FinTDA: Python package for estimating market change through persistent homology diagrams

Software Impacts, vol. 20, pp. 100637

Abstract

This paper presents a user-friendly version of Persistent Homology (PH) graph code to model financial market structures and changes. By leveraging Topological Data Analysis (TDA), the code offers an effective approach for analyzing high-dimensional stock data, enabling the identification of persistent topological features indicative of market changes. The code's potential applications in financial stability prediction, investment strategy development, and educational advancement are discussed. This contribution aims to facilitate the adoption of PH techniques in finance, promising significant implications for academic research and practical market analysis.

Authors 4

1. Hugo Gobato Souto corresponding

   HAN University of Applied Sciences

   Affiliation as printed

   International School of Business at HAN University of Applied Sciences, Ruitenberglaan 31, 6826 CC Arnhem, The Netherlands

2. Ismail Baris corresponding

   Deutsches Zentrum für Luft- und Raumfahrt e. V. (DLR)

   Affiliation as printed

   DLR Microwaves and Radar Institute at German Aerospace Center (DLR), Oberpfaffenhofen 1, 82234 Weßling, Germany

3. Storm Koert Heuvel corresponding Aachen

   Leiden University

   Affiliation as printed

   Leiden Institute of Advanced Computer Science at Leiden University, Niels Bohrweg 1, 2333 CA Leiden, The Netherlands

4. Amir Moradi corresponding

   HAN University of Applied Sciences

   Affiliation as printed

   International School of Business at HAN University of Applied Sciences, Ruitenberglaan 31, 6826 CC Arnhem, The Netherlands