{"id":"https://openalex.org/W2317123573","doi":"https://doi.org/10.1108/k-08-2015-0208","title":"Constructing ZSCORE-based financial crisis warning models using fruit fly optimization algorithm and general regression neural network","display_name":"Constructing ZSCORE-based financial crisis warning models using fruit fly optimization algorithm and general regression neural network","publication_year":2016,"publication_date":"2016-04-04","ids":{"openalex":"https://openalex.org/W2317123573","doi":"https://doi.org/10.1108/k-08-2015-0208","mag":"2317123573"},"language":"en","primary_location":{"id":"doi:10.1108/k-08-2015-0208","is_oa":false,"landing_page_url":"https://doi.org/10.1108/k-08-2015-0208","pdf_url":null,"source":{"id":"https://openalex.org/S168682784","display_name":"Kybernetes","issn_l":"0368-492X","issn":["0368-492X","1758-7883"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319811","host_organization_name":"Emerald Publishing Limited","host_organization_lineage":["https://openalex.org/P4310319811"],"host_organization_lineage_names":["Emerald Publishing Limited"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Kybernetes","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/A5082223919","display_name":"Tsui-Hua Huang","orcid":null},"institutions":[{"id":"https://openalex.org/I154864474","display_name":"National Taiwan University of Science and Technology","ror":"https://ror.org/00q09pe49","country_code":"TW","type":"education","lineage":["https://openalex.org/I154864474"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Tsui-Hua Huang","raw_affiliation_strings":["Graduate Institute of Management, National Taiwan University of Science and Technology, Taipei, Taiwan, R.O.C"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Graduate Institute of Management, National Taiwan University of Science and Technology, Taipei, Taiwan, R.O.C","institution_ids":["https://openalex.org/I154864474"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5082040897","display_name":"Yungho Leu","orcid":"https://orcid.org/0000-0002-8748-6968"},"institutions":[{"id":"https://openalex.org/I154864474","display_name":"National Taiwan University of Science and Technology","ror":"https://ror.org/00q09pe49","country_code":"TW","type":"education","lineage":["https://openalex.org/I154864474"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Yungho Leu","raw_affiliation_strings":["Department of Information Management, National Taiwan University of Science and Technology, Taipei, Taiwan, R.O.C"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Information Management, National Taiwan University of Science and Technology, Taipei, Taiwan, R.O.C","institution_ids":["https://openalex.org/I154864474"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100785653","display_name":"Wen-Tsao Pan","orcid":"https://orcid.org/0000-0002-1470-3685"},"institutions":[{"id":"https://openalex.org/I154864474","display_name":"National Taiwan University of Science and Technology","ror":"https://ror.org/00q09pe49","country_code":"TW","type":"education","lineage":["https://openalex.org/I154864474"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Wen-Tsao Pan","raw_affiliation_strings":["Department of Information Management, National Taiwan University of Science and Technology, Taipei, Taiwan, R.O.C"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Information Management, National Taiwan University of Science and Technology, Taipei, Taiwan, R.O.C","institution_ids":["https://openalex.org/I154864474"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":4.7927,"has_fulltext":false,"cited_by_count":17,"citation_normalized_percentile":{"value":0.94852333,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":"45","issue":"4","first_page":"650","last_page":"665"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11653","display_name":"Financial Distress and Bankruptcy Prediction","score":0.9991000294685364,"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":0.9991000294685364,"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/T11326","display_name":"Stock Market Forecasting Methods","score":0.9976999759674072,"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/T12276","display_name":"Risk Management in Financial Firms","score":0.9648000001907349,"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/multivariate-adaptive-regression-splines","display_name":"Multivariate adaptive