The Impact of Algorithmic Trading on Market Efficiency and Price Discovery: An Empirical Analysis of the Indian Stock Market

Narendra Kumar

The Impact of Algorithmic Trading on Market Efficiency and Price Discovery: An Empirical Analysis of the Indian Stock Market

Authors: Narendra Kumar

Published: 2025-05-02 11:13:09.599

Abstract

This paper investigates the impact of algorithmic trading (AT) on market efficiency and price discovery within the Indian stock market. Utilizing high-frequency data from the National Stock Exchange (NSE) over a period of five years, we employ econometric techniques, including event study methodology and vector autoregression (VAR) models, to analyze the effects of increased AT activity on various market microstructure characteristics. Our findings suggest that while AT can enhance liquidity and speed up price discovery, it also contributes to increased volatility and the potential for market manipulation. The results highlight the complex relationship between AT and market quality, providing valuable insights for policymakers and market participants seeking to optimize the benefits and mitigate the risks associated with this evolving trading paradigm. Further research is needed to explore the long-term implications of AT and develop effective regulatory frameworks to ensure fair and efficient market operations.

Keywords

Algorithmic Trading, Market Efficiency, Price Discovery, High-Frequency Trading, Indian Stock Market, Volatility, Liquidity, Market Microstructure, Econometrics, Time Series Analysis.

References

Angel, J. J., Harris, L. E., & Spatt, C. S. (2003). Equity trading in the 21st century: An overview. Journal of Financial Markets, 6(1), 1-29.
Brogaard, J. (2010). High-frequency trading and its impact on market quality. Review of Financial Studies, 23(4), 1456-1484.
Chakrabarty, B., Shkilko, A., & Yao, T. (2012). Algorithmic trading and price discovery. Financial Management, 41(3), 655-678.
Corradi, V., Distaso, W., & Fernandes, M. (2012). Estimating the instantaneous speed of adjustment. Journal of Econometrics, 169(2), 187-202.
Hasbrouck, J. (1995). One security, many markets: Determining the contributions to price discovery. The Journal of Finance, 50(4), 1175-1199.
Hasbrouck, J., & Saar, G. (2009). Low-latency trading. Journal of Financial Markets, 12(4), 672-705.
Kirilenko, A., Kyle, A. S., Samadi, M., & Tuzun, T. (2017). The flash crash: High-frequency trading in an electronic market. The Journal of Finance, 72(3), 967-1024.
Menkveld, A. J., & Zoican, M. A. (2017). Need for speed? How high-frequency trading impacts market quality. The Review of Financial Studies, 30(5), 1715-1747.
Stoica, O. A., Serea, D. C., & Militaru, G. (2016). Automated Trading Systems and Their Impact on the Romanian Capital Market. Management & Marketing Challenges for the Knowledge Society, 11(2), 368-384.
Aldridge, I. (2013). High-frequency trading: A practical guide to algorithmic strategies and trading systems. John Wiley & Sons.
Cartea, Á., Jaimungal, S., & Penalva, J. (2015). Algorithmic and high-frequency trading. Cambridge University Press.
Easley, D., de Prado, M. M., & O'Hara, M. (2011). The volume clock: Insights into the high-frequency trading. The Journal of Portfolio Management, 37(2), 64-79.
Hendershott, T., Jones, C. M., & Menkveld, A. J. (2011). Does algorithmic trading improve liquidity?. The Journal of Finance, 66(1), 1-33.
Madhavan, A. (2000). Market microstructure: A review. Financial Markets, Institutions & Instruments, 9(2), 1-65.
Zhang, X. (2010). High-frequency trading, stock volatility, and price discovery. SSRN Electronic Journal.

Download PDF

How to Cite

Narendra Kumar, (2025-05-02 11:13:09.599). The Impact of Algorithmic Trading on Market Efficiency and Price Discovery: An Empirical Analysis of the Indian Stock Market. JANOLI International Journal of Economics and Management Science , Volume MejgkunYNoDF1a6qqjpe, Issue 3.

ISSN: 3048-622X

JANOLI International Journal of
Economics and Management Science ( JIJEMS )