{"id":"https://openalex.org/W3011794092","doi":"https://doi.org/10.1108/dta-08-2019-0127","title":"Predicting corporate credit rating based on qualitative information of MD&amp;A transformed using document vectorization techniques","display_name":"Predicting corporate credit rating based on qualitative information of MD&amp;A transformed using document vectorization techniques","publication_year":2020,"publication_date":"2020-03-13","ids":{"openalex":"https://openalex.org/W3011794092","doi":"https://doi.org/10.1108/dta-08-2019-0127","mag":"3011794092"},"language":"en","primary_location":{"id":"doi:10.1108/dta-08-2019-0127","is_oa":false,"landing_page_url":"https://doi.org/10.1108/dta-08-2019-0127","pdf_url":null,"source":{"id":"https://openalex.org/S4210171756","display_name":"Data Technologies and Applications","issn_l":"2514-9288","issn":["2514-9288","2514-9318"],"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":"Data Technologies and Applications","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/A5051234199","display_name":"Jinwook Choi","orcid":"https://orcid.org/0000-0002-9424-9944"},"institutions":[{"id":"https://openalex.org/I197347611","display_name":"Korea University","ror":"https://ror.org/047dqcg40","country_code":"KR","type":"education","lineage":["https://openalex.org/I197347611"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Jinwook Choi","raw_affiliation_strings":["Korea University Business School, Seoul, Republic of Korea"],"affiliations":[{"raw_affiliation_string":"Korea University Business School, Seoul, Republic of Korea","institution_ids":["https://openalex.org/I197347611"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5035953216","display_name":"Yongmoo Suh","orcid":"https://orcid.org/0000-0002-3958-5777"},"institutions":[{"id":"https://openalex.org/I197347611","display_name":"Korea University","ror":"https://ror.org/047dqcg40","country_code":"KR","type":"education","lineage":["https://openalex.org/I197347611"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Yongmoo Suh","raw_affiliation_strings":["Korea University Business School, Seoul, Republic of Korea"],"affiliations":[{"raw_affiliation_string":"Korea University Business School, Seoul, Republic of Korea","institution_ids":["https://openalex.org/I197347611"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5010860064","display_name":"Namchul Jung","orcid":null},"institutions":[{"id":"https://openalex.org/I94588446","display_name":"Hongik University","ror":"https://ror.org/00egdv862","country_code":"KR","type":"education","lineage":["https://openalex.org/I94588446"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Namchul Jung","raw_affiliation_strings":["School of Business Administration, Hongik University, Seoul, Republic of Korea"],"affiliations":[{"raw_affiliation_string":"School of Business Administration, Hongik University, Seoul, Republic of Korea","institution_ids":["https://openalex.org/I94588446"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5051234199"],"corresponding_institution_ids":["https://openalex.org/I197347611"],"apc_list":null,"apc_paid":null,"fwci":2.3382,"has_fulltext":false,"cited_by_count":14,"citation_normalized_percentile":{"value":0.88785188,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":"54","issue":"2","first_page":"151","last_page":"168"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11653","display_name":"Financial Distress and Bankruptcy Prediction","score":0.9994000196456909,"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":0.9994000196456909,"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/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.984000027179718,"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"}},{"id":"https://openalex.org/T11496","display_name":"Credit Risk and Financial Regulations","score":0.9502000212669373,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/word2vec","display_name":"Word2vec","score":0.84544438123703},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7233270406723022},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.7195512652397156},{"id":"https://openalex.org/keywords/vectorization","display_name":"Vectorization (mathematics)","score":0.6006235480308533},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.523216724395752},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4551711678504944},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.45457005500793457},{"id":"https://openalex.org/keywords/credit-rating","display_name":"Credit rating","score":0.4542267322540283},{"id":"https://openalex.org/keywords/tf\u2013idf","display_name":"tf\u2013idf","score":0.424269437789917},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.38945356011390686},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.3469087481498718},{"id":"https://openalex.org/keywords/finance","display_name":"Finance","score":0.2136906087398529},{"id":"https://openalex.org/keywords/business","display_name":"Business","score":0.13917085528373718}],"concepts":[{"id":"https://openalex.org/C2776461190","wikidata":"https://www.wikidata.org/wiki/Q22673982","display_name":"Word2vec","level":3,"score":0.84544438123703},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7233270406723022},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.7195512652397156},{"id":"https://openalex.org/C41681595","wikidata":"https://www.wikidata.org/wiki/Q7917855","display_name":"Vectorization (mathematics)","level":2,"score":0.6006235480308533},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.523216724395752},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4551711678504944},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.45457005500793457},{"id":"https://openalex.org/C205208723","wikidata":"https://www.wikidata.org/wiki/Q372765","display_name":"Credit