Stock ranking prediction using a graph aggregation network based on stock price and stock relationship information

Abstract

The volatility of stock prices makes it difficult to predict stock price trends correctly. This volatility is affected by many factors, including other stocks related to it. Stock prediction based on graph learning uses various graph neural networks to learn how stocks interact to provide more information. However, they tend to adopt statically defined stock relations based on prior knowledge (such as industry relations and Wiki relations), making it difficult to capture the interplay between stocks over time. In addition, their predictions mostly rely on a single stock relationship, while many types of stock relationships affect the volatility of stock prices in a complex and intertwined manner. A new price similarity relation graph is first constructed using the multi-view stock price similarity to capture dynamic stock relationships. Based on three stock graphs (price similarity, Wiki and industry), we further propose a multi-relational graph attention ranking (MGAR) network. In MGAR, the multi-graph aggregation is achieved by applying adaptive learning mechanisms, thereby forming effective relation embeddings. When combined with the captured price trend embedding, MGAR model gives a ranking list of future returns and chooses K stocks with the best returns to trade so that the return on investment is maximized. Extensive experiments demonstrate that MGAR method outperforms state-of-the-art stock predicting solutions, achieving average returns of 164% and 236% on two real datasets, respectively.