Abstract
With the maturity of neural network theory, it provides new ideas and methods for the prediction and analysis of stock market investment. The purpose of this paper is to improve the accuracy of stock market investment prediction, we build neural network model and genetic algorithm model, study the law of stock market operation, and improve the effectiveness of neural network and genetic algorithm. Through the empirical research, it is found that the neural network model can make up for the shortcomings of the traditional algorithm through the optimization of genetic algorithm.
Highlights
In the western countries with developed financial markets, the stock market has been running for more than 300 years, which to a certain extent reflects the development and trend of social economy in a specific period of time, and has a profound impact on economic development
In the process of building algorithm model, different types of algorithms are integrated into neural network toolbox, so as to improve the convenience of algorithm design
We found that the neural network model has high accuracy in prediction
Summary
In the western countries with developed financial markets, the stock market has been running for more than 300 years, which to a certain extent reflects the development and trend of social economy in a specific period of time, and has a profound impact on economic development. Investors need to take a greater risk to get a higher return. In this context, a more reasonable stock market forecasting method can effectively reduce risk and increase returns. It is of great significance to strengthen the research of stock market forecasting methods. The number of investors in the stock market is increasing, and investors hope to obtain an effective analysis method to maximize the ratio of return to risk [2]. With the general investors more and more understanding of the basic rules of stock market investment, as well as the increasing number of stock market investment researchers, stock market forecasting methods
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