Stock Price forecasting based on Quantum Particle swarm optimization

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