Corporate investment prediction using a weighted temporal graph neural network

Abstract

Corporate investment is an important part of corporate financial decision-making and affects the future profit and value of the corporation. Predicting corporate investment provides great significance for capital market investors to understand the future operation and development of a corporation. Many researchers have studied independent prediction methods. However, individual firms imitate each other's investment in the actual decision-making process. This phenomenon of investment convergence indicates investment correlation among individual firms, which is ignored in these existing methods. In this article, we first identify key variables in multivariate sequences by our designed two-way fixed effects model for precise corporate network construction. Then, we propose a weighted temporal graph neural network called weighted temporal graph neural network (WTGNN) for graph learning and investment prediction over the corporate network. WTGNN improves the graph convolution capability by weighted sampling with attention and multivariate time series aggregation. We conducted extensive experiments using real-world financial reporting data. The results show that WTGNN can achieve excellent graph learning performance and outperforms existing methods in the investment prediction task. This article is categorized under: Technologies > Prediction Technologies > Machine Learning Application Areas > Business and Industry