Stock Price Forecasting Using RBF Neural Network and Hybrid Particle Swarm Optimization

2016 ◽  
Author(s):  
Guest Editor Jianping Du
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Stock Price ◽  
Rbf Neural Network ◽  
Particle Swarm ◽  
Price Forecasting ◽  
Swarm Optimization ◽  
Hybrid Particle ◽  
Stock Price Forecasting ◽  
Hybrid Particle Swarm Optimization

scholarly journals Stock Price forecasting based on Quantum Particle swarm optimization

2021 ◽  
Vol 04 (10) ◽  
Author(s):  
Guohui Huang ◽  
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Stock Price ◽  
Quantum Particle ◽  
Particle Swarm ◽  
Price Forecasting ◽  
Swarm Optimization ◽  
Stock Price Forecasting ◽  
Quantum Particle Swarm Optimization ◽  
Bee Colony

The stock market is very volatile, so the change of the stock price is also widely concerned by investors. In this paper, a new stock price forecasting model based on Quantum Particle Swarm Optimization(QPSO) , Quantum Bee Colony Optimization Algorithm(QABC) and Quantum Fruit Fly Optimization Algorithm (QFOA) is proposed. The three methods all use BP neural network to adjust the parameters of particle swarm, bee colony and Drosophila to reach the optimal parameters. Taking the daily closing price of CITIC Securities and Tianfeng Securities, a large-scale and a small-scale securities company, as the object of empirical analysis, comparing the accuracy of the three methods in predicting stocks, it also analyzes whether the size of the company has an effect on the accuracy of the model. The results show that the prediction effect of qpso is the best, and the size of the company has some influence on the prediction effect.

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