Stock Price Prediction using Deep Recurrent Model
DOI:
https://doi.org/10.54097/hset.v44i.7365Keywords:
DataSeries.cs, opening price, closing price, learning curveAbstract
In this paper, we aim to predict the stock trend using previous stock data. In specific, we perform LSTM training on the stock data of 2017 and 2018 by using the Sandp500 data as the base. The data includes the stock date, opening price, closing price, the highest price of the day, the lowest price of the day, and volume. Through these data we conducted 100 epoch training. After training, our LSTM can correctly predict the learning curve without any delay influence, with a satisfying performance. And we got the visual graph and chart to prove that the result is effective. The MSE value decreased from the original 3.026 to 0.238. This shows that the error of the result is small and the reliability is high. After LSTM training, it can help stock investment companies and related institutions and individuals to better avoid risks and provide individuals with high-credibility investment advice.
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