{"id":"https://openalex.org/W4307346065","doi":"https://doi.org/10.1145/3558819.3565215","title":"Prediction of Enterprise Credit Bond Default based on Random Forest and Neural Network","display_name":"Prediction of Enterprise Credit Bond Default based on Random Forest and Neural Network","publication_year":2022,"publication_date":"2022-09-23","ids":{"openalex":"https://openalex.org/W4307346065","doi":"https://doi.org/10.1145/3558819.3565215"},"language":"en","primary_location":{"id":"doi:10.1145/3558819.3565215","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3558819.3565215","pdf_url":null,"source":{"id":"https://openalex.org/S4363608855","display_name":"Proceedings of the 7th International Conference on Cyber Security and Information Engineering","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 7th International Conference on Cyber Security and Information Engineering","raw_type":"proceedings-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/A5057565481","display_name":"Menghan Fu","orcid":null},"institutions":[{"id":"https://openalex.org/I137867983","display_name":"Central University of Finance and Economics","ror":"https://ror.org/008e3hf02","country_code":"CN","type":"education","lineage":["https://openalex.org/I137867983"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Menghan Fu","raw_affiliation_strings":["Central University of Finance and Economics, China"],"affiliations":[{"raw_affiliation_string":"Central University of Finance and Economics, China","institution_ids":["https://openalex.org/I137867983"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5031355271","display_name":"Jie Su","orcid":"https://orcid.org/0000-0003-0275-0334"},"institutions":[{"id":"https://openalex.org/I137867983","display_name":"Central University of Finance and Economics","ror":"https://ror.org/008e3hf02","country_code":"CN","type":"education","lineage":["https://openalex.org/I137867983"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jie Su","raw_affiliation_strings":["Central University of Finance and Economics, China"],"affiliations":[{"raw_affiliation_string":"Central University of Finance and Economics, China","institution_ids":["https://openalex.org/I137867983"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5018517860","display_name":"Ling Zhou","orcid":"https://orcid.org/0000-0001-6655-5162"},"institutions":[{"id":"https://openalex.org/I25254941","display_name":"Beijing Normal University","ror":"https://ror.org/022k4wk35","country_code":"CN","type":"education","lineage":["https://openalex.org/I25254941"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ling Zhou","raw_affiliation_strings":["Beijing Normal University, China"],"affiliations":[{"raw_affiliation_string":"Beijing Normal University, China","institution_ids":["https://openalex.org/I25254941"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5057565481"],"corresponding_institution_ids":["https://openalex.org/I137867983"],"apc_list":null,"apc_paid":null,"fwci":0.5928,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.63544304,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"902","last_page":"906"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11653","display_name":"Financial Distress and Bankruptcy Prediction","score":0.9976999759674072,"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":0.9976999759674072,"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/T11496","display_name":"Credit Risk and Financial Regulations","score":0.9745000004768372,"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"}},{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.9569000005722046,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.8398606777191162},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6728747487068176},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6333537697792053},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.620083212852478},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5626787543296814},{"id":"https://openalex.org/keywords/credit-default-swap","display_name":"Credit default swap","score":0.5540067553520203},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5412944555282593},{"id":"https://openalex.org/keywords/bond","display_name":"Bond","score":0.5143579244613647},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4892418384552002},{"id":"https://openalex.org/keywords/itraxx","display_name":"iTraxx","score":0.45158299803733826},{"id":"https://openalex.org/keywords/credit-rating","display_name":"Credit rating","score":0.43280890583992004},{"id":"https://openalex.org/keywords/credit-risk","display_name":"Credit risk","score":0.4243486225605011},{"id":"https://openalex.org/keywords/empirical-research","display_name":"Empirical research","score":0.4240038990974426},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.3449266254901886},{"id":"https://openalex.org/keywords/credit-valuation-adjustment","display_name":"Credit valuation adjustment","score":0.2715023159980774},{"id":"https://openalex.org/keywords/business","display_name":"Business","score":0.24423468112945557},{"id":"https://openalex.org/keywords/actuarial-science","display_name":"Actuarial science","score":0.24370798468589783},{"id":"https://openalex.org/keywords/finance","display_name":"Finance","score":0.18377435207366943},{"id":"https://openalex.org/keywords/economics","display_name":"Economics","score":0.13695284724235535},{"id":"https://openalex.org/keywords/credit-reference","display_name":"Credit