AI-Driven Trading: Navigating the Complex Landscape of Market Manipulation

Abstract

Algorithmic trading has evolved beyond traditional methods by incorporating machine learning techniques to analyse extensive datasets. The integration of machine learning and ATS has helped in enhancing the decision-making process, leading to more accurate predictions of market trends, risk assessments, and optimal execution strategies. The opaque nature of artificial trading models can create challenges in understanding the decision-making process of these systems. This lack of clear understanding raises questions about accountability, and market participants lack transparency on whether movements are economic-driven or algorithmic trading strategies. The chapter explores the development of I-driven trading and Key Characteristics of Algorithmic Trading Systems. In conclusion, the integration of machine learning into capital markets represents a major shift in how investment decisions are made, risks are managed, and how markets operate independent artificial intelligence trading systems. Its increasing use highlights the need for careful ethical consideration, regulatory flexibility and ongoing monitoring.

Citation

Jain, S. (2024), "AI-Driven Trading: Navigating the Complex Landscape of Market Manipulation", Dadwal, S.S., Jahankhani, H. and Revett, K. (Ed.) Market Grooming, Emerald Publishing Limited, Leeds, pp. 37-58. https://doi.org/10.1108/978-1-83549-001-320241002