用图神经网络分析全球股市关联，揭示各国市场影响力差异。
Stock Market Telepathy: Graph Neural Networks Predicting the Secret Conversations between MINT and G7 Countries
- 构建股市关系图谱，用MTGNN捕捉多国市场时序依赖关系。
- 美国和加拿大对全球股市影响最大，印尼与土耳其在新兴市场中尤为关键。
- 相比传统方法，该模型显著提升对发达与新兴市场指数的预测精度。
新兴经济体，尤其是MINT国家（墨西哥、印度尼西亚、尼日利亚和土耳其），在全球股市中的影响力日益增强，但仍易受发达国家如G7（加拿大、法国、德国、意大利、日本、英国和美国）经济状况影响。这种金融市场的相互关联性与敏感性，使得准确理解各国间关系对投资者和政策制定者至关重要。为此，我们分析了2012至2024年间G7与MINT国家主要股票指数数据，采用一种新型图神经网络算法——多变量时间序列预测图神经网络（MTGNN）。该方法能有效建模多变量时间序列中的复杂时空关系。实验结果显示，美国和加拿大在预测过程中对股市指数影响最大，印尼和土耳其在MINT国家中表现最为突出。此外，MTGNN在预测G7与MINT国家股市指数方面均优于传统方法。研究为理解经济集团间市场动态提供了宝贵洞见，并展示了利用MTGNN进行全球股市联动分析的实证有效性。
原文摘要 · Abstract (English)
Emerging economies, particularly the MINT countries (Mexico, Indonesia, Nigeria, and Türkiye), are gaining influence in global stock markets, although they remain susceptible to the economic conditions of developed countries like the G7 (Canada, France, Germany, Italy, Japan, the United Kingdom, and the United States). This interconnectedness and sensitivity of financial markets make understanding these relationships crucial for investors and policymakers to predict stock price movements accurately. To this end, we examined the main stock market indices of G7 and MINT countries from 2012 to 2024, using a recent graph neural network (GNN) algorithm called multivariate time series forecasting with graph neural network (MTGNN). This method allows for considering complex spatio-temporal connections in multivariate time series. In the implementations, MTGNN revealed that the US and Canada are the most influential G7 countries regarding stock indices in the forecasting process, and Indonesia and Türkiye are the most influential MINT countries. Additionally, our results showed that MTGNN outperformed traditional methods in forecasting the prices of stock market indices for MINT and G7 countries. Consequently, the study offers valuable insights into economic blocks' markets and presents a compelling empirical approach to analyzing global stock market dynamics using MTGNN.
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