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Signal-herding in cryptocurrencies

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  • Philippas, Dionisis
  • Philippas, Nikolaos
  • Tziogkidis, Panagiotis
  • Rjiba, Hatem

Abstract

The paper examines the influence of informative signals derived from exogenous factors on herding intensity in the cryptocurrency market. We propose a novel approach whereby extracted signals are endogenized in investors’ decision-making. The signals may induce investors to converge towards (depart from) the market consensus, contributing to herding amplification (dampening). The findings reveal substantial asymmetries with respect to the intensity of herding stemming from exogenous influences. We conclude that the evidenced diversity is indicative of the value that investors attach to the information embedded in the different external signals they receive.

Suggested Citation

  • Philippas, Dionisis & Philippas, Nikolaos & Tziogkidis, Panagiotis & Rjiba, Hatem, 2020. "Signal-herding in cryptocurrencies," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 65(C).
    • Handle: RePEc:eee:intfin:v:65:y:2020:i:c:s1042443120300755
    DOI: 10.1016/j.intfin.2020.101191
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    5. Rosy Dhall & Bhanwar Singh, 2020. "The COVID-19 Pandemic and Herding Behaviour: Evidence from India’s Stock Market," Millennial Asia, , vol. 11(3), pages 366-390, December.
    6. Cole, Benjamin M. & Dyhrberg, Anne H. & Foley, Sean & Svec, Jiri, 2022. "Can Bitcoin be Trusted? Quantifying the economic value of blockchain transactions," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 79(C).
    7. Mohamad, Azhar & Stavroyiannis, Stavros, 2022. "Do birds of a feather flock together? Evidence from time-varying herding behaviour of bitcoin and foreign exchange majors during Covid-19," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 80(C).
    8. Jinesh Jain & Nidhi Walia & Simarjeet Singh & Esha Jain, 2022. "Mapping the field of behavioural biases: a literature review using bibliometric analysis," Management Review Quarterly, Springer, vol. 72(3), pages 823-855, September.
    9. Naeem, Muhammad Abubakr & Mbarki, Imen & Shahzad, Syed Jawad Hussain, 2021. "Predictive role of online investor sentiment for cryptocurrency market: Evidence from happiness and fears," International Review of Economics & Finance, Elsevier, vol. 73(C), pages 496-514.
    10. Tsionas, Mike G. & Philippas, Dionisis & Philippas, Nikolaos, 2022. "Multivariate stochastic volatility for herding detection: Evidence from the energy sector," Energy Economics, Elsevier, vol. 109(C).
    11. Bikramaditya Ghosh & Spyros Papathanasiou & Dimitrios Kenourgios, 2022. "Cross-Country Linkages and Asymmetries of Sovereign Risk Pluralistic Investigation of CDS Spreads," Sustainability, MDPI, vol. 14(21), pages 1-10, October.
    12. Neto, David, 2022. "Examining interconnectedness between media attention and cryptocurrency markets: A transfer entropy story," Economics Letters, Elsevier, vol. 214(C).
    13. Daekook Kang, 2021. "Box-office forecasting in Korea using search trend data: a modified generalized Bass diffusion model," Electronic Commerce Research, Springer, vol. 21(1), pages 41-72, March.
    14. Philippas, Dionisis & Dragomirescu-Gaina, Catalin & Goutte, Stéphane & Nguyen, Duc Khuong, 2021. "Investors’ attention and information losses under market stress," Journal of Economic Behavior & Organization, Elsevier, vol. 191(C), pages 1112-1127.
    15. Yarovaya, Larisa & Matkovskyy, Roman & Jalan, Akanksha, 2021. "The effects of a “black swan” event (COVID-19) on herding behavior in cryptocurrency markets," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 75(C).
    16. N. Blasco & P. Corredor & N. Satrústegui, 2022. "The witching week of herding on bitcoin exchanges," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-18, December.
    17. Ukpong, Idibekeabasi & Tan, Handy & Yarovaya, Larisa, 2021. "Determinants of industry herding in the US stock market," Finance Research Letters, Elsevier, vol. 43(C).
    18. Jia, Boxiang & Shen, Dehua & Zhang, Wei, 2022. "Extreme sentiment and herding: Evidence from the cryptocurrency market," Research in International Business and Finance, Elsevier, vol. 63(C).
    19. Umar, Muhammad & Su, Chi-Wei & Rizvi, Syed Kumail Abbas & Shao, Xue-Feng, 2021. "Bitcoin: A safe haven asset and a winner amid political and economic uncertainties in the US?," Technological Forecasting and Social Change, Elsevier, vol. 167(C).
    20. Nikolaos A. Kyriazis, 2021. "Investigating the diversifying or hedging nexus of cannabis cryptocurrencies with major digital currencies," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 44(2), pages 845-861, December.
    21. Sakemoto, Ryuta, 2021. "Economic Evaluation of Cryptocurrency Investment," MPRA Paper 108283, University Library of Munich, Germany.
    22. Neto, David, 2021. "Are Google searches making the Bitcoin market run amok? A tail event analysis," The North American Journal of Economics and Finance, Elsevier, vol. 57(C).
    23. Imran Yousaf & Shoaib Ali & Elie Bouri & Anupam Dutta, 2021. "Herding on Fundamental/Nonfundamental Information During the COVID-19 Outbreak and Cyber-Attacks: Evidence From the Cryptocurrency Market," SAGE Open, , vol. 11(3), pages 21582440211, July.

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    More about this item

    Keywords

    Signal-herding; Conditional herding; Cryptocurrencies;
    All these keywords.

    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
    • G40 - Financial Economics - - Behavioral Finance - - - General

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