  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.
