Abstract
The stock market is a place that brings investors opportunity to gain profit. Meanwhile, the stock market also brings investors high risks, which requires machine learning methods to improve the accuracy of prediction. This paper uses the Long Short-Term Memory (LSTM) model to predict stock prices. In the study, the daily stock historical data of Sinopec, Moutai and SPD Bank in the past 21 years are used as samples, including the date, the trade volume, the highest price, the lowest price, and the opening and closing prices. After the LSTM model has been trained, the three companies' predictions of opening price have achieved good results, and the predicted opening price curve and the actual opening price curve seem to be quite consistent. In terms of evaluation indicators, MAPE of the three companies is less than 1%. These results can provide some help for investors to predict stocks and shed light on guiding further studies.
Publisher
Darcy & Roy Press Co. Ltd.
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