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

Domaine:

socioeconomic

Type de record:

paper

Créateur:

Bur

Hôte:

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.

Visit

arxiv.org

Similaires

Predicting Australian stock market index using neural networks exploiting dynamical swings and intermarket influencesGraph Neural Networks for Time Series Forecasting on Ghana Stock ExchangePredicting Nigeria’s petroleum price: A comparison between recurrent neural networks, multilayer perceptron neural networks, and generalized regression neural networksOrbit-Equivariant Graph Neural NetworksPrediction of Tunisian stock market indexes by convolution deep neural networksArabic fake news detection using graph neural networks with heterogeneous graph

Predicting Australian stock market index using neural networks exploiting dynamical swings and intermarket influences

This paper presents a computational approach for predicting the Australian stock market index - AORD

Graph Neural Networks for Time Series Forecasting on Ghana Stock Exchange

Forecasting stock prices in emerging markets is challenging due to market inefficiencies, low liquid

Predicting Nigeria’s petroleum price: A comparison between recurrent neural networks, multilayer perceptron neural networks, and generalized regression neural networks

Predicting petroleum pump prices assists in knowing when, how, and what quality to purchase. The pur

Orbit-Equivariant Graph Neural Networks

Orbit-Equivariant Graph Neural Networks

Poster presented at the Deep Learning Indaba 2023 by Matthew Morris

Prediction of Tunisian stock market indexes by convolution deep neural networks

p. 52-56

Arabic fake news detection using graph neural networks with heterogeneous graph

Detecting and fighting fake news is vital in the digital age, preventing the dissemination of false