Adaptive market anomaly detection (AMAD): enhancing minimum spanning tree stability in financial networks

Article

Pallotta, A. and Ciciretti, V. 2025. Adaptive market anomaly detection (AMAD): enhancing minimum spanning tree stability in financial networks. Finance Research Letters. 85 (Part D). https://doi.org/10.1016/j.frl.2025.107997
TypeArticle
TitleAdaptive market anomaly detection (AMAD): enhancing minimum spanning tree stability in financial networks
AuthorsPallotta, A. and Ciciretti, V.
Abstract

This paper introduces the adaptive market anomaly detection (AMAD) transformation, which enhances minimum-spanning tree stability in financial networks by adaptively dampening extreme market movements while preserving essential return information. Empirical validation across multiple market regimes demonstrates that AMAD-preprocessed MSTs exhibit greater edge persistence, improved structural consistency, and superior risk-adjusted portfolio performance compared to MSTs constructed using raw returns.

KeywordsMinimum spanning trees; Financial networks; Portfolio optimization; Network stability; Market stress; Robust correlation; Graph theory; Risk management; Adaptive methods; Drawdown mitigation
Sustainable Development Goals8 Decent work and economic growth
9 Industry, innovation and infrastructure
Middlesex University ThemeCreativity, Culture & Enterprise
PublisherElsevier
JournalFinance Research Letters
ISSN1544-6123
Electronic1544-6131
Publication dates
Online08 Aug 2025
PrintNov 2025
Publication process dates
Submitted08 Apr 2025
Accepted18 Jul 2025
Deposited11 Sep 2025
Output statusPublished
Publisher's version
License
File Access Level
Open
Copyright Statement

© 2025 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license
(http://creativecommons.org/licenses/by/4.0/)

Digital Object Identifier (DOI)https://doi.org/10.1016/j.frl.2025.107997
Web of Science identifierWOS:001579695000003
Related Output
Is supplemented byhttps://www.sciencedirect.com/science/article/pii/S1544612325012553?via%3Dihub#appSC
LanguageEnglish
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