Predicting stock prices presents a challenging research problem due to the inherent volatility and non-linear nature of the stock market. In recent years, knowledge-enhanced stock price prediction methods have shown groundbreaking results by utilizing external knowledge to understand the stock market. Despite the importance of these methods, there is a scarcity of scholarly works that systematically synthesize previous studies from the perspective of external knowledge types. Specifically, the external knowledge can be modeled in different data structures, which we group into non-graph-based formats and graph-based formats: 1) non-graph-based knowledge captures contextual information and multimedia descriptions specifically associated with an individual stock; 2) graph-based knowledge captures interconnected and interdependent information in the stock market. This survey paper aims to provide a systematic and comprehensive description of methods for acquiring external knowledge from various unstructured data sources and then incorporating it into stock price prediction models. We also explore fusion methods for combining external knowledge with historical price features. Moreover, this paper includes a compilation of relevant datasets and delves into potential future research directions in this domain.

翻译：股票价格预测因股市固有的波动性和非线性特征而成为具有挑战性的研究问题。近年来，知识增强型股票价格预测方法通过利用外部知识理解股票市场，取得了突破性成果。尽管这些方法至关重要，但目前鲜有学术著作从外部知识类型的角度系统梳理既往研究。具体而言，外部知识可采用不同数据结构建模，我们将其分为非图基格式与图基格式两类：1）非图基知识捕捉与个股相关的上下文信息和多媒体描述；2）图基知识捕捉股票市场中的互联与相互依赖信息。本综述旨在系统全面地阐述从多种非结构化数据源获取外部知识并将其融入股票价格预测模型的方法。我们还探讨了将外部知识与历史价格特征相结合的融合方法。此外，本文整理了相关数据集，并深入探讨了该领域未来潜在的研究方向。