Abstract
The rapid advancement of Artificial Intelligence (AI) has significantly transformed the global stock market landscape, reshaping traditional trading and investment methods. This paper examines the profound impact of AI-driven algorithmic trading and high-frequency trading (HFT) systems, highlighting how machine learning and advanced analytics enhance the speed, efficiency, and volume of stock transactions. By integrating predictive analytics, traders and financial institutions increasingly rely on AI-powered neural networks and deep learning models to forecast stock prices, analyse market trends, and make real-time trading decisions. Compared to traditional statistical models, AI models process vast, complex datasets more effectively, uncovering patterns that human traders often overlook. However, the adoption of AI in trading is not without challenges, including risks of overfitting, data biases, and unpredictable market reactions that can result in rapid, large-scale fluctuations. This study demonstrates how AI-driven models leveraged forecast of future trade patterns with increased precision for the next five years. It explores real-life cases of hedge funds and firms like Renaissance Technologies and Citadel. Regulatory bodies face new challenges in monitoring the lightning-fast trades executed by AI, raising concerns about market integrity, systemic risks, and the potential for AI-induced flash crashes. To address these challenges, this paper discusses current regulatory frameworks and highlights the need for updated policies that balance technological innovation with transparency and fairness. The study argues that while AI has revolutionized trading by increasing accuracy and profitability, responsible governance, explainability, and ethical deployment are vital to prevent misuse and ensure financial market stability. By analyzing both the opportunities and risks, this research contributes to the ongoing global dialogue about integrating AI into the financial system that benefits investors, institutions, and markets.