Deep learning stock market prediction method combined with financial news

Abstract

The invention relates to a deep learning stock market prediction method combined with financial news, and the method comprises the following steps: S1, a web crawler technology is utilized for financial news to crawl relevant financial information corresponding to a stock from Sina financial news and Netease financial news and save the information to a local database, and a financial news document database is formed; S2, the financial news information is processed, and news emotion analysis is performed; S3, an RNN deep learning network based on LSTM is provided; S4, training characteristics are extracted; and S5, model training and prediction are performed. According to the invention, a technology of news emotion analysis is utilized, the RNN deep learning network based on LSTM is adopted, the most common technical indexes used by financial market investors are combined with the method to perform feature vector prediction, and good effect is achieved.

Description

technical field [0001] The invention relates to the field of deep learning forecasting, in particular to a deep learning stock market forecasting method combined with financial news. Background technique [0002] The securities market and financial investment occupy an important position in modern society. The modeling and forecasting research of the stock market is of great significance to my country's economic development and financial construction, and has always been concerned by people. The stock market is affected by many factors such as national policies, economic situation, company development status and investor psychology. [0003] Common stock market forecasting methods include securities investment analysis methods, time series forecasting analysis methods, and nonlinear forecasting methods. Securities investment analysis method includes fundamental analysis method and technical analysis method. Fundamental analysis mainly includes macroeconomic analysis and in...

AI Technical Summary

Problems solved by technology

Keyword recognition is to use clearly defined influential words that appear in the text, such as "happy", "sad", "sad", etc., to affect classification; lexical association is to find and analyze words that influence each other; statistical methods control Elements in machine learning, such as latent semantic analysis, SVM, word bag model, etc., but pure text sentiment analysis cannot be directly applied to stock forecasting, because stock price changes are affected by many complex factors

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