Comparative Analysis of Currency Exchange and Stock Markets in BRICS Using Machine Learning to Forecast Optimal Trends for Data-Driven Decision Making

The BRICS nations’ economies show that the countries are global financial powerhouses whose currency exchange rates and stock markets have influence globally. In this paper, the analysis of the forecast trends in both Currency Exchange and Stock Markets using a dual layered machine learning approach exposing models such as Long Short Term Memory (LSTM), Random Forest, Gradient Boosting and Support vector machines (SVM) is conducted. Their performance is tested twice, first on currency exchange and then on stock market data, to compare them on the basis of predictive power to deliver actionable insights. Each model is applied to currency and stock market data, separately, as the study mainly uses extensive historical datasets from BRICS economies. Benchmarking is done using metrics such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and R-squared values. For currency exchange, LSTM turned out to be the most effective model as it can handle a sequence of time series data. The best performance for stock market forecasting was achieved by Gradient Boosting, which is adept at finding complex nonlinear relationships. Random Forest proved to be consistent across both Datasets but SVM was found to be challenged on Scalability and Data Complexity, with relatively lower accuracy. The research goes on to repeat the comparative analysis for each of the different models, to illustrate the subtle differences between machine learning techniques in their capacity to effectively process financial datasets of all varieties. Predictive accuracy and reliability is further enhanced to reconcile conflicting trends between currency and stock markets by creating an ensemble model of all algorithms. These findings provide a robust framework for informed decision making for stakeholders to identify the more stable and hence more profitable market in the BRICS context. The results of this study add to the expansion of application of machine learning to global finance by demonstrating how tailored algorithms can offer significant economic planning and investment strategy plans.