Stock trend classification prediction method based on intelligent fusion calculation

A technology for classifying forecasts and stocks, applied in computing, computer components, finance, etc.

Pending Publication Date: 2020-04-03
XI AN JIAOTONG UNIV
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  • Abstract
  • Description
  • Claims
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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, el

Method used

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  • Stock trend classification prediction method based on intelligent fusion calculation
  • Stock trend classification prediction method based on intelligent fusion calculation
  • Stock trend classification prediction method based on intelligent fusion calculation

Examples

Experimental program
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Effect test

Embodiment

[0198] Select the stock data of Microsoft (Microsoft, stock code MSFT), a US listed company under the platform of Oriental Fortune (Choice data), from October 25, 2017 to March 20, 2019, a total of 351 sets (trading days) of data as experimental data set. At the same time, 14 technical indicators that may have a greater impact on the future trend of the stock and can be used for experimental calculations are selected, namely, the previous closing price, opening price, highest price, lowest price, closing price, turnover, average price, circulating market value, Price-to-cash ratio, price-to-sales ratio, price-to-book ratio, trading volume, price-earnings ratio, and turnover rate.

[0199] Table 1 Condition attribute labels

[0200]

[0201] Based on the historical data of the stock before the selected date, predict the rise and fall of the next trading day and use it as a decision attribute. These 14 technical indicators have different impacts on the future rise and fall ...

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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...

Claims

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Application Information

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IPC IPC(8): G06Q40/04G06K9/62
CPCG06Q40/04G06F18/24143G06F18/23213G06F18/24155G06F18/254
Inventor 闫涛韩崇昭贾勇张恺桐杨纪元
Owner XI AN JIAOTONG UNIV
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