Predictive Analysis of Forex Market Trends Using Machine Learning Algorithms
Abstract
The foreign exchange market is inherently dynamic and highly volatile, posing persistent challenges for traders seeking consistent profitability. Conventional forecasting approaches often prove inadequate in capturing the complex, nonlinear, and time-dependent characteristics of currency price movements. In response to these limitations, this study systematically examines the application of machine learning (ML) algorithms for the predictive analysis of forex market trends. Linear Regression, Random Forest, Support Vector Machines , and Long Short-Term Memory networks are employed to predict price movements for the EUR/USD, GBP/USD, and USD/NGN currency pairs. Historical data covering the period 2015–2025 are preprocessed and evaluated using root mean squared error , mean absolute error , directional accuracy (DA), and simulated trading profitability. The results empirically demonstrate that the LSTM model outperforms the other approaches in capturing temporal dependencies inherent in forex time-series data, while the Random Forest model serves as a reliable and robust baseline for comparative evaluation. The proposed AI-based trading decision support system is conceptually illustrated through structured flowcharts and use-case representations to enhance clarity and practical relevance. Overall, the study underscores the effectiveness of AI-driven methodologies in supporting informed forex trading decisions and establishes a strong foundation for future research in intelligent, data-driven currency market analysis.
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