Machine Learning Solutions for Predicting Stock Trends in BRICS amid Global Economic Shifts and Decoding Market Dynamics

Abstract

In this paper we examine the role of machine learning (ML) in predicting stock market trends within BRICS economies. Complex, interdependent global and regional economic factors are today and will in the future increasingly influence stock markets, which necessitates innovative techniques for trend analysis. Using this state of the art ML models Support Vector Machines (SVMs), Random Forests, and neural networks the study predicts market fluctuations based on historical stock data, economic indicators and geopolitical events. This research emphasizes the increasing role of deep learning, especially with models such as Transformers and LSTMs, to meet the demand for high accuracy predictive systems in the volatile market. Its analysis brings model performance into comparison of BRICS nations, taking into account the peculiar financial and economic behavior peculiar to each of them. These results illuminate how ML can provide actionable intelligence for investors and policymakers to better manage risk and better make strategic investments. Findings from the study underscore the requirement for adopting sophisticated data driven tools in order to negotiate the intricacies of globalized financial systems. This study also explains the basis in helping us understand how machine learning changes the perspective on stock market analysis. It is a great source to understand how different ML techniques such as Support Vector Machines which are good at classification problems and Random Forests, which are known to handle over fitting, can be used on a financial dataset. It shows cutting edge tools for market prediction such as deep learning models like LSTMs, which are able to handle sequential time series data, or Transformers that further improve the traditional architectures with attention mechanisms. The paper also discusses data preprocessing methods, such as feature engineering and normalization, and the importance of their inclusion in improving model performance. This research shows the value of ML literacy and provides future financial analysts and decision makers with tools for addressing market volatility in a data driven and strategic context.