Stock Market Telepathy: Graph Neural Networks Predicting the Secret Conversations between MINT and G7 Countries

TL;DR

This paper employs graph neural networks to analyze and predict stock market interactions between MINT and G7 countries, revealing influential nations and outperforming traditional forecasting methods.

Contribution

It introduces the application of MTGNN to model complex spatio-temporal stock market relationships between MINT and G7 countries, providing new insights into their interconnectedness.

Findings

US and Canada are most influential G7 countries in stock indices.

Indonesia and T"urkiye are the most influential MINT countries.

MTGNN outperforms traditional forecasting methods.

Abstract

Emerging economies, particularly the MINT countries (Mexico, Indonesia, Nigeria, and T\"urkiye), 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 T\"urkiye 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.