Xue Xing: Stock Price Forecasting Using Rbf Neural Networks and Hybrid Particle Swarm
نویسنده
چکیده
With the characteristics of nonlinearity and randomness, stock prices change with a strong feature of disorder, and its mathematical model is often complex which makes it difficult to accurately determine the price or contain chaos. One single forecast method can only describe the stock price information partially, but fails to reflect the overall picture. In this paper, a method of Radial Basis Function (RBF) neural network and Hybrid Particle Swarm Optimization is proposed to forecast the stock price. The Hybrid Particle Swarm Optimization algorithm is used to forecast the stock price and capture its linear changing trend. RBF neural network is used to forecast the nonlinear and random change rules. The results of the two are combined to get the stock price forecast. The simulation results show that compared with the single forecasting model, a combined forecasting model offers a more comprehensive description of the stock price change rules, improves the precision of stock price forecasting, well reflects the stock market trend, and has the ability to provide more constructive investment advice to investors.
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