Sentiment Analysis and Market Forecasting for BRICS Policies Using Machine Learning and LLM Model

EasyChair Preprint 15964

Abstract

The global financial market is highly sensitive to economic and political policy shifts, particularly those enacted by BRICS (Brazil, Russia, India, China, and South Africa). As an influential economic alliance, BRICS policies have the potential to impact global stock indices and key industry sectors. However, there is currently no systematic method for evaluating these effects in real-time. This research aims to analyze the impact of BRICS policies on global stock markets by integrating Sentiment Analysis and Time Series Forecasting. By leveraging Large Language Model (LLM) and machine learning models, this study will connect investor sentiment regarding BRICS policies to stock market movements. Additionally, it will identify the most affected sectors and provide data-driven insights for investors and policymakers. By employing an AI-based approach, this research seeks to enhance the understanding of the relationship between BRICS policies and global financial market dynamics.

Keyphrases: BRICS, LLM Model, Sentiment Analysis, deep learning, machine learning

@booklet{EasyChair:15964,
  author    = {Liao Xiuya and Rizki Syaputra and Mohd Shahizan Bin Othman},
  title     = {Sentiment Analysis and Market Forecasting for BRICS Policies Using Machine Learning and LLM Model},
  howpublished = {EasyChair Preprint 15964},
  year      = {EasyChair, 2025}}