LSTM-Based Stock Price Prediction

Hongwei Lu

doi:10.54097/fcis.v4i2.10208

Authors

Hongwei Lu

DOI:

https://doi.org/10.54097/fcis.v4i2.10208

Keywords:

Stock Market, Recurrent Neural Network, Long Short-term Memory

Abstract

Predicting stock prices is a job that researchers and analysts have been working on for many years. Investors have shown great interest in this area so that they can better manage their assets. Accurately predicting changes in stock prices in the market can generate huge economic benefits. In view of the high noise, nonlinearity and non-stationarity of stock price data, which makes it very difficult to accurately predict the stock price, this paper intends to use the long-short-term memory network (Long Short-Term Memory, LSTM) Recurrent Neural Network (RNN) architecture to establish A model that predicts the future value of a stock.

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