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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 is concerned with markets operating in Turkey in the Istanbul Stock Exchange (BIST), which have been observed and studied in relation to herd behavior. During the research part of the study, the existence of herd behavior was investigated with the help of the daily closing price data of the firms in BIST between January 2011 and December 2017. In the research section of the study, the authors used regression analysis. In the analysis, the authors used the index value of the BIST whole Index. The average value of the index value of BIST whole Index was taken. Then, according to this average, 1% percentile and 5% percentile were taken. In the periods in the 1% percentile (at the dates) the result was that herd behavior was present. The herd behavior was observed for the periods (for dates) included in the percentile of 1%. On the other hand, the results of the analysis for the 5% percentile show that the herd behavior is only seen in the upper extremes.

Starting in the 1980s, financial liberalization and technological developments have enabled individual investors to participate in financial markets and carry out easy transactions. With these developments, academics began to wonder how the individual investors decide to invest and what factors affect these decisions.

According to traditional finance theory, it is suggested that markets are efficient and investors show rational behaviors in their financial purchasing decisions. However, in many studies conducted in recent years, it was determined that investors included emotional elements as well as rational elements in their decision-making process and therefore exhibited irrational behaviors by believing rumors instead of real information. It is thought that many factors such as personal characteristics, psychological factors, demographic and socio-economic factors play a role in the behavior of investors in purchasing a financial product.

In this study, the importance of herd behavior, which is one of the psychological factors that play a very important role in financial markets, on financial product purchasing process is examined in the light of the behavioral finance theory. It is thought that information included in the study will be useful for researchers who want to study herd behavior and for those who are interested in the subject.

This study investigates how individuals' self-awareness (specifically, private and public self-awareness) and environment-awareness (perceived expertise, similarity and familiarity) shape herd behavior, encompassing discounting one’s information and imitating others. Drawing from latent state-trait theory, this research aims to discern the impact of these factors on purchase intention and behavior.

Longitudinal data from 231 users in Xiaohongshu, China’s leading social commerce platform, were collected to test the proposed model and hypotheses.

The findings from this study show that private self-awareness negatively influences discounting one’s own information and imitating others. Public self-awareness positively affects imitating others, while it does not affect discounting one’s own information. Perceived expertise diminishes discounting one’s own information but does not significantly affect imitating others. Perceived similarity and perceived familiarity are positively related to discounting one’s own information and imitating others. The results confirm different interaction effects between self-awareness and environment-awareness on herd behavior.

First, this contributes back to the latent state-trait theory by expanding the applicability of this theory to explain the phenomenon of herd behavior. Second, this study takes an important step toward theoretical advancement in the extant literature by qualifying that both self- and environment-awareness should be considered to trigger additional effects on herd behavior. Third, this study provides a more enlightened understanding of herd behavior by highlighting the significance of considering the interplay between self- and environment-awareness on herd behavior. Finally, this study also empirically confirms the validity of classifying self-awareness into private and public aspects.

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.

It has been argued in the literature that structural changes in the financial markets, such as integration, have the potential to cause herding behavior or correlated behavioral patterns in traders. The purpose of this study is to investigate whether there is any financial herding behavior in the Latin American Integrated Market (MILA), a transnational stock market composed of Chile, Peru, Colombia and Mexico stock exchanges and whether there is any ARCH or GARCH effect in the herding behavior models.

This study uses the modified return dispersion approach on daily index return data. The sample period is from January 03, 2002 to May 07, 2019. The data are obtained from the MILA database. To count time-varying volatilities in herding models, the authors run ARCH family regression with GARCH (1,1) settings. Hwang and Salmon (2004) model is used as a robustness test.

The authors found strong herding behavior under the general market conditions and moderate and partial herding behavior under some specified markets circumstances, such as bull and bear markets and high-low volatility states. Moreover, the pre-MILA period exhibits more herding behavior than the post-MILA period. The empirical results show that most of the ARCH and GARCH effects are statistically significant, implying that the past information of stock returns and market volatility significantly affect the volatility of following periods, which can also explain the formation of herding tendency among investors. Finally, the results of the robustness tests (Hwang and Salmon, 2004) confirm herding in all periods, except full sample period for Mexico and post-MILA period for Mexico and Colombia.

This study investigates the herding behavior in the MILA market in terms of market return, volatility and timing. A limitation of the paper is that the authors have not included other factors on the formation of herding behavior, such as macroeconomic factors, effects of regional or international markets and policy influences. The authors will explore the issue in the extension of the paper.

As MILA is the first virtual integration of stock exchanges without merging, the study provides useful findings and draws good inferences of herding behavior in the MILA market in terms of market return, volatility and timing which are useful for academics, investors and policymakers in their investment and decision makings.

The paper provides useful findings and draws good inferences of herding behavior in the MILA market in terms of market return, volatility and timing which are not only useful in practical implications, but also in social implications.

This study contributes to the herding literature by examining four different hypotheses in respect of the unique case of transnational stock exchange without fusions or corporate mergers, where each market maintains its independence and regulatory autonomy. The authors also contribute to the literature by including both ARCH and GARCH effects in the herding behavioral models along the Hwang and Salmon (2004) approach.

The purpose of this paper is to investigate investor herding behaviour and the effect of presidential elections on investor herding behaviour in African stock markets at the sector level.

