Journal Article

Stock intelligent investment strategy based on support vector machine parameter optimization algorithm

Li, Xuetao, Sun, Yi

2020

Overview

The changes in China’s stock market are inseparable from the country’s economic development and macroeconomic regulation and control and have far-reaching significance in promoting China’s national economic growth. Compared with the Western developed capital market, China’s current stock market’s main smart investment strategy still has certain defects. Based on the SVM model, this paper establishes a predictive model that combines kernel parameters and parameter optimization to model. The mesh search method, genetic algorithm, and particle swarm optimization algorithm are used to optimize the parameters of the SVM under various kernel functions such as radial basis kernel function. The algorithm and particle swarm optimization algorithm optimize the parameters of the SVM to strengthen the applicability of the model in practice. The empirical results show that under the three-parameter optimization algorithms, the prediction results are higher than the random prediction accuracy, which indicates that it is effective to optimize the model by adjusting the parameters of the SVM. Among them, the SVM using the genetic algorithm parameter optimization under the radial basis kernel function shows the better prediction effect, which is the closest to the real value in the stock market forecast. The particle swarm algorithm supports the vector machine to predict the effect is slightly lower than the grid. Search method. In addition, through comparison experiments, the guess accuracy of BP neural network is worse than that of the support vector machine model before the adjustment. Finally, this paper uses the well-trained model to plan the stock smart investment plan.

• Publisher: Springer London,Springer Nature B.V
• Subject: Artificial Intelligence / Computational Biology/Bioinformatics / Computational Science and Engineering / Computer Science / Data Mining and Knowledge Discovery / Deep Learning for Big Data Analytics / Economic development / Economic forecasting / Finite element method / Genetic algorithms / Image Processing and Computer Vision / Investment policy / Investment strategy / Investments / Kernel functions / Mathematical models / Neural networks / Optimization algorithms / Parameters / Particle swarm optimization / Prediction models / Probability and Statistics in Computer Science / Search methods / Securities markets / Support vector machines