  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.
