Financial technology (FinTech) is a field that uses artificial intelligence to automate financial services. One area of FinTech is stock analysis, which aims to predict future stock prices in order to develop investment strategies that maximize profits. Traditional methods of stock market prediction, such as time series analysis and machine learning, struggle to handle the non-linear, chaotic, and sudden changes in stock data and may not consider the interdependence between stocks. Recently, graph neural networks (GNNs) have been used in stock market forecasting to improve prediction accuracy by incorporating the interconnectedness of the market. GNNs can process non-Euclidean data in the form of a knowledge graph. However, financial knowledge graphs can have dynamic and complex interactions, which can be challenging for graph modeling technologies. This work presents a systematic review of graph based approaches for stock market forecasting. This review covers different types of stock analysis tasks (classification, regression, and stock recommendation), a generalized framework for solving these tasks, and a review of various features, datasets, graph models, and evaluation metrics used in the stock market. The results of various studies are analyzed, and future directions for research are highlighted.

中文翻译：

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基于图神经网络的股票市场预测方法的系统评价


金融技术 （FinTech） 是一个使用人工智能实现金融服务自动化的领域。金融科技的一个领域是股票分析，旨在预测未来的股票价格，以制定实现利润最大化的投资策略。传统的股票市场预测方法，例如时间序列分析和机器学习，难以处理股票数据的非线性、混乱和突然变化，并且可能没有考虑股票之间的相互依赖关系。最近，图形神经网络 （GNN） 已用于股票市场预测，通过整合市场的互连性来提高预测准确性。GNN 可以以知识图谱的形式处理非欧几里得数据。但是，金融知识图谱可能具有动态和复杂的交互，这对于图建模技术来说可能具有挑战性。这项工作对基于图的股票市场预测方法进行了系统综述。这篇综述涵盖了不同类型的股票分析任务（分类、回归和股票推荐），解决这些任务的通用框架，以及对股票市场中使用的各种特征、数据集、图形模型和评估指标的回顾。分析了各种研究的结果，并强调了未来的研究方向。