Predicting stock price movement using a DBN-RNN
This paper proposes a deep learning-based model to predict stock price movements. The proposed model is composed of a deep belief network (DBN) to learn the latent feature representation from stock prices, and a long short-term memory (LSTM) network to exploit long-range relations within the trading...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2021-10-01
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| Series: | Applied Artificial Intelligence |
| Online Access: | http://dx.doi.org/10.1080/08839514.2021.1942520 |