Prediction of Stock Prices Based on the LSTM Model

Guanze Shao ()
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Guanze Shao: Beijing Foreign Studies University

A chapter in Proceedings of the 8th International Conference on Financial Innovation and Economic Development (ICFIED 2023), 2023, pp 377-387 from Springer

Abstract: Abstract This paper focuses on improving the structure of the LSTM model and optimizing its parameters to improve its accuracy in predicting stock movements, as well as investigating the effectiveness of the LSTM neural network in predicting weekly and daily data for US stocks. On the one hand, the difference between the two models is analyzed and compared to verify the effect of different data sets on the prediction results; on the other hand, it provides suggestions on the selection of data sets for LSTM stock prediction research to ameliorate the accuracy of stock prediction. This study used a modified LSTM neural network model to predict stock price trends using a multi-series stock prediction method. The experimental results confirmed that the weekly data performed better than the daily data, with an average accuracy of 52.8% for the daily data and 58% for the weekly data, and the stock prediction accuracy was higher when the weekly data was used to train the LSTM model.

Keywords: Long Short-Term Memory (LSTM); stock price forecast; time series; short-term price (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:spr:advbcp:978-94-6463-142-5_42

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DOI: 10.2991/978-94-6463-142-5_42

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