Herd behavior in digital asset markets: evidence from Fan Tokens

Purpose This study investigates herd behavior in the Fan Tokens market, comparing it with the non-fungible tokens (NFTs) and traditional cryptocurrency markets. Design/methodology/approach This study investigates herding behavior by examining the relationship between the cross-sectional dispersion of asset returns and overall market returns, utilizing five distinct model specifications. To enhance the robustness of the findings, the regressions are re-estimated using the GARCH model, ensuring more reliable parameter estimates and capturing the impact of volatility on herding behavior. Findings The analysis reveals strong evidence of herd behavior in the Fan Token market, particularly during bearish conditions, heightened volatility, and low trading volume. Positive news was found to amplify volatility more than negative news. In contrast, no statistically significant herd behavior was identified in the NFT and traditional cryptocurrency markets, where investors showed a more cautious response to market conditions. Practical implications Understanding the unique dynamics of Fan Tokens can help investors, regulators, and market participants make informed decisions and develop strategies to mitigate risks associated with herd behavior and volatility in this rapidly evolving market. Originality/value This study highlights the unique characteristics of Fan Tokens, emphasizing their strong ties to fan sentiment and sports outcomes, as well as the role of uninformed investors in shaping market dynamics. The findings contribute to the literature on digital asset markets and investor psychology, offering novel insights into this emerging asset class.

Authors

Publication Details

Journal
Review of Behavioral Finance
Published
2025-04-08
DOI
https://doi.org/10.1108/rbf-01-2025-0025
Citations
2
Primary Topic
Financial Markets and Investment Strategies
Type
article
Field-Weighted Citation Impact
4.42
Controls
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article

Herd behavior in digital asset markets: evidence from Fan Tokens

Athanasios Fassas, Ourania Dedousi
2 citations
Review of Behavioral Finance
Financial Markets and Investment Strategies
4.42
article

Herd behavior in digital asset markets: evidence from Fan Tokens

Athanasios Fassas, Ourania Dedousi
article en
2 citations

Abstract

Purpose This study investigates herd behavior in the Fan Tokens market, comparing it with the non-fungible tokens (NFTs) and traditional cryptocurrency markets. Design/methodology/approach This study investigates herding behavior by examining the relationship between the cross-sectional dispersion of asset returns and overall market returns, utilizing five distinct model specifications. To enhance the robustness of the findings, the regressions are re-estimated using the GARCH model, ensuring more reliable parameter estimates and capturing the impact of volatility on herding behavior. Findings The analysis reveals strong evidence of herd behavior in the Fan Token market, particularly during bearish conditions, heightened volatility, and low trading volume. Positive news was found to amplify volatility more than negative news. In contrast, no statistically significant herd behavior was identified in the NFT and traditional cryptocurrency markets, where investors showed a more cautious response to market conditions. Practical implications Understanding the unique dynamics of Fan Tokens can help investors, regulators, and market participants make informed decisions and develop strategies to mitigate risks associated with herd behavior and volatility in this rapidly evolving market. Originality/value This study highlights the unique characteristics of Fan Tokens, emphasizing their strong ties to fan sentiment and sports outcomes, as well as the role of uninformed investors in shaping market dynamics. The findings contribute to the literature on digital asset markets and investor psychology, offering novel insights into this emerging asset class.

Review of Behavioral FinanceVol. 17(3)
Openalex Percentile: Top 7%
Financial Markets and Investment Strategies
4.42
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