Stock Movement Prediction Algorithm Based on Deep Learning

Abstract

Abstract: To improve the accuracy of stock movement prediction， this paper proposes a stock movement prediction algorithm AACL（Adversarial Attentive CNN-LSTM）which utilizes CNN and LSTM for feature extraction and combines attention mechanism and adversarial training. The algorithm uses CNN to extract the overall trend information of the stock， LSTM to extract the short-term fluctuation information of the stock， and connects multiple stocks through the attention mechanism to capture the rising and falling relationship between stocks. The algorithm also introduces adversarial training to improve the robustness of the algorithm by interfering the data. To verify the effectiveness of the AACL algorithm， experiments are carried out on three data sets KDD17， ACL18， and China50， and compared with existing algorithms. Experiments results show that the algorithm proposed in this paper can obtain the best result.

Key words: neural network, attention mechanism, adversarial training, stock movement prediction

ZHOU Run-jia. Stock Movement Prediction Algorithm Based on Deep Learning[J]. Computer and Modernization, 2023, 0(01): 69-73.

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