Knowledge graph (KG) techniques have achieved successful results in many tasks, especially in semantic web and natural language processing domains. In recent years, representation learning on KG has been successfully applied to e-business applications, such as event-driven automatic investment strategies. However, there is still limited research about learning events’ influence on KG for modern quantitative investment. In this paper, we propose a novel event influence learning framework to predict stock market trends, called ST-Trend, leveraging enterprise knowledge graph to represent company correlation relationships, for mining the deep background knowledge of web events, with three self-supervised learning tasks. In particular, we devise two jointly self-supervised tasks to identify the relations between web events and companies. The first task is for generating ground-truth event-company correlation labels based on the enterprise knowledge graph. The second task is used to train how to identify the correlated companies of an event based on the generated correlation labels, with the encoding of web events, company features, and technical sequential data. We then design the prediction network to infer an event’s influence on stock price trends of the identified correlated companies based on the enterprise KG. Finally, we perform extensive experiments on a massive real-life dataset to validate the effectiveness of our proposed framework, and the experimental results demonstrate its superior performance in predicting stock market trends via considering events’ influences with the enterprise knowledge graph.

知识图谱（KG）技术在许多任务中取得了成功，特别是在语义网和自然语言处理领域。近年来，KG 上的表示学习已成功应用于电子商务应用，例如事件驱动的自动投资策略。然而，关于学习事件对现代量化投资KG的影响的研究仍然有限。在本文中，我们提出了一种新的事件影响学习框架来预测股市趋势，称为 ST-Trend，利用企业知识图来表示公司相关关系，用于挖掘网络事件的深层背景知识，具有三个自监督学习任务. 特别是，我们设计了两个联合自我监督的任务来识别网络事件和公司之间的关系。第一个任务是基于企业知识图生成真实事件-公司相关标签。第二个任务用于训练如何根据生成的相关标签识别事件的相关公司，并对网络事件、公司特征和技术序列数据进行编码。然后，我们设计预测网络，以基于企业 KG 推断事件对已识别关联公司股价趋势的影响。最后，我们在大量真实数据集上进行了广泛的实验，以验证我们提出的框架的有效性，实验结果证明了它在通过考虑事件对企业知识图谱的影响来预测股票市场趋势方面的优越性能。