The study segregates listed firms into financial, consumer goods, consumer services and basic materials sectors and uses the cross-sectional absolute deviation approach as a metric of detecting herding in each of the sectors. The authors extend the model to tease out the effect of presidential elections on investor herding behaviour.

The study reveals that sectoral differences are fundamental to the evolution of herding. Herding is prominent in a financial services sector dominated by banks. The phenomenon also prevails in markets with smaller consumer goods and services sectors. A post-presidential election effect on investor herding is found for the consumer goods and services sectors of Ghana and a pre-presidential election effect is documented in Nigeria's consumer services sector. The authors conclude that post-presidential election effect is as a result of political connections whilst a pre-presidential election effect is attributable to political business cycles.

The study is based on four African countries due to data constraints. Nonetheless, the study is the first in Africa to the best of the authors' knowledge, and the results are very solid and have a lot of practical and policy implications.

The study has implications for investors as it guides investment behaviour in pre- and post-presidential election periods.

Past studies on investor herding behaviour in African stock markets have largely concentrated on the aggregate market. Knowledge on sectoral differences in investor herding is almost non-existent for African stock markets. Furthermore, premised on the fact that stock markets react to presidential elections, there is no known study that have attempted to examine the effect of presidential elections on investor herding behaviour. This paper contributes to the literature by providing evidence on sectoral differences in investor herding behaviour and the effect of presidential elections on sectoral herding behaviour.

The purpose of this paper is to examine the herding behavior in GCC Islamic stock markets.

The authors followed the methodology developed by Chiang and Zheng (2010) to test herding behavior. Cross-sectional tests have been considered in this paper. The authors use both OLS and GARCH estimations to examine herding behavior by using a sample of GCC Islamic stock markets.

By applying monthly data for the period between January 2006 and February 2016 for five Islamic GCC stock returns (Bahrain, Kuwait, Qatar, Saudi Arabia and UAE), results suggest a significant evidence of herd behavior in Saudi and Qatari Islamic stock markets only. When the authors take into account the existence of asymmetry in herd behavior between down- and up-market periods, evidence of herding behavior during down market periods in the case of Qatar and Saudi Arabia was found. In addition, the authors found that Kuwaiti and Emirates Islamic stock markets herd with the local conventional stock market, showing the interdependencies between Islamic and conventional markets.

In this paper, the authors found an absence of herding behavior in some Islamic stock markets (Bahrain, Kuwait and Emirates). This is not the result of Shariah guidelines in these Islamic markets, but this is mainly due to the weak oscillations of returns which are very close to zero. In our future research, the authors could apply daily data and compare the results to those obtained in this paper by using monthly data.

This paper provides a practical framework in order to analyze the herding behavior concept for GCC Islamic stock markets. Its originality consists of linking the herding behavior to ethics and morality to verify whether the properties and guidelines of Islam are respected in Islamic stock markets. To the best of the authors’ knowledge, no other paper has treated the case of herding behavior in Islamic stock markets and taking into account the possible influence of the conventional market on the Islamic stock market that may impact herding behavior.

This article aims to clarify the mechanism by which herding behavior influences perceived market efficiency, investment decisions and the performance of individual investors actively trading on the Pakistan Stock Exchange (PSX).

The deductive approach was used in this study, as the research is based on the theoretical framework of behavioral finance. A questionnaire and cross-sectional design were employed to collect data from the sample of 309 investors trading on the PSX. The collected data were analyzed using SPSS and AMOS graphics software. Hypotheses were tested using structural equation modeling (SEM).

The article provides further empirical insights into the relationship between herding behavior and investment management and perceived market efficiency. The results suggest that herding behavior has a markedly negative influence on perceived market efficiency and investment performance, while positively influencing the decision-making of individual investors.

The current study is the first to focus on links between herding behavior and investment management activities and perceived market efficiency. This article enhances the understanding of the role that herding behavior plays in investment management and, more importantly, it improves understanding of behavioral aspects and their influence on investment decision-making in an emerging market. It also adds to the literature in the area of behavioral finance, specifically the role of herding behavior in investment management; this field is in its initial stage, even in developed countries, while little work has been done in developing countries.

In recent years, significant research has focused on the question of whether severe market periods are accompanied by herding behavior. As herding behavior is a considerable cause of the speculative bubble and leads to stock market deviations from their basic values it is necessary to examine the motivators which led to herding behavior among investors. The paper aims to discuss this issue.

In this study, the authors performed a two-phase analysis to address the research questions of the study. In the first phase, for text analysis NVivo software was used to identify the factors driving herding behavior among Indian stock investors. The analysis of a text was performed using word frequency analysis. While in the second phase, the Fuzzy-AHP analysis techniques were employed to examine the relative importance of all the factors determined and assign priorities to the factors extracted.

Results of the study depicted Investor Cognitive Psychology (ICP), Market Information (MI), Stock Characteristics (SC) as the top-ranked factors driving herding behavior, while Socio-Economic Factors (SEF) emerged as the least important factor driving herding behavior.

The current study was undertaken among stock investors from North India only. Moreover, numerous factors are not part of the study but might significantly influence the investors' herding behaviors.

Comprehending the influences of the different factors discussed in the study would enable stock investors to be more aware of their investment choices and not resort to herd behavior. This research enables decision-makers to understand the reasons for herd activity and helps them act accordingly to improve the stock market's performance.

The current study will provide an inclusive overview of herding behavior motivators among Indian stock investors. This study's results can be extremely useful for both academics and policymakers to gain some insight into the functioning of the Indian stock market.