Stock price forecasting model based on modified convolution neural network and financial time series analysis

Abstract

Summary To forecast the future trend of financial activities through its rules, a convolutional neural network (CNN) is used to forecast stock index. Firstly, a CNN stock index prediction model is constructed, the structural parameter relationship of the CNN model is analyzed, and a CNN model algorithm is implemented. Secondly, the influence of model parameters on prediction results is discussed, and the stock index prediction model based on CNN‐support vector machine (SVM) is established. At last, the empirical analysis is made, and the results show that the two prediction models are feasible and effective. It is concluded that the use of neural networks for financial prediction can deal with the continuous and categorical prediction variables and obtain good prediction results.