Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/18569
Título: | Financial distress model prediction using SVM + |
Autor(es): | Ribeiro, Bernardete Silva, C. Vieira, Armando Gaspar-Cunha, A. Neves, João |
Data: | 2010 |
Editora: | IEEE |
Revista: | IEEE International Joint Conference on Neural Networks (IJCNN) |
Resumo(s): | Financial distress prediction is of great importance to all stakeholders in order to enable better decision-making in evaluating firms. In recent years, the rate of bankruptcy has risen and it is becoming harder to estimate as companies become more complex and the asymmetric information between banks and firms increases. Although a great variety of techniques have been applied along the years, no comprehensive method incorporating an holistic perspective had hitherto been considered. Recently, SVM+ a technique proposed by Vapnik [17] provides a formal way to incorporate privileged information onto the learning models improving generalization. By exploiting additional information to improve traditional inductive learning we propose a prediction model where data is naturally separated into several groups according to the size of the firm. Experimental results in the setting of a heterogeneous data set of French companies demonstrated that the proposed model showed superior performance in terms of prediction accuracy in bankruptcy prediction and misclassification cost. |
Tipo: | Artigo em ata de conferência |
URI: | https://hdl.handle.net/1822/18569 |
ISBN: | 9781424469178 |
DOI: | 10.1109/IJCNN.2010.5596729 |
ISSN: | 2161-4393 |
Arbitragem científica: | yes |
Acesso: | Acesso aberto |
Aparece nas coleções: | IPC - Resumos alargados em actas de encontros científicos internacionais com arbitragem |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
---|---|---|---|---|
Ribeiro et al._2010_Financial Distress Model Prediction using SVM.pdf | 1,07 MB | Adobe PDF | Ver/Abrir |