Abstract: Neural networks have been used in various fields, such as image recognition, cognitive science, and genomics. In this paper, we use a multi-layer perceptron model (MLP) to predict the stock market. Before making predictions, we need to process the collected data, use canonical correlation analysis to discuss the correlation between influencing factors, and use some of these factors as independent variables with the time axis. The trading volume in the stock market is input as labels into the MLP model. The results showed that the accuracy rate on the test set was 87%, and the effectiveness of the model was verified by comparing the predicted results with actual data
Keywords: Keywords- Stock Market; Multi-layer Perceptron Prediction Model; Multi-Factor
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