{"id":"https://openalex.org/W4410794688","doi":"https://doi.org/10.1145/3727353.3727367","title":"A Study on Forecasting Financial Distress of Listed Companies Based on LightGBM Algorithm","display_name":"A Study on Forecasting Financial Distress of Listed Companies Based on LightGBM Algorithm","publication_year":2025,"publication_date":"2025-01-10","ids":{"openalex":"https://openalex.org/W4410794688","doi":"https://doi.org/10.1145/3727353.3727367"},"language":"en","primary_location":{"id":"doi:10.1145/3727353.3727367","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3727353.3727367","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3727353.3727367","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2025 4th International Conference on Big Data, Information and Computer Network","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3727353.3727367","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Zhe Yang","orcid":"https://orcid.org/0009-0002-9082-1672"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhe Yang","raw_affiliation_strings":["Beiing China-Power Information Technology co.,LTD, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0002-9082-1672","affiliations":[{"raw_affiliation_string":"Beiing China-Power Information Technology co.,LTD, Beijing, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":null,"display_name":"Xinyue Zhang","orcid":"https://orcid.org/0009-0009-3289-7557"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xinyue Zhang","raw_affiliation_strings":["Beiing China-Power Information Technology co.,LTD, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0009-3289-7557","affiliations":[{"raw_affiliation_string":"Beiing China-Power Information Technology co.,LTD, Beijing, China","institution_ids":[]}]},{"author_position":"last","author":{"id":null,"display_name":"Shuai Zhang","orcid":"https://orcid.org/0009-0004-7085-4674"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shuai Zhang","raw_affiliation_strings":["Beiing China-Power Information Technology co.,LTD, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0004-7085-4674","affiliations":[{"raw_affiliation_string":"Beiing China-Power Information Technology co.,LTD, Beijing, China","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.12896,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"81","last_page":"87"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11653","display_name":"Financial Distress and Bankruptcy Prediction","score":0.9897000193595886,"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":0.9897000193595886,"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":0.9093000292778015,"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/financial-distress","display_name":"Financial distress","score":0.6933995485305786},{"id":"https://openalex.org/keywords/finance","display_name":"Finance","score":0.5085691213607788},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.49247393012046814},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.466875284910202},{"id":"https://openalex.org/keywords/business","display_name":"Business","score":0.2205800712108612},{"id":"https://openalex.org/keywords/financial-system","display_name":"Financial system","score":0.1295892298221588}],"concepts":[{"id":"https://openalex.org/C2984760201","wikidata":"https://www.wikidata.org/wiki/Q1785212","display_name":"Financial distress","level":2,"score":0.6933995485305786},{"id":"https://openalex.org/C10138342","wikidata":"https://www.wikidata.org/wiki/Q43015","display_name":"Finance","level":1,"score":0.5085691213607788},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.49247393012046814},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.466875284910202},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.2205800712108612},{"id":"https://openalex.org/C73283319","wikidata":"https://www.wikidata.org/wiki/Q1416617","display_name":"Financial system","level":1,"score":0.1295892298221588}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3727353.3727367","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3727353.3727367","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3727353.3727367","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2025 4th International Conference on Big Data, Information and Computer Network","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3727353.3727367","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3727353.3727367","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3727353.3727367","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2025 4th International Conference on Big Data, Information and Computer Network","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4410794688.pdf","grobid_xml":"https://content.openalex.org/works/W4410794688.grobid-xml"},"referenced_works_count":5,"referenced_works":["https://openalex.org/W3215669537","https://openalex.org/W4281855090","https://openalex.org/W4303415456","https://openalex.org/W4308908953","https://openalex.org/W4396720932"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2051487156","https://openalex.org/W2073681303","https://openalex.org/W2390279801","https://openalex.org/W4391913857","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109"],"abstract_inverted_index":{"Forecasting":[0],"financial":[1,87,92],"crises":[2],"is":[3,63,95,111],"a":[4,58,99],"major":[5],"concern":[6],"in":[7,66,113],"the":[8,21,31,36,39,42,53,55,82,107],"field":[9],"of":[10,23,27,33,38,69],"finance":[11],"and":[12,35,71,97,101,120],"economic":[13],"research":[14],"particularly":[15],"for":[16,47,85],"publicly":[17],"traded":[18],"companies":[19,25],"since":[20],"survival":[22],"such":[24],"would,":[26],"course,":[28],"directly":[29],"affect":[30],"interests":[32],"investors":[34],"stability":[37],"market.":[40],"In":[41],"recent":[43],"past,":[44],"growing":[45],"popularity":[46],"machine":[48,61],"learning":[49],"has":[50],"brought":[51],"to":[52],"forefront":[54],"LightGBM":[56,83,110],"algorithm,":[57],"gradient":[59],"boosting":[60],"that":[62,109],"efficient,":[64],"especially":[65],"its":[67,114],"handling":[68],"medium":[70],"big":[72],"real-world":[73],"datasets":[74],"with":[75],"high":[76],"dimensionality.":[77],"The":[78],"article":[79],"focuses":[80],"on":[81],"algorithm":[84],"corporate":[86],"distress":[88],"prediction.":[89],"A":[90],"complete":[91],"indicator":[93],"system":[94],"constructed,":[96],"by":[98],"thorough":[100],"comprehensive":[102],"empirical":[103],"analysis,":[104],"it":[105],"justifies":[106],"fact":[108],"superior":[112],"prediction":[115],"accuracy,":[116],"model":[117],"training":[118],"time,":[119],"resource":[121],"consumption.":[122]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
