Abstract This study presents a comparative empirical analysis of algorithmic trading and its impact on equity market liquidity and volatility in India and the United States over the period 2013-2023. Algorithmic trading is the concept of using computer-driven programs to execute financial transactions at speeds that far exceed human capability. It has transformed global equity markets to an extraordinary extent, yet its impact on market quality indicators has been actively debated in the financial and economic literature. With the primary use of publicly available secondary data from sites like NSE India, CBOE and World Bank World Development Indicators, this study examines three research objectives using various techniques to gain its findings. The Key findings of this paper reveal that India’s market liquidity has improved by 73.8% over the decade that has been studied, rising to a turnover ratio 53.46% of GDP, while, in contrast, the US market has declined significantly by 30.8%. Both markets have displayed convergent average volatility despite significant structural differences, with COVID-19 a dominant volatility shock for both. A note to be made is that India shows superior post-COVID volatility resilience, recovering 5.4% below its pre-pandemic levels, while the US remained 39.4% above. Furthermore, the liquidity gap between the markets narrowed down by 50.1% from 166.64 to 83.07 percentage points over the decade. This study concluded that algorithmic trading’s major impacts on market quality are not uniform but are powerfully shaped by each market’s structural characteristics, regulatory frameworks, and the stage of adoption.
Christian Fernandes (Tue,) studied this question.