Ensemble Learning with an Adversarial Hypergraph Model and a Convolutional Neural Network to Forecast Stock Price Variations

AI has increased scholarly interest in predicting financial stock prices, a tough task.Recurrent neural network (RNN) time series price movement analysis is common but ignores market, investor, and headline factors.Graph neural networks excel at capturing complicated relationships and learning representations, making them useful for a variety of applications.This study investigates the predictive capabilities of graph neural networks about stock prices.Recent studies motivated to utilize hypergraphs for capturing intricate group-level data, such as corporate mobility patterns.Using a graph model to obtain paired linkages sets us apart from previous studies on hypergraphs.Also, demonstrated that using RNNs after applying this fundamental graph model is not advisable, in contrast to previous research.This study demonstrates the potential of future Recurrent Neural Networks (RNNs) to acquire knowledge about the long-term interconnections across companies, leading to enhanced predictive capabilities.The proposed model is an innovative ensemble learning framework that created specifically for the purpose of forecasting stock values.Part, one consists of a Graph Convolution Network (GCN), which encodes price and industry pairs.Part two involves a hypergraph convolution network that conducts adversarial training by modifying inputs before the final prediction layer.This modification facilitates the transmission of group-oriented information through hyperedges.There is empirical evidence that suggests that the proposed model outperforms approaches that are considered to be state-of-the-art on average and demonstrates remarkable performance during times when the market is declining.The proposed framework beats the second-best baseline by increments of 1.14%, 3.63%, and 2.45% for three different five-days, ten days and twenty days trade periods.