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Stock trend prediction method of fused graph convolutional network

A convolutional network and trend forecasting technology, applied in forecasting, neural learning methods, biological neural network models, etc., can solve the problems of traditional models such as real-time performance and unsatisfactory prediction accuracy

Inactive Publication Date: 2021-09-14
HARBIN UNIV OF SCI & TECH
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Problems solved by technology

[0003] The purpose of the present invention is to propose a stock trend prediction method that integrates graph convolutional networks, which solves the problem of unsatisfactory real-time performance and prediction accuracy of traditional models

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  • Stock trend prediction method of fused graph convolutional network
  • Stock trend prediction method of fused graph convolutional network
  • Stock trend prediction method of fused graph convolutional network

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Embodiment Construction

[0039] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some of the embodiments of the present invention, not all of them. Based on this The embodiments in the invention, and all other embodiments obtained by persons of ordinary skill in the art without creative efforts, all belong to the scope of protection of the present invention.

[0040] Such as Figure 1-Figure 3 As shown, an embodiment of a stock trend prediction method fused with a graph convolutional network described in this embodiment includes the following steps.

[0041] Step 1. Use knowledge in the financial field to construct various relationship graphs between stocks, such as equity graphs, industry graphs, and topical graphs.

[0042] Equity Chart G X = (V,E X , A X ) is used to encode the equity impact, a...

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Abstract

The invention provides a stock trend prediction method based on a fused graph convolutional network. The method comprises the following steps: 1, constructing a plurality of relational graphs among stocks by using financial domain knowledge; 2, extracting cross effect features based on the relation graphs by using a graph convolutional network GCN; 3, connecting the cross effect features generated in the step 2 with stock historical data to serve as combined features; 4, inputting the combined features generated in the step 3 into a long short-term memory (LSTM) network, and establishing a time correlation model of the stock prices; and 5, superposing a full connection layer with a sigmoid activation function on the LSTM to obtain a prediction trend of the stock set. According to the method, the problem that related stocks have a cross effect neglected by previous research is considered, and the prediction precision is effectively improved.

Description

technical field [0001] The invention relates to a stock trend prediction method fused with a graph convolutional network, which belongs to the field of data mining. Background technique [0002] Both traditional finance and modern behavioral finance believe that stock price fluctuations are driven by information. Information affects investors' beliefs and behaviors, thereby changing the trend of stocks. Therefore, understanding how the stock market converts information into stock prices is crucial in stock forecasting. In recent years, researchers have used machine learning or deep learning to model the correlation between various information and stock prices. However, the core assumption of these algorithms is that stocks are independent of each other. They mainly extract the autocorrelation of stocks based on the historical information of a single stock itself, while ignoring the dynamic impact of the cross-effect of stocks over time on stock prices. Contents of the i...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06Q40/04G06N3/04G06N3/08
CPCG06Q10/04G06Q40/04G06N3/049G06N3/08G06N3/048G06N3/045
Inventor 李鹏刘伟尹莉莉
Owner HARBIN UNIV OF SCI & TECH
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