Stock trend classification prediction method based on intelligent fusion calculation

Abstract

The method comprises the following steps: performing discretization preprocessing on data in a complete data set of a target stock in a target time period by adopting an equidistant discretization algorithm and a one-dimensional K-Means clustering discretization algorithm; carrying out attribute reduction of the technical indexes; adopting a naive Bayes classifier and a K-nearest neighbor classifier, and according to the complete data set subjected to attribute reduction, carrying out classification prediction on the increase and decrease amplitude of the target stock in the next trading day;and performing decision fusion on the classification prediction results of the future increase and decrease of the target stock obtained by the two classifiers by using a D-S evidence combination rule, and finally taking the decision fusion result as a final classification prediction result of the future increase and decrease of the target stock. According to the invention, the prediction accuracyof various stock trend prediction methods based on a neural network, an SVM and the like can be obviously improved. When the method is used for constructing a multi-factor stock selection model, thenonlinear relationship between various stock index data and stock income is more significant.

Description

technical field [0001] The invention belongs to the technical field of artificial intelligence, and in particular relates to a stock trend classification and prediction method based on intelligent fusion calculation. Background technique [0002] Traditional qualitative investment strategies mainly rely on investors to make judgments on the stock properties of listed companies, which are often limited by their professional capabilities and make judgment errors. Moreover, due to the limited energy of investors, the number of stock samples they can research is usually insufficient. The biggest difference between quantitative investment and qualitative investment is that it uses a computer to analyze stock data, supplemented by a certain mathematical model, so as to achieve a stable and profitable strategy. This strategy not only improves the stability of the mathematical model, but also saves a lot of manpower and material resources. The multi-factor stock selection model is...

AI Technical Summary

Problems solved by technology

[0005] However, all the above-mentioned stock trend prediction methods fail to fully tap the correlation between the existing data indicators and the future trend of the stock, eliminate redundant data indicators, and predict the trend based on the stock index that has an important impact on the future trend, so that Further improve the prediction accuracy

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