Stock price fluctuation prediction method of convolutional neural network combining financial news

A convolutional neural network and prediction method technology, applied in the field of convolutional neural network stock price fluctuation prediction, can solve problems such as high matrix dimension, long news text length, dimension disaster, etc.

Inactive Publication Date: 2018-10-23
SHANDONG UNIV OF FINANCE & ECONOMICS
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AI Technical Summary

Problems solved by technology

The use of multiple information sources can obtain higher prediction accuracy than single information, and the advancement of natural language processing technology has made it possible to study the impact of financial news on stock market price fluct...

Method used

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  • Stock price fluctuation prediction method of convolutional neural network combining financial news
  • Stock price fluctuation prediction method of convolutional neural network combining financial news
  • Stock price fluctuation prediction method of convolutional neural network combining financial news

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Embodiment Construction

[0048] The present invention provides a convolutional neural network stock price fluctuation prediction method combined with financial news, such as figure 1 As shown, the methods include:

[0049] S1: Scan the corpus, preset keywords, set the length of the scanning window, and configure the co-occurrence matrix within the set scanning window;

[0050] It is assumed that words with high correlation are more likely to appear in the same document, so each word can be represented by surrounding words. Set the length of the window to be n; scan the sentences in the set window to obtain the number of times X that keywords i and j appear in the set window ij . Obtain the co-occurrence matrix X after traversing the entire corpus.

[0051] S2: Configure the co-occurrence probability based on the co-occurrence matrix;

[0052] Calculate the co-occurrence probability of keywords i, j

[0053]

[0054] This ratio reflects the correlation between words. The words i and j are called...

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Abstract

The invention provides a stock price fluctuation prediction method of a convolutional neural network combining financial news. According to the method, natural-language processing technology is utilized to extract features in relevant news, and thus an association degree of the financial news and a stock price trend is analyzed and observed. In combination with valid information of news reports, the invention provides the stock price fluctuation prediction method based on the convolutional neural network. Firstly, word segmentation is carried out on the news, main events are extracted, top 3000 words appearing most frequently are used as keywords, and a Glove model is used to indicate the same as low-dimensional dense word vectors; then news features are correlated with stock prices, timeis divided into a short time period, a medium time period and a long time period, the convolutional neural network is used to simulate short-term and long-term effects of the news events on stock price changes; and finally, up and down situations of a stock are predicted through a trained model.

Description

technical field [0001] The invention relates to the field of big data, in particular to a convolutional neural network stock price fluctuation prediction method combined with financial news. Background technique [0002] The stock market has become an important part of the financial market because of its relatively flexible operating characteristics. The high-risk and high-return characteristics of the stock market have attracted many economists and investment enthusiasts, but in general, investors are rarely able to accurately judge the price changes in the stock market. Therefore, it is of great theoretical and practical significance to study and build a scientific model with high prediction accuracy to effectively grasp the fluctuation rules of the stock market and help investors avoid risks and increase returns. [0003] For the return of stocks, our expectation is that the rate of return is the largest while the risk is the smallest. This requires an effective method t...

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Application Information

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IPC IPC(8): G06Q10/04G06Q30/02G06Q40/04
CPCG06Q10/04G06Q30/0278G06Q40/04
Inventor 王玉洁刘慧张彩明郭强刘鑫
Owner SHANDONG UNIV OF FINANCE & ECONOMICS
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