Industry experts as well as academic scholars have directed substantial attention toward researching stock market volatility. Most of the past research has focused on using singular features such as closing prices, opening prices, or news stories to predict stock movement. In recent times, there has been a growing interest in using multimodal graph neural networks which can analyze a variety of features. Most of these methods focus on node aggregation to extract relevant features from related stocks to further improve model accuracy. The current state-of-the-art model-ML-GAT: Multilevel Graph Attention Model-constructs a graph network between the stocks using Wikidata relations. It uses multiple layers of graph attention to aggregate features such as historical price features and current news. However, the high number of intercompany relationships in ML-GAT may include irrelevant and noisy edges. It also uses individual attention coefficients for each layer, leading to inefficient utilization of computational resources. To overcome these challenges, a Sparse Graph Attention Network for Stock Prediction (SGAT-SP) is proposed in this paper. SGAT-SP uses a single set of attention coefficients to reduce training and inference time. It assigns a binary mask to every edge which represents whether they will be used for node aggregation to reduce noisy edges. The proposed approach achieves an average accuracy score of 0.83, a slight improvement over ML-GAT, which has an accuracy score of 0.827. Additionally, it significantly reduces inference time by 85%, resulting in faster results and decreased computational expenses.
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