Stock price prediction method based on LSTM neural network

Abstract

The embodiment of the invention relates to a stock price prediction method based on an LSTM neural network. The stock price prediction method based on the LSTM neural network comprises the steps of obtaining first feature data of a first target stock on a current trading day; performing normalization processing on the first feature data; inputting the first feature data after normalization processing into a trained stock prediction model to acquire the price trend of the target stock on the next trading day, wherein the stock prediction model comprises a first Lstm layer, a first Dropout layer, a second Lstm layer, a third Lstm layer, a second Dropout layer and a Dense layer, which are sequentially connected according to a Sequence sequence module built in Keras. According to the stock price prediction method based on the LSTM neural network provided by the embodiment of the invention, the accuracy of predicting the rising and falling trend of the stock is obviously improved, and the prediction effect of the LSTM network model tends to be stable along with the increase of historical stock data.

Description

technical field [0001] The embodiment of the present application relates to the technical field of computer software development, in particular to a stock price prediction method based on LSTM neural network. Background technique [0002] With the development of economic globalization, the stock market is increasingly favored by Chinese investors. With the development and strong demand of the financial market, stock price trend forecasting has attracted much attention from academia and the industry, marked by interconnection, digitization, and intelligence. The information technology innovation in China is deeply integrated, and the trend of combining quantitative technology and finance is becoming more and more obvious. While investing in stocks has high returns, it is also accompanied by high risks. There are many internal and external factors that affect stock market fluctuations and are difficult to quantify. Processing massive and complicated stock data requires a large...

AI Technical Summary

Problems solved by technology

While investing in stocks has high returns, it is also accompanied by high risks. There are many internal and external factors that affect stock market fluctuations and are difficult to quantify. Processing massive and complicated stock data requires a large amount of engineering. Therefore, traditional non-artificial intelligence methods are more effective in stock price prediction. often unsatisfactory

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