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

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.