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Text mining arXiv: a look through quantitative finance papers

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  • Michele Leonardo Bianchi

Abstract

This paper explores articles hosted on the arXiv preprint server with the aim to uncover valuable insights hidden in this vast collection of research. Employing text mining techniques and through the application of natural language processing methods, we examine the contents of quantitative finance papers posted in arXiv from 1997 to 2022. We extract and analyze crucial information from the entire documents, including the references, to understand the topics trends over time and to find out the most cited researchers and journals on this domain. Additionally, we compare numerous algorithms to perform topic modeling, including state-of-the-art approaches.

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  • Michele Leonardo Bianchi, 2024. "Text mining arXiv: a look through quantitative finance papers," Papers 2401.01751, arXiv.org, revised Apr 2024.
    • Handle: RePEc:arx:papers:2401.01751
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    References listed on IDEAS

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    1. Markus Vogl, 2022. "Quantitative modelling frontiers: a literature review on the evolution in financial and risk modelling after the financial crisis (2008–2019)," SN Business & Economics, Springer, vol. 2(12), pages 1-69, December.
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