Status and Trends of Quantitative Trading Research——Based on the Analysis of Web of Science Literature
Year: 2026
Type: conference-paper
Abstract: The advancement of big data, cloud computing, and artificial intelligence technologies has driven unprecedented expansion of quantitative trading in global capital markets. Characterized by high automation, data-driven decision-making, and intensive technical application, it has become a pivotal direction for financial trading innovation. However, existing research suffers from overlapping concepts, fragmented domains, and complex interdisciplinary integration, with insufficient systematic collation of technical application practices. Most review studies focus on single-dimensional analysis, lacking a comprehensive summary of quantitative trading technology usage and evolutionary context. This study employs CiteSpace to conduct a scientometric analysis of 12,303 literatures retrieved from the Web of Science Core Collection spanning 2006—2025, including quantitative description, clustering analysis, and keyword co-occurrence network analysis. It supplements in-depth analysis of core technical applications such as high-frequency data processing, algorithmic strategy implementation, and risk control technology. The results indicate distinct phased characteristics of research in this field: the number of publications surged significantly after 2020; keyword analysis identifies four core thematic clusters and key technical application directions; the research trend has shifted from traditional quantitative frameworks to in-depth integration with intelligent technologies. This study systematically maps the knowledge landscape, technical application status, evolutionary paths, and future trends of quantitative trading research, providing valuable references for academic exploration and practical applications.
Authors: Yu Ren, Changxiao Li
Language: English
Subfield: Finance
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W7204194175