Herding unmasked: Insights into cryptocurrencies, stocks and US ETFs.

Herding behavior has become a familiar phenomenon to investors, with potential dangers of both undervaluing and overvaluing assets, while also threatening market stability. This study contributes to the literature on herding behavior by using a recent dataset, covering the most impactful events of r...

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Main Authors: An Pham Ngoc Nguyen, Martin Crane, Thomas Conlon, Marija Bezbradica
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2025-01-01
Series:PLoS ONE
Online Access:https://doi.org/10.1371/journal.pone.0316332
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author An Pham Ngoc Nguyen
Martin Crane
Thomas Conlon
Marija Bezbradica
author_facet An Pham Ngoc Nguyen
Martin Crane
Thomas Conlon
Marija Bezbradica
author_sort An Pham Ngoc Nguyen
collection DOAJ
description Herding behavior has become a familiar phenomenon to investors, with potential dangers of both undervaluing and overvaluing assets, while also threatening market stability. This study contributes to the literature on herding behavior by using a recent dataset, covering the most impactful events of recent years. To our knowledge, this is the first study examining herding behavior across three different types of investment vehicle and also the first study observing herding at a community (subset) level. Specifically, we first explore this phenomenon in each separate type of investment vehicle, namely stocks, US ETFs and cryptocurrencies, using the Cross-Sectional Absolute Deviation model. We find mostly similar herding patterns for stocks and US ETFs. Subsequently, the same experiment is implemented on a combination of all three investment vehicles. For a deeper investigation, we adopt graph-based techniques including the Minimum Spanning Tree and Louvain community detection to partition the combination into smaller subsets to detect herding behavior for each subset. We find that herding behavior exists at all times across all types of investment vehicle at a subset level, although perhaps not at the superset level, and that this herding behavior tends to stem from specific events that solely impact that subset of assets. Lastly, we explore herding by examining the financial contagion effects between these types of investment vehicle. Results show that US ETFs not only have a tendency to propagate similar trading behaviors in stocks and especially cryptocurrencies but also show self-reinforcing herding behavior, acting as drivers of their own trends.
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publishDate 2025-01-01
publisher Public Library of Science (PLoS)
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spelling doaj-art-54492a0ae262412ca345aea914bf86e22025-02-09T05:30:41ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01202e031633210.1371/journal.pone.0316332Herding unmasked: Insights into cryptocurrencies, stocks and US ETFs.An Pham Ngoc NguyenMartin CraneThomas ConlonMarija BezbradicaHerding behavior has become a familiar phenomenon to investors, with potential dangers of both undervaluing and overvaluing assets, while also threatening market stability. This study contributes to the literature on herding behavior by using a recent dataset, covering the most impactful events of recent years. To our knowledge, this is the first study examining herding behavior across three different types of investment vehicle and also the first study observing herding at a community (subset) level. Specifically, we first explore this phenomenon in each separate type of investment vehicle, namely stocks, US ETFs and cryptocurrencies, using the Cross-Sectional Absolute Deviation model. We find mostly similar herding patterns for stocks and US ETFs. Subsequently, the same experiment is implemented on a combination of all three investment vehicles. For a deeper investigation, we adopt graph-based techniques including the Minimum Spanning Tree and Louvain community detection to partition the combination into smaller subsets to detect herding behavior for each subset. We find that herding behavior exists at all times across all types of investment vehicle at a subset level, although perhaps not at the superset level, and that this herding behavior tends to stem from specific events that solely impact that subset of assets. Lastly, we explore herding by examining the financial contagion effects between these types of investment vehicle. Results show that US ETFs not only have a tendency to propagate similar trading behaviors in stocks and especially cryptocurrencies but also show self-reinforcing herding behavior, acting as drivers of their own trends.https://doi.org/10.1371/journal.pone.0316332
spellingShingle An Pham Ngoc Nguyen
Martin Crane
Thomas Conlon
Marija Bezbradica
Herding unmasked: Insights into cryptocurrencies, stocks and US ETFs.
PLoS ONE
title Herding unmasked: Insights into cryptocurrencies, stocks and US ETFs.
title_full Herding unmasked: Insights into cryptocurrencies, stocks and US ETFs.
title_fullStr Herding unmasked: Insights into cryptocurrencies, stocks and US ETFs.
title_full_unstemmed Herding unmasked: Insights into cryptocurrencies, stocks and US ETFs.
title_short Herding unmasked: Insights into cryptocurrencies, stocks and US ETFs.
title_sort herding unmasked insights into cryptocurrencies stocks and us etfs
url https://doi.org/10.1371/journal.pone.0316332
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AT martincrane herdingunmaskedinsightsintocryptocurrenciesstocksandusetfs
AT thomasconlon herdingunmaskedinsightsintocryptocurrenciesstocksandusetfs
AT marijabezbradica herdingunmaskedinsightsintocryptocurrenciesstocksandusetfs