Stock Forecasting Using Sequential Models and GNN

Abstract

Forecasting stock prices, market trends, and associated emotions remains a complex challenge due to the market’s inherent volatility, nonlinearity, susceptibility to factors such as news events, and the limited availability of financial data. Stock prices are noisy, unpredictable, and sensitive to investor sentiment. To account for this, this study considers three models that effectively capture both the spatial relationships (similar sector stocks) between stocks and the temporal trends in their price movements, thereby addressing limitations in traditional forecasting methods. Specifically, we consider semiconductor stocks, which are known for their high volatility and strong correlation to technological advancements. The proposed models include Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) neural networks, a GNN-based architecture, and a Spatio-Temporal Graph Neural Network (ST-GNN). These models are trained, tested, and validated on datasets that combine historical data with sentiment insights from financial news sources. It utilizes a GRU-based temporal module to improve recognition of evolving market patterns. We demonstrate our model’s ability to adapt to dynamic financial environments and predict whether a stock closes at a higher or lower price than at the trading day open.