International Journal For Multidisciplinary Research

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Impact of AI-Driven Trading on Market Liquidity and Volatility: An Empirical Analysis of Modern Financial Markets

Author(s) Mr. Sohaib Mehmood, Dr. Aasim Mir
Country India
Abstract Financial markets across the world have undergone significant change over the past two decades, and majority of that has been pushed by computing power, fast data feeds, and the steady automation of order execution. Among the more recent developments, AI-driven trading systems have begun to alter how decisions are made on the buy and sell side. These systems read large volumes of market and non-market data, revise their own strategies on the fly, and place orders without interference of human. Using daily data on the NIFTY 50 index, the India VIX, and the policy repo rate from January 2015 to December 2024, the analysis used 2,475 trading-day observation. The testing strategy is indirect because exchanges in India do not publish any data of AI-driven order flow. The argument for the study is that if the market is being shaped by AI-style trading, then its observable behaviour should look like what the literature already documents for algorithmically intensive markets. The descriptive results are consistent with that view: daily log returns are sharply negative skewed (-1.4144) and heavy-tailed (kurtosis = 23.53), a far cry from the Gaussian benchmark. India VIX turns out to be a strong determinant of both the Amihud illiquidity measure (t = 6.83, p < 0.001) and short-run absolute returns (t = 8.90, p < 0.001). Estimated GARCH(1,1) parameters give α + β = 0.970 — close to a unit-root volatility process — and the GJR-GARCH leverage term γ = 0.150 (p = 0.0003) is highly significant, so negative shocks raise conditional variance much more than positive shocks of the same size. Splitting the sample into pre-COVID, COVID, and post-COVID windows shows that realized volatility during COVID was 2.05 times its pre-crisis level, while post-crisis liquidity (lower Amihud values) was about 46 percent better than the pre-COVID baseline. All three null hypotheses are rejected at the 1 percent level. Taken together, the NIFTY 50 displays the kind of liquidity, volatility, and stability behaviour the literature associates with markets shaped by AI-driven activity. The paper’s contribution is twofold: it shows that an indirect, outcome-based testing framework can be applied where direct trading-system data are not available, and it draws out specific implications for SEBI, the RBI, and large institutional participants in India.
Keywords AI-Driven Trading; Market Liquidity; Volatility Dynamics; GARCH Models; Indian Financial Markets; NIFTY 50; Market Microstructure
Field Sociology > Banking / Finance
Published In Volume 8, Issue 3, May-June 2026
Published On 2026-06-04
DOI https://doi.org/10.36948/ijfmr.2026.v08i03.80391
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