Algorithmic Trading and AI: A Review of Strategies and Market Impact

Abstract

This review explores the dynamic intersection of algorithmic trading and artificial intelligence (AI) within financial markets. It delves into the evolution, strategies, and broader market impact of algorithmic trading fueled by AI technologies. Examining the symbiotic relationship between advanced algorithms and AI, the review navigates through the various strategies employed, shedding light on their implications for market efficiency, liquidity, and overall stability. From high-frequency trading to machine learning-driven predictive analytics, this review unveils the multifaceted landscape of algorithmic trading in the era of AI, presenting both opportunities and challenges for financial markets. The review begins by tracing the historical development of algorithmic trading, emphasizing the paradigm shift with the integration of AI. From traditional programmatic trading to the emergence of sophisticated algorithms driven by machine learning and deep learning, the evolution sets the stage for a comprehensive understanding of the subject. An in-depth analysis of diverse algorithmic trading strategies unfolds, covering areas such as trend following, statistical arbitrage, market making, and sentiment analysis. The incorporation of AI introduces adaptive learning capabilities, enabling algorithms to evolve and optimize strategies based on real-time market conditions. Exploring the impact of algorithmic trading on financial markets, the review examines how AI-driven strategies contribute to market efficiency, liquidity provision, and price discovery. It dissects the implications for traditional market structures, regulatory considerations, and the potential risks associated with algorithmic dominance. Acknowledging the transformative power of algorithmic trading with AI, the review critically assesses the challenges and ethical considerations. From algorithmic bias to systemic risks, the review delves into the darker corners of this technological advancement, prompting a reflection on the need for responsible and transparent practices. The review concludes by peering into the future trajectory of algorithmic trading fueled by AI. Anticipated innovations, regulatory responses, and the evolving landscape of financial markets are discussed, offering insights into the ongoing transformation and potential disruptions in the realm of algorithmic trading. In essence, this review provides a nuanced perspective on the intricate relationship between algorithmic trading and AI, offering a comprehensive understanding of their strategies and the transformative impact on financial markets.