  Stock price prediction is a crucial element in financial trading as it allows
traders to make informed decisions about buying, selling, and holding stocks.
Accurate predictions of future stock prices can help traders optimize their
trading strategies and maximize their profits. In this paper, we introduce a
neural network-based stock return prediction method, the Long Short-Term Memory
Graph Convolutional Neural Network (LSTM-GCN) model, which combines the Graph
Convolutional Network (GCN) and Long Short-Term Memory (LSTM) Cells.
Specifically, the GCN is used to capture complex topological structures and
spatial dependence from value chain data, while the LSTM captures temporal
dependence and dynamic changes in stock returns data. We evaluated the LSTM-GCN
model on two datasets consisting of constituents of Eurostoxx 600 and S&P 500.
Our experiments demonstrate that the LSTM-GCN model can capture additional
information from value chain data that are not fully reflected in price data,
and the predictions outperform baseline models on both datasets.
