Technology-driven advancements: Mapping the landscape of algorithmic trading literature

Alexandra Horobet (), Sabri Boubaker (), Lucian Belascu (), Cristina Negreanu () and Zeno Dinca ()
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Alexandra Horobet: A.S.E. - The Bucharest University of Economic Studies / Academia de Studii Economice din Bucureşti
Sabri Boubaker: Métis Lab EM Normandie - EM Normandie - École de Management de Normandie = EM Normandie Business School, VNU - Vietnam National University [Hanoï], Swansea University
Lucian Belascu: ULBS - "Lucian Blaga" University/Universitatea „Lucian Blaga” din Sibiu
Cristina Negreanu: A.S.E. - The Bucharest University of Economic Studies / Academia de Studii Economice din Bucureşti
Zeno Dinca: A.S.E. - The Bucharest University of Economic Studies / Academia de Studii Economice din Bucureşti

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Abstract: Our study is a comprehensive examination of the existing literature pertaining to algorithmic trading and its temporal progression in a framework driven by technology development. A total of 4552 papers were analyzed, spanning the period from 1990 to 2023. Performance metrics evaluation and science mapping approaches were utilized in this study. The data was obtained from the Scopus database, and the analysis was conducted using the Biblioshiny environment. The research landscape has undergone significant changes in recent years due to advancements in data-driven technology and the implementation of sophisticated algorithms such as machine learning, deep learning, and genetic algorithms. The shift in research interest has been particularly pronounced in the last decade compared to earlier periods. The most significant contribution in terms of production is associated with authors who are affiliated with the People's Republic of China. Another significant discovery is the limited knowledge dissemination and collaboration among scholars, as seen by the examination of coauthorship in academic papers. In relation to the conceptual framework of the study domain, we have identified two primary trajectories, specifically financial markets, and energy markets, whereby the utilization of deep learning techniques has garnered significant attention.

Keywords: Bibliometric analysis; Algorithmic trading; Energy trading; Science mapping; Lotka's law (search for similar items in EconPapers)
Date: 2024-09-11
Note: View the original document on HAL open archive server: https://hal.science/hal-04990283v1
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Published in Technological Forecasting and Social Change, 2024, 209, pp.123746. ⟨10.1016/j.techfore.2024.123746⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-04990283

DOI: 10.1016/j.techfore.2024.123746

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