Deep learning for stock market prediction from financial news articles
| Autor: | Alexandre G. Evsukoff, Manuel R. Vargas, Beatriz Souza Leite Pires de Lima |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Stock market prediction
Artificial neural network Computer science business.industry Deep learning Context (language use) 02 engineering and technology Machine learning computer.software_genre Convolutional neural network Recurrent neural network 020204 information systems 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Stock market Artificial intelligence Types of artificial neural networks business computer |
| Zdroj: | 2017 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA). |
| DOI: | 10.1109/civemsa.2017.7995302 |
| Popis: | This work uses deep learning methods for intraday directional movements prediction of Standard & Poor's 500 index using financial news titles and a set of technical indicators as input. Deep learning methods can detect and analyze complex patterns and interactions in the data automatically allowing speed up the trading process. This paper focus on architectures such as Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), which have had good results in traditional NLP tasks. Results has shown that CNN can be better than RNN on catching semantic from texts and RNN is better on catching the context information and modeling complex temporal characteristics for stock market forecasting. The proposed method shows some improvement when compared with similar previous studies. |
| Databáze: | OpenAIRE |
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