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Exploring the attention mechanism in LSTM-based Hong Kong stock price movement prediction.
- Source :
- Quantitative Finance; Sep2019, Vol. 19 Issue 9, p1507-1515, 9p, 6 Diagrams, 7 Charts, 1 Graph
- Publication Year :
- 2019
-
Abstract
- State-of-the-art methods using attention mechanism in Recurrent Neural Networks have shown exceptional performance targeting sequential predictions and classifications. We explore the attention mechanism in Long–Short-Term Memory (LSTM) network based stock price movement prediction. Our proposed model significantly enhances the LSTM prediction performance in the Hong Kong stock market. The attention LSTM (AttLSTM) model is compared with the LSTM model in Hong Kong stock movement prediction. Further parameter tuning results also demonstrate the effectiveness of the attention mechanism in LSTM-based prediction method. [ABSTRACT FROM AUTHOR]
- Subjects :
- STOCK prices
RECURRENT neural networks
STOCK exchanges
MACHINE learning
Subjects
Details
- Language :
- English
- ISSN :
- 14697688
- Volume :
- 19
- Issue :
- 9
- Database :
- Complementary Index
- Journal :
- Quantitative Finance
- Publication Type :
- Academic Journal
- Accession number :
- 137968563
- Full Text :
- https://doi.org/10.1080/14697688.2019.1622287