New metrics and approaches for predicting bankruptcy

Página do item simplificado
dc.contributor.authorBarboza F.
dc.contributor.authorBasso L.F.C.
dc.contributor.authorKimura H.
dc.date.accessioned2024-03-12T19:12:53Z
dc.date.available2024-03-12T19:12:53Z
dc.date.issued2023
dc.description.abstract© 2021 Taylor & Francis Group, LLC.Credit risk models, particularly those seeking to understand signals given by companies close to bankruptcy, are under constant discussion and updating. Statistical models are traditional and easy to understand but machine learning have been tested in the financial context. Here, we developed bankruptcy forecast models for non-financial US companies, using a dataset comprising 1980–2014. We examined static, growth and also growth variation as inputs for predicting which firms would go bankrupt one year later. Seven techniques were tested, including Discriminant Analysis, Logistic Regression, and machine learning ones, such as Neural Networks, Support Vector Machines, AdaBoost, Bagging, and Random Forest. Given the number of models evaluated, the analyses are rich and varied, with emphasis on the finding that traditional techniques with the appropriate variables are more capable of achieving superior performance than machine learning models. These results open a debate with the current literature, which has been declaring precisely the opposite. Nevertheless, this article contributes to the literature in several ways, as it uses a comprehensive sample and a performance comparison, allowing potential application by both practitioners and researchers. Besides, this is the first work that simultaneously tests growth measures and growth variation rates, which have relevant rationale and impacts.
dc.description.firstpage2615
dc.description.issuenumber6
dc.description.lastpage2632
dc.description.volume52
dc.identifier.doi10.1080/03610918.2021.1910837
dc.identifier.issn1532-4141
dc.identifier.urihttps://dspace.mackenzie.br/handle/10899/34254
dc.relation.ispartofCommunications in Statistics: Simulation and Computation
dc.rightsAcesso Restrito
dc.subject.otherlanguageArtificial intelligence
dc.subject.otherlanguageBankruptcy forecasting
dc.subject.otherlanguageGrowth metrics
dc.subject.otherlanguageProbability of default
dc.titleNew metrics and approaches for predicting bankruptcy
dc.typeArtigo
local.scopus.citations3
local.scopus.eid2-s2.0-85114482737
local.scopus.subjectBankruptcy forecasting
local.scopus.subjectCredit risk modeling
local.scopus.subjectForecast models
local.scopus.subjectGrowth metric
local.scopus.subjectGrowth-variation
local.scopus.subjectLogistics regressions
local.scopus.subjectMachine-learning
local.scopus.subjectNeural-networks
local.scopus.subjectProbability of defaults
local.scopus.subjectStatistic modeling
local.scopus.updated2024-12-01
local.scopus.urlhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85114482737&origin=inward
Arquivos
Coleções
Artigos de periódico