Examining herding behavior in the cryptocurrency market
Examining herding behavior in the cryptocurrency market

Summary/Abstract: Research background: The research employs the Cross-Sectional Absolute Deviation of re-turns (CSAD) model, augmented with modifications by Chiang and Zheng (2010) to addressasymmetric investor behavior, facilitating the detection of herding behavior. Additionally, thestudy leverages Quantile Regression (QR), demonstrated by Barnes and Hughes (2002) toeffectively capture extreme values in financial data with fat tails or skewed distributions. Thisapproach is particularly relevant in the context of the volatile cryptocurrency market, allowingfor the analysis of outliers and the assessment of the magnitude of return impacts using T-statand Quantile Process Estimates.Purpose of the article: This study primarily centers its empirical analysis on identifying mar-ket-wide herding behavior (Henker et al., 2006) within the cryptocurrency market, spanningfrom January 1, 2016, to February 1, 2019, juxtaposed with the period from January 1, 2019, toJanuary 7, 2022. The selected time frames were chosen to evaluate potential shifts in herding dynamics within this market, particularly during its phases of rapid expansion and subse-quent stagnation.Methods: The Cross-Sectional Absolute Deviation (CSAD) methodology, as proposed byChiang and Zheng (2010), was employed for herding detection, alongside the incorporation ofdummy variables to discern the market conditions under which herding occurs. Herdingbehavior manifests when dispersion diminishes, or its increase is less than proportionate tomarket returns, indicating an inverse correlation between market returns and dispersion inthe presence of herding. Additionally, CSAD estimation was conducted utilizing quantileregression to encompass a broader range of quantiles, facilitating the identification of herdingtendencies across various return magnitudes. To delve further into investor behavior, Bitcoinwas utilized as an illustrative example, elucidating investor reactions to market bubblesthrough the application of the Hodrick-Prescott (HP) Filter.Findings & value added: The findings reveal instances of herding behavior during downwardmarket movements and at higher return levels preceding 2019. However, post-2019, herding isobserved during upward market movements and at medium to higher return levels. Thisstudy presents compelling evidence of herding phenomena coinciding with the bursting ofbubbles, particularly concerning Bitcoin. The findings provide a deeper understanding of howherding manifests differently across distinct market conditions and timeframes, offeringactionable insights for investors and policymakers navigating the volatile cryptocurrencylandscape. Additionally, by highlighting the correlation between herding behavior and mar-ket bubbles, particularly in the context of Bitcoin, this study contributes to the broader dis-course on cryptocurrency market dynamics.

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