A Stock Price Foresting Using LSTM Based on Attention Mechanism

Xiaofei Wu ()
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Xiaofei Wu: Minzu University of China

A chapter in Proceedings of the 2022 International Conference on Economics, Smart Finance and Contemporary Trade (ESFCT 2022), 2022, pp 1467-1476 from Springer

Abstract: 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.

Keywords: Stock price prediction; LSTM; Attention mechanism; Rate change (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:spr:advbcp:978-94-6463-052-7_162

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DOI: 10.2991/978-94-6463-052-7_162

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