Comprehensive Review on Artificial Intelligence Techniques for Financial Forecasting and Their Applications in Stock Market Analysis

Abstract

The methodology involves a systematic review of scholarly literature, concentrating on peer-reviewed studies that discuss the efficacy, obstacles, and future directions of AI in stock market forecasting. Results indicate that AI holds significant promise for improving market efficiency and enhancing the understanding of price volatility. Nonetheless, issues such as data integrity, transparency of AI models, and the demand for comprehensive regulatory oversight remain critical concerns. The conclusions emphasize AI’s transformative capacity to process large-scale datasets and forecast market behavior with greater precision. At the same time, the research acknowledges current AI limitations and advocates for a hybrid approach that integrates AI with traditional forecasting techniques and ongoing algorithmic improvements. Recommendations stress the importance of interdisciplinary collaboration among AI developers, ethical scholars, and financial professionals to create AI systems that are transparent, ethically responsible, and operationally effective. Overall, this paper provides an extensive overview of AI’s impact on financial forecasting, offering valuable insights for future research. It highlights both the substantial opportunities and complex challenges AI introduces to stock market analysis, marking a significant step toward more data-driven decision-making in finance.