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The Journal of Finance and Data Science

The Journal of Finance and Data Science (JFDS) is the leading analytical journal on finance and data science, providing detailed analyses of theoretical and empirical foundations and their applications in financial economics.

JFDS publishes evaluations of both well-established and new theories using financial data, data-scientific measurements of variables relevant to financial decision-making and financial service, the econometrics of financial market data, and the development of new econometric methodologies with financial applications. JFDS also provides readers with results-oriented computer hardware and software treatments and enhanced treatment of data information for practical finance products and techniques. Big data financial economic analytics has led to new challenges and advanced state-of-the-art computer science techniques; in return, computer data science has provided indispensable techniques for financial analysis. JFDS includes articles on

  • Machine-learning, high-frequency trading for algorithm trading in finance
  • Theoretical and empirical financial results from the perspective of data science
  • Current practical treatments for data science in financial economics
  • Innovative designs and techniques for computer hardware and software for finance
  • Applications of theoretical results for real-world problems related to data science
  • Illustrations and rigorous analyses of essential innovations in data science for financial economics
  • New financial products modeled by data science
  • Case studies on the industry of financial services, financial economics related to data science, and traditional financial research areas

View full aims and scope

Editor in Chief: Jingzhi (Jay) Huang
View full editorial board

Journal Metrics
CiteScore: 5.8
2020: 5.8
CiteScore measures the average citations received per peer-reviewed document published in this title. CiteScore values are based on citation counts in a range of four years (e.g. 2017-2020) to peer-reviewed documents (articles, reviews, conference papers, data papers and book chapters) published in the same four calendar years, divided by the number of these documents in these same four years (e.g. 2017–2020).
Source Normalized Impact per Paper (SNIP): 2.362
Source Normalized Impact per Paper (SNIP):
2019: 2.362
SNIP measures contextual citation impact by weighting citations based on the total number of citations in a subject field.
Imprint: KeAi
ISSN: 2405-9188
The most downloaded articles from The Journal of Finance and Data Science in the last 90 days.
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Zhijian (James) Huang | Li Xu
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