Designing topological data to forecast bankruptcy using convolutional neural networks

DOI10.1007/s10479-022-04780-7OpenAlexW4283397490MaRDI QIDQ6115949

Philippe du Jardin

Publication date: 13 July 2023

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

Full work available at URL: https://doi.org/10.1007/s10479-022-04780-7

zbMATH Keywords

clusteringfinancial risktopological dataconvolutional neural networksbankruptcy prediction

Mathematics Subject Classification ID

Mathematical programming (90Cxx) Artificial intelligence (68Txx) Actuarial science and mathematical finance (91Gxx)

Cites Work

• Bankruptcy prediction in banks and firms via statistical and intelligent techniques -- a review
• Dynamic analysis of the forecasting bankruptcy under presence of unobserved heterogeneity
• Multi-criteria ranking of corporate distress prediction models: empirical evaluation and methodological contributions
• Forecasting financial failure using a Kohonen map: a comparative study to improve model stability over time
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• Machine learning for combinatorial optimization: a methodological tour d'horizon
• Optimization problems for machine learning: a survey
• A new approach to deal with variable selection in neural networks: an application to bankruptcy prediction
• Forecasting bankruptcy using biclustering and neural network-based ensembles
• Forecast bankruptcy using a blend of clustering and MARS model: case of US banks
• An out-of-sample evaluation framework for DEA with application in bankruptcy prediction
• Model combination for credit risk assessment: a stacked generalization approach
• Self-organizing maps.
• Comparative analysis of artificial neural network models: Application in bankruptcy prediction
• Random forests

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