Data-Driven insights on the relationship between BRICS financial policies and global investment trends

Abstract

This study investigates the dynamic relationship between the financial policies of BRICS nations—Brazil, Russia, India, China, and South Africa—and global investment trends. As emerging markets like the BRICS play a crucial role in the global economic growth, it is critical to understand how changing in the financial policies in these markets interact with international investment flows for both investors and policymakers. The study leverages data of economic indicators, policy measures, and global investment patterns by building regression, decision trees and deep learning models based on advanced machine learning techniques, including regression models, decision trees, deep learning methods such as Long Short-Term Memory networks and Transformers. According to the findings, there are strong correlations between fiscal, monetary and trade policies in the BRICS economies and agent behavior in the global capital market. Uncovering these patterns therefore provides actionable insights for investors to navigate the changing finance terrain of those countries better and advice for policymakers on the way to fashion policies that would attract investment. This research supports the use of data driven technique to capture the intricate economic relationship and investment prediction outcomes in the case of BRICS financial systems.