reference","score":0.1010308563709259},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.09876042604446411},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.07455730438232422}],"concepts":[{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.8398606777191162},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6728747487068176},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6333537697792053},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.620083212852478},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5626787543296814},{"id":"https://openalex.org/C30589699","wikidata":"https://www.wikidata.org/wiki/Q624028","display_name":"Credit default swap","level":3,"score":0.5540067553520203},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5412944555282593},{"id":"https://openalex.org/C69738904","wikidata":"https://www.wikidata.org/wiki/Q11693","display_name":"Bond","level":2,"score":0.5143579244613647},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4892418384552002},{"id":"https://openalex.org/C26457952","wikidata":"https://www.wikidata.org/wiki/Q1655033","display_name":"iTraxx","level":5,"score":0.45158299803733826},{"id":"https://openalex.org/C205208723","wikidata":"https://www.wikidata.org/wiki/Q372765","display_name":"Credit rating","level":2,"score":0.43280890583992004},{"id":"https://openalex.org/C178350159","wikidata":"https://www.wikidata.org/wiki/Q162714","display_name":"Credit risk","level":2,"score":0.4243486225605011},{"id":"https://openalex.org/C120936955","wikidata":"https://www.wikidata.org/wiki/Q2155640","display_name":"Empirical research","level":2,"score":0.4240038990974426},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.3449266254901886},{"id":"https://openalex.org/C89997419","wikidata":"https://www.wikidata.org/wiki/Q2828882","display_name":"Credit valuation adjustment","level":4,"score":0.2715023159980774},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.24423468112945557},{"id":"https://openalex.org/C162118730","wikidata":"https://www.wikidata.org/wiki/Q1128453","display_name":"Actuarial science","level":1,"score":0.24370798468589783},{"id":"https://openalex.org/C10138342","wikidata":"https://www.wikidata.org/wiki/Q43015","display_name":"Finance","level":1,"score":0.18377435207366943},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.13695284724235535},{"id":"https://openalex.org/C24308983","wikidata":"https://www.wikidata.org/wiki/Q5183781","display_name":"Credit reference","level":3,"score":0.1010308563709259},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.09876042604446411},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.07455730438232422}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3558819.3565215","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3558819.3565215","pdf_url":null,"source":{"id":"https://openalex.org/S4363608855","display_name":"Proceedings of the 7th International Conference on Cyber Security and Information Engineering","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 7th International Conference on Cyber Security and Information Engineering","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":5,"referenced_works":["https://openalex.org/W1977009091","https://openalex.org/W2124176365","https://openalex.org/W2885442465","https://openalex.org/W2921161724","https://openalex.org/W3123077689"],"related_works":["https://openalex.org/W2618665466","https://openalex.org/W4316095752","https://openalex.org/W83360246","https://openalex.org/W2934885378","https://openalex.org/W1736214789","https://openalex.org/W70462951","https://openalex.org/W1593621785","https://openalex.org/W1864548409","https://openalex.org/W2283837480","https://openalex.org/W2154182488"],"abstract_inverted_index":{"With":[0],"the":[1,12,22,64,85,89,93,96,103,113,116,123,130],"outbreak":[2],"of":[3,14,24,63,67,88,95,118],"many":[4],"major":[5],"default":[6,17,72],"events":[7],"in":[8,115],"China's":[9],"bond":[10,16],"market,":[11],"problem":[13],"corporate":[15,119],"prediction":[18,86,117],"has":[19,126],"increasingly":[20],"become":[21],"focus":[23],"academic":[25],"and":[26,29,32,48,83,122],"practical":[27],"circles,":[28],"machine":[30],"learning":[31,34],"deep":[33],"algorithms":[35,90],"have":[36],"been":[37],"widely":[38],"used.":[39],"By":[40],"constructing":[41],"a":[42,49],"random":[43,50,104],"forest-BP":[44],"neural":[45,56,110],"network":[46,111],"model":[47,52,106,125],"forest":[51,105],"based":[53,107],"on":[54,108],"convolutional":[55,109],"network,":[57],"we":[58],"make":[59],"an":[60],"empirical":[61,99],"analysis":[62],"financial":[65],"data":[66],"listed":[68],"companies":[69],"whose":[70],"bonds":[71],"or":[73],"are":[74],"due":[75],"to":[76,81,91],"be":[77],"paid":[78],"from":[79],"2014":[80],"2019,":[82],"compare":[84],"results":[87,100],"test":[92],"effectiveness":[94],"models.":[97],"The":[98],"show":[101],"that":[102],"is":[112],"best":[114],"credit":[120],"default,":[121],"combined":[124],"better":[127],"performance":[128],"than":[129],"single":[131],"model.":[132]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1}],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2025-10-10T00:00:00"}