STOCK MARKET PREDICTION USING NEURAL NETWORKS: A COMPREHENSIVE REVIEW AND APPLIED STUDY

Abstract

In today’s rapidly evolving financial landscape, the demand for accurate stock market predictions is more pressing than ever. This study explores the transformative potential of neural network models in forecasting market trends, offering a thorough comparison with traditional predictive techniques. By tracing the development of predictive methodologies, the paper investigates the unique capabilities of neural networks, emphasizing the importance of data preprocessing and model architecture in enhancing forecasting precision. Adopting a qualitative analysis framework, the research synthesizes findings from existing literature to demonstrate the superior adaptability and pattern recognition capabilities of neural networks in volatile market conditions. The analysis underscores the critical role of data quality, model complexity, and strategic relevance for investors navigating uncertainty. While neural networks show substantial promise, challenges such as data noise, real-world complexity, and model transparency remain. The study concludes with a call for continued innovation, interdisciplinary collaboration, and refined neural architectures. Key recommendations highlight the necessity of robust, diverse datasets and iterative model tuning to fully leverage the predictive power of neural networks in stock market forecasting. This work not only provides actionable insights for investors and analysts but also lays the groundwork for future research in this dynamic and evolving field.