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The objective of this paper is to investigate the pandemic’s function as a driver of investor herding in international stock markets, given that the current coronavirus disease 2019 (COVID-19) crisis has caused a large rise in uncertainty.

The paper investigates the presence of herding behavior among the developed and BRICS (Brazil, Russia, India, China and South Africa) stock market indices during the COVID-19 crisis, by using a modified Cross-Sectional Absolute Deviation (CSAD) measure which is considered a proxy for herding and the wavelet coherence (WC) analysis between CSAD that captures the different inter-linkages between stock markets.

Using the CSAD model, the authors' findings indicate that the herding behavior of investors is present in stock markets during the four waves of COVID-19 crisis. The results also demonstrate that the transaction volume improve the herding behavior in the stock markets. As for the news concerning the number of cases caused by the pandemic, the results show that the pandemic does not stimulate herding; however, the number of deaths caused by this pandemic turns out to be a great stimulator of herding. By using the WC analysis, the authors' findings indicate the presence of herding behavior between the Chinese and stock markets (developed and emerging), especially during the first wave of the crisis and the presence of herding behavior between the Indian and stock markets (developed and emerging) in the medium and long run, especially during the third wave of the COVID-19 crisis.

The authors' study is among the first that examines the influence of the recent COVID-19 pandemic as a stimulator of herding behavior between stock markets. The study also uses the WC analysis next to the CSAD model to obtain robust results. The authors' results are consistent with the mental bias of behavioral finance where herding behavior is considered effective in volatility predictions and decision-making for international investors, specifically during the COVID-19 crisis.

This paper deeply investigates the herd behavior of the Egyptian mutual funds under changing and different conditions of the market pre- and post-events and compares the impact of asymmetric risk conditions on the herding behavior of the Egyptian mutual funds in both up and down markets.

We test for the existence of herding for the whole period from 2003 to 2022, as well as for the pre-and post-different Egyptian uprising periods. We employ two well-known models, namely the cross-sectional standard deviation (CSSD) and cross-sectional absolute deviation (CSAD) models. Additionally, we use the quantile regression approach.

We find that the behavior of mutual funds does not change following the different political and social events. For the whole period, we find evidence of herding behavior using only the model of CSAD in down-market conditions. We generalize our finding to be evidence of the existence herding behavior in different quantiles, under only the down market in specific points’ pre, post or both given events throughout the whole series. Conversely, during the upper market, we show a full absence of herding behavior considering all different quantiles. When the market is down, managers are afraid of the condition of uncertainty, neglecting their own private information, avoid acting independently and consequently, following other mutual funds. When the market is up, managers become rational and act fully independent.

Future research should delve deeper into the drivers of herding behavior, assess its longer-term effects, develop risk management strategies and consider regulatory measures to mitigate the potential negative impact on mutual fund performance and investor outcomes.

The study reveals that the behavior of mutual funds remains consistent despite various political and social events, suggesting a degree of resilience in their investment strategies. The research uncovers evidence of herding behavior in both high and low quantiles, but exclusively in down markets. In such conditions of market decline, fund managers appear to forsake their private information, exhibiting a tendency to follow the crowd rather than acting independently.

The study reveals that the behavior of mutual funds remains consistent despite various political and social events, suggesting a degree of resilience in their investment strategies. The research uncovers evidence of herding behavior in both high and low quantiles, but exclusively in down markets. In such conditions of market decline, fund managers appear to forsake their private information, exhibiting a tendency to follow the crowd rather than acting independently. Future research should delve deeper into the drivers of herding behavior, assess its longer-term effects, develop risk management strategies and consider regulatory measures to mitigate the potential negative impact on mutual fund performance and investor outcomes.

The paper investigates the herd behavior of the Egyptian mutual funds under asymmetric risk conditions, the study follows the spectrum of the herding behavior analysis and Egyptian mutual funds, extending the research with imperial analysis of market conditions pre- and post-events including currency floating, COVID-19 and political elections. The study gives substantial recommendations for policymakers and investors in emerging markets mutual funds.

The purpose of this paper is to examine whether Fama–French common risk-factor portfolio investors herd on a daily basis for five developed markets, namely, Europe, Japan, Asia Pacific ex Japan, North America and Globe.

To examine the herd behavior of common risk-factor portfolio investors, this paper utilizes the cross-sectional absolute deviations (CSAD) methodology, covering a daily data sampling period of July 1990 to January 2019 from Kenneth R. French-Data Library. CSAD driven by fundamental and non-fundamental information is assessed using Fama–French five-factor model.

The results do not provide evidence for herding under normal market conditions, either when reacting to fundamental information or non-fundamental information, for any region under consideration. However, Fama–French common risk-factor portfolio investors mimic the underlying risk factors in returns related to size and book-to-market value, size and operating profitability, size and investment and size and momentum of the equity stocks in European and Japanese markets during crisis period. Also, no considerable evidence is found for herding (on fundamental information) under crisis and up-market conditions except for Japan. Ancillary findings are discussed under conclusion.

Further research on new risk factors explaining stock return variation may help improve the model performance. The performance can be improved by adding new risk factors that are free from behavioral bias but significant in explaining common stock return variation. Also, it is necessary to revisit the existing common risk factors in order to understand behavioral aspects that may affect cost of capital calculations (e.g. pricing errors) and valuation of investment portfolios.

This is the first paper that examines the herd behavior (fundamental and non-fundamental) of Fama–French common risk-factor investors using five-factor model.

The study examines herding in the Indian stock and commodity futures market including agricultural, metal and energy commodities. Herding is studied under various market conditions: rising and declining, high and low volatility. The study also examines spillover effects of herding.