regression splines","score":0.6433373689651489},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6337875127792358},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.55599445104599},{"id":"https://openalex.org/keywords/financial-crisis","display_name":"Financial crisis","score":0.532207190990448},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5009729862213135},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.452747642993927},{"id":"https://openalex.org/keywords/value","display_name":"Value (mathematics)","score":0.44749119877815247},{"id":"https://openalex.org/keywords/domino-effect","display_name":"Domino effect","score":0.421755313873291},{"id":"https://openalex.org/keywords/warning-system","display_name":"Warning system","score":0.41591787338256836},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.41207122802734375},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.36276084184646606},{"id":"https://openalex.org/keywords/finance","display_name":"Finance","score":0.3600602149963379},{"id":"https://openalex.org/keywords/regression-analysis","display_name":"Regression analysis","score":0.3325123190879822},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.21197989583015442},{"id":"https://openalex.org/keywords/economics","display_name":"Economics","score":0.15152102708816528},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.14496386051177979},{"id":"https://openalex.org/keywords/bayesian-multivariate-linear-regression","display_name":"Bayesian multivariate linear regression","score":0.12141728401184082}],"concepts":[{"id":"https://openalex.org/C44882253","wikidata":"https://www.wikidata.org/wiki/Q3455882","display_name":"Multivariate adaptive regression splines","level":4,"score":0.6433373689651489},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6337875127792358},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.55599445104599},{"id":"https://openalex.org/C2778300220","wikidata":"https://www.wikidata.org/wiki/Q114380","display_name":"Financial crisis","level":2,"score":0.532207190990448},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5009729862213135},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.452747642993927},{"id":"https://openalex.org/C2776291640","wikidata":"https://www.wikidata.org/wiki/Q2912517","display_name":"Value (mathematics)","level":2,"score":0.44749119877815247},{"id":"https://openalex.org/C155223936","wikidata":"https://www.wikidata.org/wiki/Q682875","display_name":"Domino effect","level":2,"score":0.421755313873291},{"id":"https://openalex.org/C29825287","wikidata":"https://www.wikidata.org/wiki/Q1427940","display_name":"Warning system","level":2,"score":0.41591787338256836},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.41207122802734375},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.36276084184646606},{"id":"https://openalex.org/C10138342","wikidata":"https://www.wikidata.org/wiki/Q43015","display_name":"Finance","level":1,"score":0.3600602149963379},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.3325123190879822},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.21197989583015442},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.15152102708816528},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.14496386051177979},{"id":"https://openalex.org/C64946054","wikidata":"https://www.wikidata.org/wiki/Q4874476","display_name":"Bayesian multivariate linear regression","level":3,"score":0.12141728401184082},{"id":"https://openalex.org/C185544564","wikidata":"https://www.wikidata.org/wiki/Q81197","display_name":"Nuclear physics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C139719470","wikidata":"https://www.wikidata.org/wiki/Q39680","display_name":"Macroeconomics","level":1,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1108/k-08-2015-0208","is_oa":false,"landing_page_url":"https://doi.org/10.1108/k-08-2015-0208","pdf_url":null,"source":{"id":"https://openalex.org/S168682784","display_name":"Kybernetes","issn_l":"0368-492X","issn":["0368-492X","1758-7883"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319811","host_organization_name":"Emerald Publishing Limited","host_organization_lineage":["https://openalex.org/P4310319811"],"host_organization_lineage_names":["Emerald Publishing