Journal
QUANTITATIVE FINANCE
Volume 19, Issue 9, Pages 1507-1515Publisher
ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
DOI: 10.1080/14697688.2019.1622287
Keywords
Attention; LSTM; Stock price; Prediction
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Funding
- Huazhong University of Science and Technology Double First-Class Funds for Humanities and Social Sciences
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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.
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