Retracted Article: Using bibliometrics to understand algorithmic finance

Abstract

This bibliometric analysis aims to comprehensively map and analyse the field of algorithmic finance research, examining 1,184 publications published between 2001 and 2023 after careful filtering. Utilising the Web of Science Core Collection, Biblioshiny R package, and VOSviewer, the research reveals a substantial 9.93% annual growth in publications, highlighting collaborative efforts and diverse document types. Temporal analysis reveals patterns in recent influential research, deepening insights into algorithmic finance. The study highlights influential authors and the United States’ dominant role in research output, emphasizing international collaborations. Keyword clusters, including “algorithmic trading,” high-frequency trading, and market microstructure, unveil central themes. The study looks into five groups, led by Levendovszky, Coffer, Jain, Kalev, and Moriyasu. It looks at improvements in prediction-based option trading, making algorithmic strategies more accessible, figuring out how ETF trading works, dealing with problems caused by market volatility, and figuring out how the electronic limit order market works.

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Stored only when both papers are in the corpus — 19 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.