Stock market trend prediction using CBAM and CNN

Abstract

In recent years, deep learning has been increasingly used to analyze financial data. For deep learning to predict the buy, sell, and hold points of stocks are prone to over-fitting, unreasonable feature extraction, and other issues. This paper builds a CBAM-CNN model based on Convolutional Neural Network (CNN) and Convolutional Block Attention Module (CBAM) to predict the buy, sell and hold points. In order to verify the applicability and superiority of the proposed method, the shares of Dao 30 and SHH 50 from stock listing to August 11, 2021 are selected, and the accuracy of the deep learning algorithm is evaluated using confusion matrix, weighted F1 score, and Kappa coefficient. The analysis results show that this algorithm has a high classification prediction accuracy because it can identify most of the buy and sell instances and therefore has a better effect. In addition, compared with CNN that do not use the CBAM attention mechanism, classification performance is significantly improved. The results from this analysis can help investors determine their better investment strategies.