Bibliometric Study of the Impact of Algorithmic Trading on Market Microstructure: Trends Patterns and Implications
Year: 2024
Type: article
Abstract: The study employs Bibliometric analysis to provide a comprehensive overview of the financial markets, how they operate and how they are impacted by algorithmic trading. Market Microstructure is a dynamic process and its process continues to evolve. The expansion of algorithmic trading over the past two decades is intricately linked to technological advancements and regulatory shifts in the structuring of securities markets. The study aims to map how the literature on this field has evolved over the years by identifying the leading sources of knowledge in terms of the most influential articles, Journals and authors. The study covers 553 articles published in 146 Journals with an annual growth rate of 7.25% during the period from 2001-2024 from the Scopus database. The Journal of Quantitative Finance is the most relevant source in terms of articles published whereas the Journal of Econometrics is the leading Journal in terms of H and G index, and citations received on this topic. Tim Bolleslerv is the leading author in terms of being active over the years (2001-2024), total citations (3617), total publications (16), H index (13) and G index (16). The most cited document based on the locally cited documents is “Realized Variance and Market Microstructure Noise”(2012) authored by Peter R Hansen and Asger lunde, which was published in the “Journal of Business & Economics Statistics”. USA is the most cited country whereas Market Microstructure (246) is the most used keyword followed by High-frequency Data (179) times. This research will help the practitioners, academicians and future researchers by giving them insightful information about the current available literature and future areas in which research can be conducted.
Authors: Karishma Yadav -, Indresh Pratap Singh -, Nishant Kumar -
Institution: University of Lucknow
Language: English
Subfield: Finance
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W4406622342