The Impact of AI-Driven Algorithmic Trading on Market Efficiency and Volatility: Evidence from Global Financial Markets

• Author(s): Arunkumar Yadava
• Paper ID: 1708116
• Page: 1092-1101
• Published Date: 31-03-2025
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 8 Issue 6 December-2024

Abstract

Scientific research is currently examining the penetration of AI-driven algorithmic trading into financial markets with a view to their effect on efficiency and volatility. In fact, they have empirical evidence from different markets in the globe. The advent of AI technology completely alters the modern financial structure and functioning in a record pace. AI-powered algorithms can process a huge number of real-time data and execute trades within a few milliseconds and have now entirely become the major core trade operations in the major exchanges all over the world. For this reason, the study investigates to what extent such innovations may enhance the efficiency of the markets—distinguished by improvements in price discovery, reduced bid-ask spreads, or greater liquidity or cause greater market volatility cause, particularly in the periods of economic uncertainty or stress, based on the existence of the potential uncertainties in economic regions. The study employs a comprehensive quantitative methodology that includes both time-series analysis and regression modeling, drawing upon data sourced from leading global stock exchanges, such as the NYSE, NASDAQ, and LSE. Important variables include, but are not limited to indicators of market efficiency and a range of measures of volatility. The results show that AI-driven algorithmic trading appears to support market efficiency because it reduces the time taken for assimilating information while presenting price mechanisms that are more accurate. However, it also augments the noise-induced fluctuations of short-term market activity while eliciting abrupt surges of volatility under some conditions.

Keywords

Artificial Intelligence (AI); Algorithmic Trading; Financial Markets; Market Efficiency; Market Stability; Efficient Market Hypothesis (EMH); Adaptive Market Hypothesis (AMH); Big Data Analytics; Risk Management; Stock Market Volatility; Sentiment Analysis; High Trading Frequency (HFT)

Citations

IRE Journals:
Arunkumar Yadava "The Impact of AI-Driven Algorithmic Trading on Market Efficiency and Volatility: Evidence from Global Financial Markets" Iconic Research And Engineering Journals Volume 8 Issue 6 2024 Page 1092-1101

IEEE:
Arunkumar Yadava "The Impact of AI-Driven Algorithmic Trading on Market Efficiency and Volatility: Evidence from Global Financial Markets" Iconic Research And Engineering Journals, 8(6)