Stock prediction method of integrated model based on high-speed transfer stock prediction

Abstract

The invention provides a stock prediction method based on an integrated model for high-speed transfer stock prediction. The method comprises the following steps: S1, establishing the integrated model for high-speed transfer stock prediction; s2, determining an individual classifier; s3, optimizing parameters of the individual classifier by using a differential evolution algorithm; s4, preprocessing the data, and performing factor screening and factor synthesis on the data; s5, training an individual classifier by using the data set; s6, predicting the data set by using the trained individual classifier; s7, using a linear model as a meta-learner, and training the linear model by using the new training set; s8, performing prediction by using the trained individual classifier to obtain a preliminary prediction result; and inputting the preliminary prediction result into the trained linear model to obtain a final prediction result. According to the stock prediction method of the integrated model based on high-speed transfer stock prediction, the integrated model is better trained, and the investment safety is further ensured.

Description

technical field [0001] The invention relates to the field of economic models, in particular to a stock forecasting method based on an integrated model of high-transfer stock forecasting. Background technique [0002] In the process of model construction, the existing technology mainly builds a generalized model of annual transfer forecast through the method of ensemble learning, and the accuracy rate is about 89%. In the process of factor feature selection, there are two commonly used methods in the existing technology: 1. ) Based on the selection of recognized factors with economic significance; 2) After model training, factors that have a greater impact on the implementation of high-speed transfers are extracted. [0003] Prior art has following shortcoming: [0004] 1. The causal relationship between factors cannot be expressed, and the single selection of factors that have a significant impact on high transfer rates destroys the causal relationship between factors. [...

AI Technical Summary

Problems solved by technology

[0004] 1. The causal relationship between factors cannot be expressed, and the single selection of factors that have a significant impact on high transfer rates destroys the causal relationship between factors

[0005] 2. The pertinence is not strong, and it is usually necessary to use annual data to establish a generalized model when building a model

[0006] 3. Lack of universality, model optimization requires manual parameter adjustment

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