B1410
Title: Stock price prediction using temporal graph model with value chain data
Authors: Chang Liu - University of Trento (Italy) [presenting]
Sandra Paterlini - University of Trento (Italy)
Abstract: Stock price prediction is crucial 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. A neural network-based stock return prediction method is introduced, 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. The LSTM-GCN model is evaluated on two datasets consisting of Eurostoxx 600 and S\&P 500 constituents. The 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.
