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https://ah.lib.nccu.edu.tw/handle/140.119/98224
題名: | Corporate Default Prediction via Deep Learning | 作者: | Yeh, Shu-Hao;Wang, Chuan-Ju;Tsai, Ming-Feng 蔡銘峰 |
貢獻者: | 資科系 | 日期: | Jul-2014 | 上傳時間: | 22-Jun-2016 | 摘要: | 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: | Proceedings of the 34th International Symposium on Forecasting (ISF `14), 2014 | 資料類型: | conference |
Appears in Collections: | 會議論文 |
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