Exploring the attention mechanism in LSTM-based Hong Kong stock price movement prediction

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.

Keywords:

• Attention
• LSTM
• Stock price
• Prediction

JEL Classification:

• G17
• C53
• C45

Acknowledgments

We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan X Pascal GPU used for this research.

Disclosure statement

No potential conflict of interest was reported by the authors.

ORCID

Shun Chen http://orcid.org/0000-0002-8318-7960

Lei Ge http://orcid.org/0000-0001-8715-0797

Additional information

Funding

This work is supported by Huazhong University of Science and Technology Double First-Class Funds for Humanities and Social Sciences.