Research on Stock Prediction and Stock Selection Method Based on Improved Ant Colony Algorithm

Stock forecasting and stock selection methods play an important role in the smart stock market, but there is the problem of inaccurate stock selection direction. The traditional ant colony algorithm cannot solve the stock prediction problem in the smart stock market, and the effect is not ideal. Therefore, this paper proposes research on stock prediction and stock selection methods based on improved ant colony algorithm, and analyzes the research on stock prediction and stock selection methods. Firstly, the swarm intelligence theory is used to locate the influencing factors, and the indicators is divided according to the requirements of stock prediction and stock selection methods, so as to reduce the interference factors in stock prediction and stock selection methods. Then, the swarm intelligence theory is used to form a scheme for improving the stock prediction and stock selection method of ant colony algorithm, and the results of stock prediction and stock selection method is comprehensively analyzed. The simulation results of MATLAB show that under certain evaluation criteria, the improved ant colony algorithm is superior to the traditional ant colony algorithm in terms of the accuracy of stock prediction and stock selection method, stock prediction and time of influencing factors of stock selection method.