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.

预测股票价格是一个具有挑战性的研究问题，由于股市的固有波动性和非线性特性。最近，知识增强的股票价格预测方法通过利用外部知识来理解股市，取得了突破性的成果。尽管这些方法的重要性，但从外部知识类型的角度系统地综合以前的研究工作还是相对稀缺。具体而言，外部知识可以建模为不同的数据结构，我们将其分为非图形化格式和图形化格式：1）非图形化知识捕获与个别股票具体相关的上下文信息和多媒体描述；2）图形化知识捕获股票市场中相互关联和相互依赖的信息。本综述旨在系统全面地描述从各种非结构化数据源中获取外部知识并将其纳入股票价格预测模型的方法。我们还探索了将外部知识与历史价格特征相结合的融合方法。此外，该论文编制了相关数据集的汇编，并深入探讨了该领域的潜在未来研究方向。

股票价格预测中知识获取和整合的方法：一项调查

Methods for Acquiring and Incorporating Knowledge into Stock Price  Prediction: A Survey

The recent work of Clark et al. introduces the AI2 Reasoning Challenge (ARC)
and the associated ARC dataset that partitions open domain, complex science
questions into an Easy Set and a Challenge Set. That paper includes an analysis
of 100 questions with respect to the types of knowledge and reasoning required
to answer them; however, it does not include clear definitions of these types,
nor does it offer information about the quality of the labels. We propose a
comprehensive set of definitions of knowledge and reasoning types necessary for
answering the questions in the ARC dataset. Using ten annotators and a
sophisticated annotation interface, we analyze the distribution of labels
across the Challenge Set and statistics related to them. Additionally, we
demonstrate that although naive information retrieval methods return sentences
that are irrelevant to answering the query, sufficient supporting text is often
present in the (ARC) corpus. Evaluating with human-selected relevant sentences
improves the performance of a neural machine comprehension model by 42 points.

本文提出了一套完整的科学问题解答所需的知识和推理类型定义方法，回答 ARC 数据集中的问题需要的标注信息分布和相关统计的分析，证明了人工选取的相关句子能够提高神经机器理解模型的 42 个百分点的性能。