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Purpose The purpose of this paper is to present the Islamic stock and Sukuk market efficiency and focus on the presence of investor herding behaviour (HB) captured by Hurst exponent estimation. Design/methodology/approach The Hurst exponent was estimated with various methods. The authors studied the evolving efficiency of the “Dow Jones” indices from 1 January 2010 to 30 December 2016 using a rolling sample of the Hurst exponent. In addition, they used a time-varying parameter method based on the Hurst of delayed returns. After that, the robust Hurst method was considered. In the next step, the efficiency of the different activity types of Islamic bonds was studied using an efficiency index. Finally, the Hurst exponent estimates were applied to assess the presence of HB. Findings The results show that, firstly, there’s a strong correlation between the “DJIM” and “DJSI” prices and returns. Secondly, by using robust Hurst estimate, it is observed that the “DJIM” is the most efficient market. The Hurst exponent estimation results show that HB is more intensive in the Islamic stock market. These results indicate also the inexistence of this behaviour in the studied Sukuk market. Research limitations/implications Sukuk as Islamic financial assets is recent. Their relative time series are not long enough to apply the long memory approach. Furthermore, this work can be extended to study other Islamic financial markets. Practical implications Herding affects risk-return characteristics of assets and has an impact on asset pricing models. Practitioners are interested in understanding herding and its timing as it might create profitable trading opportunities. Social implications This work analyses the impact of Islamic principles on the financial markets and their ability to understand some behavioural biases. Originality/value This study contributes to the literature by identifying the efficiency and the presence of HB with Hurst exponent estimation in Islamic markets.

PurposeUnlike 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/approachThis 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.FindingsThe 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/implicationsThe uncertainty of COVID-19 disease and the lasting of its duration make it difficult to make the best prediction.Practical implicationsThe 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/valueTo 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.

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Unlike conventional cryptocurrencies, Islamic ones are new technologies backed by tangible assets and are characterised by their fundamental values. After the COVID-19 outbreak, cryptocurrency responses have shown different behaviour to stock market reactions. However, there is a lack of studies on the efficiency of Islamic and green cryptocurrencies during the pandemic. This paper attempts to analyse the behaviour of three typical families of cryptocurrencies (conventional, Islamic, and green) extracted according to their availability in daily frequencies during COVID-19. For this purpose, their efficiency levels are studied before and after the outbreak by employing multifractal detrended fluctuation analysis (MFDFA) to make the best predictions and strategies. The inefficiency of the cryptocurrencies is assessed through a magnitude of long-memory (MLM) efficiency index, and the impact of COVID-19 on their efficiency is evaluated. The primary results show that HelloGold was the most efficient market before the COVID-19 outbreak and that subsequently Ethereum has been the mostefficient. In addition, the findings reveal that the cryptocurrency reactions are not similar and show more resilience in the Ethereum and Litecoin markets than in other cryptocurrency markets. The main contribution of this study is the evaluation of the impact of COVID-19 on the various classes of crypto money. This work has practical implications, as it provides new insights into trading opportunities and market reactions. Moreover,  he work has theoretical implications based on its evaluation of three distinct models from different doctrine viewpoints.