Abstract
People have been interested in making profits from financial stock market prediction. However, stock market forecast has always been a challenging problem because of its uncertainty and volatility. We take a different approach by a model called recurrent convolutional neural networks (RCN) that combines the advantages of convolutions, sequence modeling, word embedding for stock price analysis and information extraction from financial news. We then combine RCN with technical analysis indicators to predict stock price. The results show that the technical analysis model combining with RCN performs better than the technical analysis alone. Besides, the prediction error of RCN is lower than that of Long-short term memory networks.
| Original language | English |
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| Title of host publication | Proceedings - 2017 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2017 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 160-165 |
| Number of pages | 6 |
| ISBN (Electronic) | 9781538642030 |
| DOIs | |
| State | Published - 09 05 2018 |
| Externally published | Yes |
| Event | 2017 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2017 - Taipei, Taiwan Duration: 01 12 2017 → 03 12 2017 |
Publication series
| Name | Proceedings - 2017 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2017 |
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Conference
| Conference | 2017 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2017 |
|---|---|
| Country/Territory | Taiwan |
| City | Taipei |
| Period | 01/12/17 → 03/12/17 |
Bibliographical note
Publisher Copyright:© 2017 IEEE.
Keywords
- Stock price prediction
- convolutional neural networks
- financial news
- long short term memory networks
- word to vector encoding