The impact of COVID-19 on Ethereum returns and Ethereum market efficiency

Abstract

This paper aims to identify herding biases and assess the inefficiency of Ethereum using an inefficiency index (MLM). Additionally, it investigates the nonlinear dynamical properties of Ethereum by estimating the MFDFA, aiming to deduce the impact of COVID-19 on Ethereum’s performance. The paper also captures abnormal changes resulting from COVID-19-related events and assesses their influence on the Ethereum market response.

The empirical results show that Ethereum was multifractal before the pandemic and became less fractal in the period following the outbreak using Generalized Hurst Exponent (GHE) estimation. Based on the MLM measure of efficiency, we found Ethereum to be more efficient in the first phase of the pandemic than before it, and as in the Hausdorff topology, the pandemic reduced herd bias. The event study analysis took into account specific events related to the pandemic and showed that each led to significant abnormal returns in the Ethereum market. The results reported are used to empirically establish differences in the value of Ethereum before and during the COVID-19 pandemic. The results are useful in a general sense for traders, investors, and policy makers because they provide new information about market trading opportunities and social responses.