Stock market trend prediction using CBAM and CNN

Yong Wang; Zhiyu Xu; Yisheng Li

doi:10.1117/12.2667378

28 March 2023 Stock market trend prediction using CBAM and CNN

Yong Wang, Zhiyu Xu, Yisheng Li

Proceedings Volume 12566, Fifth International Conference on Computer Information Science and Artificial Intelligence (CISAI 2022); 125661C (2023) https://doi.org/10.1117/12.2667378
Event: Fifth International Conference on Computer Information Science and Artificial Intelligence (CISAI 2022), 2022, Chongqing, China

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.

Citation Download Citation

Yong Wang, Zhiyu Xu, and Yisheng Li "Stock market trend prediction using CBAM and CNN", Proc. SPIE 12566, Fifth International Conference on Computer Information Science and Artificial Intelligence (CISAI 2022), 125661C (28 March 2023); https://doi.org/10.1117/12.2667378

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KEYWORDS

Education and training

Deep learning

Data modeling

Visual process modeling

Convolutional neural networks

Neurons

Deep convolutional neural networks

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