MONEY: Ensemble learning for stock price movement prediction via a convolutional network with adversarial hypergraph model

Stock price prediction is challenging in financial investment, with the AI boom leading to increased interest from researchers. Despite these recent advances, many studies are limited to capturing the time series characteristics of price movement via recurrent neural networks (RNNs) but neglect other critical relevant factors, such as industry, shareholders, and news. On the other hand, graph neural networks have been applied to a broad range of tasks due to their superior performance in capturing complex relations among entities and representation learning. This paper investigates the effectiveness of using graph neural networks for stock price movement prediction. Inspired by a recent study, we capture the complex group-level information (co-movement of similar companies) via hypergraphs. Unlike other hypergraph studies, we also use a graph model to learn pairwise relations. Moreover, we are the first to demonstrate that this simple graph model should be applied before using RNNs, rather than later, as prior research suggested. In this paper, the long-term dependencies of similar companies can be learnt by the next RNNs, which augments their predictability. We also apply adversarial training to capture the stochastic nature of the financial market and enhance the generalisation of the proposed model. Hence, we contribute with a novel ensemble learning framework to predict stock price movement, named MONEY. It is comprised of (a) a Graph Convolution Network (GCN), representing pairwise industry and price information and (b) a hypergraph convolution network for group-oriented information transmission via hyperedges with adversarial training by adding perturbations on inputs before the last prediction layer. Real-world data experiments demonstrate that MONEY significantly outperforms, on average, the state-of-the-art methods and performs particularly well in the bear market.