Limited"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Kybernetes","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":34,"referenced_works":["https://openalex.org/W1484545635","https://openalex.org/W1516234411","https://openalex.org/W1694744249","https://openalex.org/W1912982817","https://openalex.org/W1987054329","https://openalex.org/W1990564389","https://openalex.org/W1994102621","https://openalex.org/W2002924018","https://openalex.org/W2006680549","https://openalex.org/W2012552265","https://openalex.org/W2016207894","https://openalex.org/W2020848494","https://openalex.org/W2040182148","https://openalex.org/W2041604057","https://openalex.org/W2042351117","https://openalex.org/W2047754173","https://openalex.org/W2048801439","https://openalex.org/W2054359607","https://openalex.org/W2066523057","https://openalex.org/W2067526202","https://openalex.org/W2082067642","https://openalex.org/W2090780285","https://openalex.org/W2093195672","https://openalex.org/W2095199042","https://openalex.org/W2117250158","https://openalex.org/W2124532504","https://openalex.org/W2128084896","https://openalex.org/W2149723649","https://openalex.org/W2155653793","https://openalex.org/W2159423024","https://openalex.org/W2165035706","https://openalex.org/W2166996073","https://openalex.org/W2168501960","https://openalex.org/W2324154612"],"related_works":["https://openalex.org/W10576317","https://openalex.org/W1481829876","https://openalex.org/W4256152544","https://openalex.org/W2984306696","https://openalex.org/W1994886377","https://openalex.org/W2383431396","https://openalex.org/W3009500911","https://openalex.org/W2033531685","https://openalex.org/W3148095850","https://openalex.org/W2148211789"],"abstract_inverted_index":{"Purpose":[0],"\u2013":[1,72,194,234],"In":[2],"order":[3],"to":[4,38,63,82,133,137,211,219,224],"avoid":[5],"enterprise":[6,43],"crisis":[7,44,68],"and":[8,23,47,54,99,106,148,152,164],"cause":[9],"the":[10,15,20,40,50,55,74,84,87,90,96,100,103,107,114,135,139,175,178,186,199,203,206,214,227,230,247],"domino":[11],"effect,":[12],"which":[13,171,242],"influences":[14],"investment":[16],"return":[17],"of":[18,30,42,59,86,198,229],"investors,":[19],"national":[21],"economy,":[22],"financial":[24,32,67],"crisis,":[25],"establishing":[26],"a":[27,238],"complete":[28],"set":[29],"feasible":[31],"early":[33],"warning":[34,69],"model":[35,92,197],"can":[36,216],"help":[37],"prevent":[39],"possibility":[41],"in":[45,89,177],"advance,":[46],"thus,":[48],"reduce":[49],"influence":[51],"on":[52,160],"society":[53],"economy.":[56],"The":[57,195],"purpose":[58],"this":[60],"paper":[61,236],"is":[62,80,111,131],"develop":[64],"an":[65],"efficient":[66],"model.":[70,250],"Design/methodology/approach":[71],"First,":[73],"fruit":[75],"fly":[76],"optimization":[77],"algorithm":[78],"(FOA)":[79],"used":[81,132,218],"adjust":[83],"coefficients":[85],"parameters":[88,176,223],"ZSCORE":[91,180,249],"(we":[93,126],"call":[94,127],"it":[95,128],"FOA_ZSCORE":[97,200],"model),":[98,120,130],"difference":[101,136],"between":[102],"forecasted":[104],"value":[105,110],"real":[108],"target":[109],"calculated.":[112],"Afterward,":[113],"generalized":[115],"regressive":[116],"neural":[117],"network":[118],"(GRNN":[119],"with":[121,202],"optimized":[122],"spread":[123],"by":[124,184],"FOA":[125],"FOA_GRNN":[129,204],"forecast":[134],"promote":[138],"forecasting":[140],"accuracy.":[141],"Various":[142],"models,":[143],"including":[144],"ZSCORE,":[145],"FOA_ZSCORE,":[146],"FOA_ZSCORE+GRNN,":[147],"FOA_ZSCORE+FOA_GRNN,":[149],"are":[150,157,172,182],"trained":[151],"tested.":[153],"Finally,":[154],"different":[155,173],"models":[156],"compared":[158,210],"based":[159],"their":[161],"prediction":[162,208,231],"accuracies":[163],"ROC":[165],"curves.":[166],"Furthermore,":[167],"more":[168,221],"appropriate":[169,222],"parameters,":[170],"from":[174],"original":[179,248],"model,":[181,240],"selected":[183],"using":[185],"multivariate":[187],"adaptive":[188],"regression":[189],"splines":[190],"(MARS)":[191],"method.":[192],"Findings":[193],"hybrid":[196,239],"together":[201],"offers":[205,243],"highest":[207],"accuracy,":[209],"other":[212],"models;":[213],"MARS":[215],"be":[217],"select":[220],"further":[225],"improve":[226],"performance":[228,245],"models.":[232],"Originality/value":[233],"This":[235],"proposes":[237],"FOA_ZSCORE+FOA_GRNN":[241],"better":[244],"than":[246]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":4},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":2},{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
