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Herding, information cascades, and cryptocurrencies — New evidence using low frequency and high frequency data

Author

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  • Natashekara, Karthik
  • Sampath, Aravind

Abstract

We revisit herding in cryptocurrencies using a longitudinal dataset that spans both low-frequency data and high-frequency intraday data, covering significant global events, and document the presence of herding, especially during the 2022–23 Crypto-winter. Using robust analysis, we first find evidence of both individual-level and market-wide herding. Herding under different volatility regimes reveals that herds are more pronounced during periods of low volatility, and herding grouped by idiosyncratic volatilities reiterate these results while suggesting weak herding in high volatility group. We subsequently use microstructure data, estimate various informed trading models to explain herding and uncover evidence of informational cascades. Lastly, we demonstrate that the actions of informed traders can either cause or curtail herding, showing that they serve as both orchestrators and stabilizers of herding.

Suggested Citation

  • Natashekara, Karthik & Sampath, Aravind, 2026. "Herding, information cascades, and cryptocurrencies — New evidence using low frequency and high frequency data," Journal of Behavioral and Experimental Finance, Elsevier, vol. 50(C).
    • Handle: RePEc:eee:beexfi:v:50:y:2026:i:c:s2214635026000298
    DOI: 10.1016/j.jbef.2026.101167
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    Keywords

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    JEL classification:

    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G20 - Financial Economics - - Financial Institutions and Services - - - General

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