A Stock Price Foresting Using LSTM Based on Attention Mechanism

DOI

10.2991/978-94-6463-052-7_162How to use a DOI?

Keywords

Stock price prediction; LSTM; Attention mechanism; Rate change

Abstract

Stock price prediction has been a hit subject in recent decades. Many researchers find different methods to predict stock price. LSTM is an excellent variant model of RNN, but single LSTM can only process a single form of data and lacks the ability to process multiple mixed forms of data. Considering that stocks represent the financial market, the exchange rate would have a particular impact on the financial market, so rate change affects stock price movement. Therefore, attention mechanism could introduce exchange rate into LSTM, so we produce a hybrid LSTM module based on attention mechanism to predict stock price. We find that the RMSE and MSE of hybrid LSTM are lower than others.

Copyright

© 2022 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Xiaofei Wu
PY  - 2022
DA  - 2022/12/27
TI  - A Stock Price Foresting Using LSTM Based on Attention Mechanism
BT  - Proceedings of the 2022 International Conference on Economics, Smart Finance and Contemporary Trade (ESFCT 2022)
PB  - Atlantis Press
SP  - 1467
EP  - 1476
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6463-052-7_162
DO  - 10.2991/978-94-6463-052-7_162
ID  - Wu2022
ER  -