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

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...

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 fluctuations, but if the impact of all news is directly added, Models can be corrupted by noise

At the same time, the length of the news text is long, and the dimension of the generated matrix is ​​too high, which can easily cause the disaster of dimensionality.

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