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.

中文翻译：

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使用加权时间图神经网络的企业投资预测

企业投资是企业财务决策的重要组成部分，影响着企业未来的利润和价值。预测企业投资对于资本市场投资者了解企业未来的经营和发展具有重要意义。许多研究人员研究了独立的预测方法。然而，各个公司在实际决策过程中会互相模仿对方的投资。这种投资趋同现象表明个体企业之间的投资相关性，在这些现有方法中被忽略了。在本文中，我们首先通过我们设计的双向固定效应模型识别多变量序列中的关键变量，用于精确的企业网络构建。然后，我们提出了一种加权时间图神经网络，称为加权时间图神经网络（WTGNN），用于企业网络上的图学习和投资预测。WTGNN 通过注意加权采样和多变量时间序列聚合来提高图卷积能力。我们使用真实世界的财务报告数据进行了广泛的实验。结果表明，WTGNN 可以实现出色的图学习性能，并在投资预测任务中优于现有方法。