AE-ACG: A novel deep learning-based method for stock price movement prediction

Shicheng Li, Xiaoyong Huang, Zhonghou Cheng, Wei Zou and Yugen Yi

Finance Research Letters, 2023, vol. 58, issue PA

Abstract: This paper proposes a method named AE-ACG for stock price movement prediction. In AE-ACG, the convolutional neural network (CNN) and gated recurrent unit (GRU) are combined to design a base layer, which is embedded in the autoencoder (AE) framework, to efficiently extract features from financial time series data. Furthermore, skip connection links encoding and decoding to leverage hierarchical features. Attention mechanism (AM) also distinguishes the importance of historical data across periods. Extensive experiments demonstrated that the proposed model is effective in predicting price movements, showing advantages over some mainstream methods.

Keywords: Deep learning; Financial time series forecasting; Stock price movement; Autoencoder (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:eee:finlet:v:58:y:2023:i:pa:s1544612323006761

DOI: 10.1016/j.frl.2023.104304

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