The study adapts the cross-sectional absolute deviation model given by Chang et al. (2000) to examine herding in Indian stock and commodity futures markets.

The results of the study indicate absence of herding among commodity futures under all market conditions except for the declining market where herding is present among energy futures. The investors investing in agricultural and energy commodities have a higher tendency to herd during high volatility days as compared to low volatility days. Further, the study of herding spillover effects indicates that the price fluctuations in metal commodities affect herding in agricultural and energy commodities.

The results can help market participants to diversify the risk by investing in agricultural, metal and energy futures along with the stocks.

Majority of the previous studies explore herding among stocks and ignore commodities especially agricultural commodities. This study attempts to fill the gap by studying herding among various commodity futures. To the best of our knowledge this is the first study to explore herding spillover effects in the Indian stock and commodity futures market.

This paper aims to investigate the herding behavior in the French stock market and its effect on the idiosyncratic conditional volatility at a sectoral level.

This sample covers all the listed companies in the French stock market, classified by sector, over four major crisis periods. The author modifies the cross-sectional absolute deviation (CSAD) model to include trading volume and investors sentiment as herding triggers. Furthermore, the author uses a modified GARCH model to investigate the effect of herding on conditional volatility.

Herding is present in the French market during crises, and it is present in only some sectors during the entire period. The main trigger for investors to embark into a collective herding movement differs from one sector to another. Furthermore, herding behavior has an inhibiting effect on market conditional volatility.

The author modifies the CSAD model to investigate the presence of herding in the French stock market at a sectoral level during turmoil periods. Furthermore, the particularly designed GARCH model provides new insights on the effect of herding and volume turnover on the conditional volatility.

This chapter examines the existence of dynamic herding behavior by Tunisian investors in the Tunisia stock market during the revolution period of 2011–2013. The sample covers all Tunindex daily returns as a proxy for the Tunisia stock exchange index over the period 2007–2018. The author modifies the cross-sectional absolute deviation model to include all market conditions (bull and bear markets) and the geopolitical crisis effect corresponding to the Tunisian Jasmine revolution during 2011–2013, and show that herding is indeed not present in the Tunisia stock market including during its turmoil periods. These findings imply that the Tunisian emerging financial market became more vulnerable to adverse herding behavior after the revolution. There is also a clear implication for capitalist firms and angel investors in Tunisia that adverse herding behavior tends to exist on days of higher uncertainty and information asymmetry.

This study aims to test the market anomaly of herding behavior driven by the response to supply chain disruptions in extreme market conditions such as those observed during COVID-19. The authors empirically test the response of the capital market participants for B2B firms, resulting in herding behavior.

Using the event study approach based on the market model, the authors test the impact of supply chain disruptions and resultant herding behavior across six sectors and among different B2B firms. The authors used cumulative average abnormal returns (CAAR) and cross-sectional absolute deviation (CSAD) to examine the significance of herding behavior across sectors.

The event study results show a significant effect of COVID-19 due to supply chain disruptions across specific sectors. Herding was detected across the automotive and pharmaceutical sectors. The authors also provide evidence of sector-specific disruption impact and herding behavior based on the black swan event and social learning theory.

The authors examine the impact of COVID-19 on herding in the stock market of an emerging economy due to extreme market conditions. This is one of the first studies analyzing lockdown-driven supply chain disruptions and subsequent sector-specific herding behavior. Investors and regulators should take sector-specific responses that are sophisticated during extreme market conditions, such as a pandemic, and update their responses as the situation unfolds.

This study aims to analyze the herding behavior in Southeast Asian stock markets. A cross-sectional absolute deviation of the returns approach is used to identify the presence of herding. Individual stocks and market returns were employed on each stock market on a daily basis during the period of January 2008 to December 2014 from five countries selected to obtain the necessary data. The samples observed consisted of stocks having higher liquidity and larger market capitalization for each stock market. The results suggest that there is significant evidence of herding behavior found in Kuala Lumpur and Philippines Stock Exchanges. In addition, there is no evidence of herding behavior in Indonesia, Singapore, and Thailand Stock Exchanges.

Herd behavior has been studied herein and tested based on primary respondents from Indian markets.

The paper expounds the empirical evidence by applying the cross-sectional absolute deviation method and reporting on herd behavior among decision-makers who are engaged in trading in the Indian stock market. Further, the study attempts to analyze the market-wide herding in the Indian stock market using 2230 daily, 470 weekly and 108 monthly observations of Nifty 50 stock returns for a period of nine years from April 1, 2009 to March 31, 2018 during the normal market conditions, extreme market conditions and in both increasing and decreasing market conditions.

In a span of a decade witnessing different market cycles, the authors’ results exhibit that there is no evidence of herding in any market condition in Indian stock market primarily due to the dominance of institutional investors and secondly because of low market participation by individual investors.

The results reveal that there is no impact of herd behavior on the stock returns in the Indian equity market during the normal market conditions. It highlights that the participation of individuals who are more prone to herding is more evident for short-run investments, contrary to long-term holdings.

This study investigates the presence of herding in the Brazilian stock market between 2012 and 2020 and associates it with the volume of searches on the Google platform.

Following methodologies are used to investigate the presence of herding: the Cross-Sectional Standard Deviation of Returns (CSSD), the Cross-Sectional Absolute Deviation (CSAD) and the Cross-Sectional Deviation of Asset Betas to the Market.

Most of the models detected herding. In addition, there was a causal relationship between peaks in Google search volumes and the incidence of herding across the whole period, especially in 2015 and 2019.

This study suggests that confirmation bias influences investors' decisions to buy or sell assets.