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COVID-19, bitcoin market efficiency, herd behaviour

Author

Listed:
  • Emna Mnif
  • Anis Jarboui

Abstract

Purpose - Unlike previous crisis where investors tend to put their assets in safe havens like gold, the recent coronavirus pandemic is characterised by an increase in the Bitcoin purchasing described as risk heaven. This paper aims to analyse the Bitcoin dynamics and the investor response by focusing on herd biases. Therefore, the main objective of this work is to study the degree of efficiency through multifractal analysis in order to detect herd behaviour leading to build the best predictions and strategies. Design/methodology/approach - This paper develops a novel methodology that detects the presence of herding biases and assesses the inefficiency of Bitcoin through an inefficiency index (MLM) by using statistical indicators defined by measures of persistence. This study, also, investigates the nonlinear dynamical properties of Bitcoin by estimating the Multifractal Detrended Fluctuation Analysis (MFDFA) leading to deduce the effect of COVID-19 on the Bitcoin performance. Besides, this work performs an event study to capture abnormal changes created by COVID-19 related events capable to analyse the Bitcoin market response. Findings - The empirical results of the generalized Hurst exponent GHE estimation indicates that Bitcoin is multifractal before this pandemic and becomes less fractal after the outbreak. Using an efficiency index (MLM), Bitcoin is found to be more efficient after the pandemic. Based on the Hausdorff topology, the authors showed that this pandemic has reduced the herd bias. Research limitations/implications - The uncertainty of COVID-19 disease and the lasting of its duration make it difficult to make the best prediction. Practical implications - The main contribution of this study is the evaluation of the Bitcoin value after the COVID19 outbreak. This work has practical implications as it provides new insights on trading opportunities and social reactions. Originality/value - To the authors’ knowledge, this work represents the first study that analyses the Bitcoin response to different events related to COVID-19 and detects the presence of herding behaviour in such a crisis.

Suggested Citation

  • Emna Mnif & Anis Jarboui, 2021. "COVID-19, bitcoin market efficiency, herd behaviour," Review of Behavioral Finance, Emerald Group Publishing Limited, vol. 13(1), pages 69-84, March.
    • Handle: RePEc:eme:rbfpps:rbf-09-2020-0233
    DOI: 10.1108/RBF-09-2020-0233
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    Citations

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    Cited by:

    1. Nguyen, Huu Manh & Bakry, Walid & Vuong, Thi Huong Giang, 2023. "COVID-19 pandemic and herd behavior: Evidence from a frontier market," Journal of Behavioral and Experimental Finance, Elsevier, vol. 38(C).
    2. Memon, Bilal Ahmed & Yao, Hongxing & Naveed, Hafiz Muhammad, 2022. "Examining the efficiency and herding behavior of commodity markets using multifractal detrended fluctuation analysis. Empirical evidence from energy, agriculture, and metal markets," Resources Policy, Elsevier, vol. 77(C).
    3. Zdenek Smutny & Zdenek Sulc & Jan Lansky, 2021. "Motivations, Barriers and Risk-Taking When Investing in Cryptocurrencies," Mathematics, MDPI, vol. 9(14), pages 1-22, July.
    4. Abdullah, Mohammad & Chowdhury, Mohammad Ashraful Ferdous & Sulong, Zunaidah, 2023. "Asymmetric efficiency and connectedness among green stocks, halal tourism stocks, cryptocurrencies, and commodities: Portfolio hedging implications," Resources Policy, Elsevier, vol. 81(C).
    5. Mustafa Özer & Serap Kamisli & Fatih Temizel & Melik Kamisli, 2022. "Are COVID-19-Related Economic Supports One of the Drivers of Surge in Bitcoin Market? Evidence from Linear and Non-Linear Causality Tests," Mathematics, MDPI, vol. 11(1), pages 1-24, December.
    6. Assaf, Ata & Mokni, Khaled & Yousaf, Imran & Bhandari, Avishek, 2023. "Long memory in the high frequency cryptocurrency markets using fractal connectivity analysis: The impact of COVID-19," Research in International Business and Finance, Elsevier, vol. 64(C).

    More about this item

    Keywords

    Bitcoin; Herding bias; Efficiency index; Generalised Hurst exponent; COVID19; C22; G10; G14; G15;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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