COMPARATIVE ANALYSIS OF MACHINE LEARNING AND STATISTICAL APPROACHES FOR FINANCIAL MARKET FORECASTING: A MATHEMATICAL PERSPECTIVE

Abstract

Efficient financial market forecasting is crucial for informed decision-making. This study presents a comprehensive analysis that juxtaposes traditional statistical methods with modern machine learning techniques for forecasting in financial markets. The research evaluates empirical performance, interpretability, and adaptability across various financial datasets. Commencing with a thorough Literature Review, the study explores Time Series Models such as ARIMA, alongside contemporary approaches like Neural Networks and Gradient Boosting Machines. The Comparative Methodology encompasses data pre-processing and model implementation for both traditional and modern forecasting approaches. Results showcase accuracy metrics, resilience to market fluctuations, and inherent strengths of each method. Additionally, our findings shed light on the mathematical principles influencing outcomes, offering a valuable perspective from a mathematical standpoint. The practical implications extend to portfolio management, risk assessment, and the formulation of effective trading strategies. Moreover, the study deliberates on future directions, delving into emerging mathematical techniques and the potential of hybrid models. The Conclusion succinctly summarizes key contributions, emphasizing the significance of understanding mathematical foundations for successful forecasting. Bridging theory and practice, this research provides insights into the selection of appropriate methods, guiding real-world financial decisions. Furthermore, the discussion of the research results highlights the effectiveness of the Random Forest model in stock price forecasting, affirming its superiority over other approaches.