Deep Belief Networks for Predicting Corporate Defaults

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Title:	Deep Belief Networks for Predicting Corporate Defaults
Authors:	Yeh, Shu-Hao;Wang, Chuan-Ju;王釧茹;Tsai, Ming-Feng
Contributors:	臺北市立大學資訊科學系
Date:	2015-10
Issue Date:	2019-02-14
Abstract:	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.
Relation:	the 24th IEEE Wireless and Optical Communication Conference (WOCC’15)，Taipei，P.159-163，2015
Appears in Collections:	[Department of Computer Science] Proceedings

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