The DOI matches its Crossref record; 25 of the 25 references it cites were found in a registry or an index.
Herding behaviour in cryptocurrencies
Abstract
This study examines the presence of herding behaviour in the cryptocurrency market. The latter is the outcome of mass collaboration and imitation. Results from the static model suggest no significant herding. However, the presence of structural breaks and nonlinearities in the data series suggests applying a static model is not appropriate. Accordingly, we conduct a rolling-window analysis, and those results point to significant herding behaviour, which varies over time. Using a logistic regression, we find that herding tends to occur as uncertainty increases. Our findings induce useful insights related to portfolio and risk management, trading strategies, and market efficiency.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
1 retracted
RETRACTED: Datestamping the Bitcoin and Ethereum bubbles
Corbet, S., Lucey, B., & Yarovaya, L. (2018) · Finance Research Letters · doi:10.1016/j.frl.2017.12.006 · Cited by 638
RetractedMeasuring Economic Policy Uncertainty*
Baker, S. R., Bloom, N., & Davis, S. J. (2016) · The Quarterly Journal of Economics · doi:10.1093/qje/qjw024 · Cited by 12,458
Computation and analysis of multiple structural change models
Bai, J., & Perron, P. (2003) · Journal of Applied Econometrics · doi:10.1002/jae.659 · Cited by 5,771
A test for independence based on the correlation dimension
Broock, W. A., Scheinkman, J. A., Dechert, W. D., & LeBaron, B. (1996) · Econometric Reviews · doi:10.1080/07474939608800353 · Cited by 3,506
Bitcoin: Medium of exchange or speculative assets?
Baur, D. G., Hong, K., & Lee, A. D. (2018) · Journal of International Financial Markets, Institutions and Money · doi:10.1016/j.intfin.2017.12.004 · Cited by 1,449
On the hedge and safe haven properties of Bitcoin: Is it really more than a diversifier?
Bouri, E., Molnár, P., Azzi, G., Roubaud, D., & Hagfors, L. I. (2017) · Finance Research Letters · doi:10.1016/j.frl.2016.09.025 · Cited by 1,432
Self-citationCheah, E.-T., & Fry, J. (2015) · Economics Letters · doi:10.1016/j.econlet.2015.02.029 · Cited by 1,383
Exploring the dynamic relationships between cryptocurrencies and other financial assets
Corbet, S., Meegan, A., Larkin, C., Lucey, B., & Yarovaya, L. (2018) · Economics Letters · doi:10.1016/j.econlet.2018.01.004 · Cited by 1,335
Rational herding in financial economics
Devenow, A., & Welch, I. (1996) · European Economic Review · doi:10.1016/0014-2921(95)00073-9 · Cited by 1,263
Following the Pied Piper: Do Individual Returns Herd around the Market?
Christie, W. G., & Huang, R. D. (1995) · Financial Analysts Journal · doi:10.2469/faj.v51.n4.1918 · Cited by 1,163
Cited by
RETRACTED: Cryptocurrencies as a financial asset: A systematic analysis
Corbet, S., Lucey, B., Urquhart, A., & Yarovaya, L. (2019) · International Review of Financial Analysis · doi:10.1016/j.irfa.2018.09.003 · Cited by 1,172
RetractedDynamic connectedness and integration in cryptocurrency markets
Ji, Q., Bouri, E., Lau, C. K. M., & Roubaud, D. (2019) · International Review of Financial Analysis · doi:10.1016/j.irfa.2018.12.002 · Cited by 624
Self-citationVolatility connectedness in the cryptocurrency market: Is Bitcoin a dominant cryptocurrency?
Yi, S., Xu, Z., & Wang, G.-J. (2018) · International Review of Financial Analysis · doi:10.1016/j.irfa.2018.08.012 · Cited by 434
Bubbles in Bitcoin and Ethereum: The role of halving in the formation of super cycles
M'bakob, G. B. (2024) · Sustainable Futures · doi:10.1016/j.sftr.2024.100178 · Cited by 20
Metadata
- Authors
- Bouri, E., Gupta, R., & Roubaud, D.
- Venue
- Finance Research Letters
- Publisher
- Elsevier BV
- Published
- 2019-06
- Volume
- 29
- Pages
- 216-221
- ISSN
- 1544-6123 (ISSN-L)
- OpenAlex
- W2883143933
- Cited by
- 531
- References
- 25
- FWCI
- 49.52
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Herding behaviour in cryptocurrencies. https://app.cochat.ai/papers/10.1016/j.frl.2018.07.008
- The paper
- Bouri, E., Gupta, R., & Roubaud, D. (2019). Herding behaviour in cryptocurrencies. Finance Research Letters. https://doi.org/10.1016/j.frl.2018.07.008
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.