Abstract
Numerous studies show that the prices volatility and trading volume of stock have a positive correlation, and they show a nonlinear relationship. In this paper, through analyzing the stock volume, we use a nonlinear ARMA (n, m) model in forecasting the short-term stock price, and use the model's residual sum of squares as the fitness function, to identify the parameters of the model based on the modified Particle Swarm Optimization (PSO) algorithm. Different stocks' test data and the actual data are used to verify the accuracy of the model. The simulation results show that the model has good adaptability and high prediction accuracy with good overall fast convergence and strong approximation performance, and proves to be an effective short-term stock price prediction method.
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