V3I5P122

Study on AI-Driven Trading Algorithms and Their Effect on Market Efficiency 

Neha Sharma^1*, Abhay Kumar Tiwari²

Abstract

Artificial Intelligence (AI)-driven trading algorithms have transformed modern financial markets by enabling high-speed trade execution, advanced data processing, and automated decision-making. This study examines the impact of AI-driven trading algorithms on market efficiency, focusing on liquidity, price discovery, volatility, and market stability. The research adopts a descriptive and analytical approach using both secondary data and perception-based primary data collected through a structured questionnaire. The findings indicate that AI-driven trading enhances market liquidity, improves informational efficiency, and accelerates price discovery processes. However, the study also reveals concerns regarding increased short-term volatility, flash events, unequal technological access, and systemic risk. The research concludes that while AI trading significantly contributes to efficient market functioning, stronger regulatory oversight, transparent AI systems, and balanced governance mechanisms are necessary to ensure financial stability and fair market participation.

Keywords:

Artificial Intelligence, Algorithmic Trading, High-Frequency Trading, Market Efficiency, Liquidity, Volatility, Price Discovery