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    政大機構典藏 > 理學院 > 資訊科學系 > 會議論文 >  Item 140.119/98224
    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/98224

    Title: Corporate Default Prediction via Deep Learning
    Authors: Yeh, Shu-Hao;Wang, Chuan-Ju;Tsai, Ming-Feng
    Contributors: 資科系
    Date: 2014-07
    Issue Date: 2016-06-22 16:21:20 (UTC+8)
    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: Proceedings of the 34th International Symposium on Forecasting (ISF '14), 2014
    Data Type: conference
    Appears in Collections:[資訊科學系] 會議論文

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