Understanding the Impact of Algorithmic Trading on Indian Financial Markets: A Quantitative Analysis

This paper explores the transformative impact of algorithmic trading on the Indian financial markets, with a focus on market volatility, liquidity, and efficiency. The aim of the paper is to examine the impact of Algorithmic trading on Indian financial market and to assess the role of high-frequency trading in market liquidity and efficiency. In study design, used a mixed-methods approach, the study combines quantitative analysis of historical trading data from the Bombay Stock Exchange (BSE) and National Stock Exchange (NSE) with qualitative insights from regulatory filings and industry reports. The study employed the time series analysis, event studies and regression analysis techniques for analyzed the Market volatility (standard deviation of returns), liquidity (bid-ask spread), and trading volumes. The findings highlight significant benefits, including enhanced liquidity, tighter spreads, and improved execution speed. However, challenges persist, such as short-term volatility spikes and the risk of systemic disruptions during flash crashes. Regulatory interventions, like SEBI’s circuit breakers and AI surveillance systems, have mitigated some risks, but ongoing challenges in equitable infrastructure access remain. The study concludes with recommendations to balance technological innovation with market stability, advocating a hybrid approach that integrates algorithmic precision with human oversight to ensure efficiency and resilience in an increasingly automated trading ecosystem.