AI in Financial Forecasting: Evaluating the Accuracy of Predictive Analytics in Stock Markets

Abstract

This research paper delves into the realm of financial forecasting with a specific focus on the utilization of artificial intelligence (AI) in predicting stock market trends. With the advent of advanced machine learning algorithms and data analytics techniques, AI has emerged as a powerful tool for financial analysts and investors seeking to gain insights into market movements. However, the accuracy and reliability of AI-driven predictive models remain subjects of scrutiny and debate. This study aims to evaluate the effectiveness of predictive analytics in stock markets by analyzing the performance of various AI algorithms in forecasting price fluctuations and identifying market trends. Through a comprehensive review of literature, empirical analysis, and comparative studies, this research seeks to provide a nuanced understanding of the strengths, limitations, and potential implications of AI in financial forecasting. By assessing the accuracy of AI models against historical data and benchmarking them against traditional forecasting methods, this paper aims to offer valuable insights for stakeholders in the finance industry, including investors, traders, and financial institutions. Ultimately, the findings of this study contribute to the ongoing discourse on the role of AI in shaping the future of financial markets and inform decision-making processes in the realm of investment and portfolio management.