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

Open access

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

Open access

Samenvatting

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.

OrganisatieHAN University of Applied Sciences
AfdelingAcademie International School of Business
Lectoraten
LectoraatInternational Business
Gepubliceerd inSoftware Impacts, 100637
Datum2024-03-28
TypeArtikel
DOI10.1016/j.simpa.2024.100637
TaalEngels

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