Forecasting Stock Market Trends: A Machine Learning and Game Theory Approach

Abstract

This research paper explores the synergistic application of Machine Learning (ML) & Game Theory in predicting stock market trends. In an increasingly volatile financial landscape, accurate prediction of market movements is crucial for investors, analysts, and policymakers alike. Traditional methods often fall short in capturing the complexity and dynamics of modern markets, prompting the integration of advanced computational techniques.

Machine Learning offers powerful tools for pattern recognition and predictive modeling, utilizing algorithms such as neural networks and support vector machines to analyze historical data and uncover intricate market patterns. Concurrently, Game Theory provides a strategic framework to model investor behavior and market interactions, enhancing the predictive accuracy by accounting for strategic decision-making among market participants. The combined approach leverages the strengths of both disciplines: ML processes vast datasets to discern trends and anomalies, while Game Theory refines predictions by simulating various strategic scenarios and their potential outcomes. By integrating these methodologies, this study aims to improve prediction precision, thereby assisting investors in making informed decisions and mitigating financial risks.

Through empirical validation and comparative analysis with traditional methods, this research demonstrates the efficacy of the ML-Game Theory synergy in forecasting stock market trends. Case studies and real-world applications further illustrate the practical implications of this approach. Ultimately, this paper contributes to advancing the field of financial forecasting, suggesting avenues for future research and innovation in predictive analytics.