[14],"(NPDA)":[15],"for":[16,106,114,127,162],"predicting":[17,39,107],"the":[18,27,45,48,61,67,108,128,145,166],"three-state":[19,88,116,155],"outcome":[20,89],"of":[21,30,50,135],"bankruptcy":[22,51,68,129],"filing.":[23],"The":[24],"study":[25],"compares":[26],"classification":[28,156,169],"accuracy":[29],"these":[31],"procedures.":[32],"It":[33],"differs":[34],"from":[35],"previous":[36,104],"studies":[37,105],"on":[38,44,124],"financial":[40,110],"distress":[41,111],"by":[42,76],"focusing":[43],"firm":[46],"after":[47],"filing":[49,62],"using":[52],"accounting":[53],"data,":[54,56],"market":[55],"court-related":[58],"information.":[59],"Following":[60],"through":[64],"court":[65],"approval":[66],"is":[69,90],"resolved":[70],"as":[71,80],"firms":[72,139],"are":[73,171],"either":[74],"acquired":[75],"other":[77],"firms,":[78],"emerging":[79],"independent":[81],"operating":[82],"entities,":[83],"or":[84],"liquidated.":[85],"Distinguishing":[86],"this":[87],"more":[91],"complex":[92],"than":[93,157],"discriminating":[94],"between":[95],"healthy":[96],"financially":[98],"distressed":[99],"firms.":[100],"Models":[101],"suggested":[102],"in":[103,168],"two-group":[109],"perform":[112],"poorly":[113],"our":[115],"scenario.":[117],"Therefore,":[118],"we":[119],"develop":[120],"models":[121],"which":[122,140],"focus":[123],"characteristics":[125],"relevant":[126],"resolution.":[130],"We":[131],"use":[132],"a":[133],"sample":[134,147],"237":[136],"publicly":[137],"traded":[138],"have":[141],"complete":[142],"data.":[143],"For":[144],"entire":[146],"estimation":[149],"samples,":[150],"ANNs":[151],"provide":[152],"significantly":[153],"better":[154],"NPDA.":[160],"However,":[161],"some":[163],"holdout":[164],"samples":[165],"differences":[167],"accuracies":[170],"statistically":[172],"insignificant.":[173],"\u00a9":[174],"1997":[175],"John":[176],"Wiley":[177],"&":[178],"Sons,":[179],"Ltd.":[180]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":3},{"year":2018,"cited_by_count":3},{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":2},{"year":2013,"cited_by_count":2},{"year":2012,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}