This paper provides a new perspective on the default prediction problem using deep learning algorithms. Via the advantages of deep learning, the representable factors of input data will no longer need to be explicitly extracted, but can be implicitly learned by the deep learning algorithms. We consider the stock returns of both default and solvent companies as input signals and adopt one of the deep learning architecture, Deep Belief Networks (DBN), to train the prediction models. The preliminary results show that the proposed approach outperforms traditional machine learning algorithms.
關聯:
the 24th IEEE Wireless and Optical Communication Conference (WOCC’15)，Taipei，P.159-163，2015
如有問題歡迎與系統管理員聯繫 