rating","level":2,"score":0.4542267322540283},{"id":"https://openalex.org/C81758059","wikidata":"https://www.wikidata.org/wiki/Q796584","display_name":"tf\u2013idf","level":3,"score":0.424269437789917},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.38945356011390686},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3469087481498718},{"id":"https://openalex.org/C10138342","wikidata":"https://www.wikidata.org/wiki/Q43015","display_name":"Finance","level":1,"score":0.2136906087398529},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.13917085528373718},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1108/dta-08-2019-0127","is_oa":false,"landing_page_url":"https://doi.org/10.1108/dta-08-2019-0127","pdf_url":null,"source":{"id":"https://openalex.org/S4210171756","display_name":"Data Technologies and Applications","issn_l":"2514-9288","issn":["2514-9288","2514-9318"],"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":"Data Technologies and Applications","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5299999713897705,"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":37,"referenced_works":["https://openalex.org/W1614298861","https://openalex.org/W1975421067","https://openalex.org/W2016031348","https://openalex.org/W2032273384","https://openalex.org/W2047869949","https://openalex.org/W2072875480","https://openalex.org/W2081180521","https://openalex.org/W2085831731","https://openalex.org/W2127067780","https://openalex.org/W2131744502","https://openalex.org/W2141466109","https://openalex.org/W2141599568","https://openalex.org/W2153579005","https://openalex.org/W2158339117","https://openalex.org/W2159494272","https://openalex.org/W2162256707","https://openalex.org/W2163094209","https://openalex.org/W2167277498","https://openalex.org/W2324188699","https://openalex.org/W2329680044","https://openalex.org/W2480068437","https://openalex.org/W2490971013","https://openalex.org/W2509219942","https://openalex.org/W2511832088","https://openalex.org/W2531447192","https://openalex.org/W2556504831","https://openalex.org/W2560858617","https://openalex.org/W2574904969","https://openalex.org/W2588586499","https://openalex.org/W2610250061","https://openalex.org/W2897098357","https://openalex.org/W3122045351","https://openalex.org/W3122556742","https://openalex.org/W3122971762","https://openalex.org/W3123287217","https://openalex.org/W3123440458","https://openalex.org/W4280624190"],"related_works":["https://openalex.org/W2580878117","https://openalex.org/W2937631562","https://openalex.org/W3195168932","https://openalex.org/W1996541855","https://openalex.org/W3175119974","https://openalex.org/W4211165872","https://openalex.org/W4285992209","https://openalex.org/W3107602296","https://openalex.org/W3202346131","https://openalex.org/W3011794092"],"abstract_inverted_index":{"Purpose":[0],"The":[1],"purpose":[2],"of":[3,11,75,126,153,222],"this":[4,210],"study":[5,81,184],"is":[6],"to":[7,42,90,95,98,205],"investigate":[8],"the":[9,120,124,158,173,220],"effectiveness":[10],"qualitative":[12,69,196],"information":[13,25,44,70,197],"extracted":[14,168,198],"from":[15,150,169,199],"firm\u2019s":[16],"annual":[17],"report":[18],"in":[19,40,49,54,64,71,133,219],"predicting":[20],"corporate":[21,51,76,143,190,223],"credit":[22,52,77,144,191,224],"rating.":[23,78],"Qualitative":[24],"represented":[26,45],"by":[27,46,162,178,194],"published":[28],"reports":[29,136],"or":[30],"management":[31],"interview":[32],"has":[33],"been":[34],"known":[35],"as":[36,116,137,139,202],"an":[37,100,117,203],"important":[38],"source":[39],"addition":[41],"quantitative":[43],"financial":[47,135,140,164,180],"values":[48],"assigning":[50],"rating":[53,145,192,225],"practice.":[55],"Nevertheless,":[56],"prior":[57],"studies":[58],"have":[59],"room":[60],"for":[61,189],"further":[62],"research":[63,211],"that":[65,109,229],"they":[66],"rarely":[67],"employed":[68],"developing":[72],"prediction":[73,193,207,226],"model":[74],"Design/methodology/approach":[79],"This":[80,183],"adopted":[82,212],"three":[83,215],"document":[84],"vectorization":[85,217],"methods,":[86],"Bag-Of-Words":[87],"(BOW),":[88],"Word":[89],"Vector":[91,96],"(Word2Vec)":[92],"and":[93,129,142,166,213,227,234],"Document":[94],"(Doc2Vec),":[97],"transform":[99],"unstructured":[101],"textual":[102,216],"data":[103,171],"into":[104],"a":[105,151,186],"numeric":[106],"vector,":[107],"so":[108],"Machine":[110],"Learning":[111],"(ML)":[112],"algorithms":[113],"accept":[114],"it":[115],"input.":[118],"For":[119],"experiments,":[121],"we":[122],"used":[123],"corpus":[125],"Management\u2019s":[127],"Discussion":[128],"Analysis":[130],"(MD&amp;A)":[131],"section":[132],"10-K":[134],"well":[138],"variables":[141,165],"data.":[146],"Findings":[147],"Experimental":[148],"results":[149],"series":[152],"multi-class":[154],"classification":[155],"experiments":[156],"show":[157],"predictive":[159],"models":[160,175],"trained":[161,176],"both":[163],"vectors":[167],"MD&amp;A":[170,200],"outperform":[172],"benchmark":[174],"only":[177],"traditional":[179],"variables.":[181],"Originality/value":[182],"proposed":[185],"new":[187],"approach":[188],"using":[195],"documents":[201],"input":[204],"ML-based":[206],"models.":[208],"Also,":[209],"compared":[214],"methods":[218],"domain":[221],"showed":[228],"BOW":[230],"mostly":[231],"outperformed":[232],"Word2Vec":[233],"Doc2Vec.":[235]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":3}],"updated_date":"2026-04-03T22:45:19.894376","created_date":"2025-10-10T00:00:00"}
