AI-Driven Predictive Analytics for Financial Market Forecasting

This study explores the ability of utilizing AI technology in predictive analysis of Monetary market's by employing machine learning algorithms, natural language processing and data analytics. Crook and Christopher, 2007 noted that because the nature of and interactions within financial markets are complex and nonlinear, they often pose a problem to mainstream financial theory. These problems can be address by reinforcement learning and deep learning AI technologies because these technologies are capable of processing large data sets to reveal critical details and trends. In this work, we compare the predictive accuracy of many statistical AI methods applied to the prediction of economic indexes, trends, and stock values. Moreover, we compare and differentiate such approaches as support vector machines and neural networks and decision trees. Furthermore, the work also establishes sentiment analysis for the inclusion of news sentiment and market sentiment in the prediction models. In additions, the results illustrate significant enhancements in the predictive reliability and thereby offers crucial information to investors, financial experts and policymakers.