Forecast bankruptcy using a blend of clustering and MARS model: case of US banks

DOI10.1007/s10479-018-2845-8zbMath1434.62219OpenAlexW3123118958MaRDI QIDQ2288890

Zeineb Affes, Rania Hentati-Kaffel

Publication date: 20 January 2020

Published in: Annals of Operations Research (Search for Journal in Brave)

Full work available at URL: https://halshs.archives-ouvertes.fr/halshs-01314553/file/16026.pdf

zbMATH Keywords

MARSCARTK-meansbankruptcy predictionearly-warning system

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Numerical computation using splines (65D07) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Applications of statistics to actuarial sciences and financial mathematics (62P05)

Related Items (4)

Optimal feedback control of stock prices under credit risk dynamics ⋮ Designing topological data to forecast bankruptcy using convolutional neural networks ⋮ Forecasting bankruptcy using biclustering and neural network-based ensembles ⋮ Forecast bankruptcy using a blend of clustering and MARS model: case of US banks

Cites Work

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• Multivariate adaptive regression splines
• Artificial neural networks in bankruptcy prediction: General framework and cross-validation analysis
• Forecast bankruptcy using a blend of clustering and MARS model: case of US banks
• Business failure prediction using decision trees
• Dynamic Generalized Linear Models and Bayesian Forecasting

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