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Text Mining arXiv: A Look Through Quantitative Finance Papers

Author

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  • Michele Leonardo Bianchi

    (Financial Stability Directorate, Banca d’Italia, 00184 Rome, Italy)

Abstract

This paper explores articles hosted on the arXiv preprint server with the aim of uncovering valuable insights hidden in this vast collection of research. Employing text mining techniques and through the application of natural language processing methods, I xamine the contents of quantitative finance papers posted in arXiv from 1997 to 2022. I extract and analyze, without relying on ad hoc software or proprietary databases, crucial information from the entire documents, including the references, to understand the topic trends over time and to find out the most cited researchers and journals in this domain. Additionally, I compare numerous algorithms for performing topic modeling, including state-of-the-art approaches.

Suggested Citation

  • Michele Leonardo Bianchi, 2025. "Text Mining arXiv: A Look Through Quantitative Finance Papers," Mathematics, MDPI, vol. 13(9), pages 1-20, April.
  • Handle: RePEc:gam:jmathe:v:13:y:2025:i:9:p:1375-:d:1640514
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    References listed on IDEAS

    as
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    2. Jialiang Lin & Yao Yu & Yu Zhou & Zhiyang Zhou & Xiaodong Shi, 2020. "How many preprints have actually been printed and why: a case study of computer science preprints on arXiv," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(1), pages 555-574, July.
    3. Janine Huisman & Jeroen Smits, 2017. "Duration and quality of the peer review process: the author’s perspective," Scientometrics, Springer;Akadémiai Kiadó, vol. 113(1), pages 633-650, October.
    4. Nees Jan Eck & Ludo Waltman, 2010. "Software survey: VOSviewer, a computer program for bibliometric mapping," Scientometrics, Springer;Akadémiai Kiadó, vol. 84(2), pages 523-538, August.
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