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The authors employ the vessels that comprise the dry bulk segment of the maritime industry and examine how market sentiment affects the herding behavior of shipping investors in a real asset market.

The authors employ a threshold regression model to examine how changes in market sentiment can affect herding behavior in oceanic dry bulk shipping.

The results show that the behavioral aspect of investing, measured through intentional and unintentional herding, contrary to the results for financial markets, is affected by sentiment on the buy side (newbuildings) but not on the sell side (scrapping). Furthermore, the authors provide evidence that when market sentiment is negative, investors tend to follow market leaders (intentional herding), while, when sentiment is positive, unintentional herding leads to common investment practices among shipping investors.

The results have significant implications both for academics and for practitioners since they reflect a clear distinction of the pattern of investment decisions for real assets, compared to financial assets.

The purpose of this paper is to examine intentional herding among institutional investors with a particular focus on the technology sector that was the driver of the “New Economy” in the USA during the dot-com bubble of the 1990s.

Using data on technology stockholdings of 115 large institutional investors, the authors test the presence of herding by examining linear dependence and feedback between individual investors’ technology stockholdings and that of the aggregate market. Unlike other models to detect herding, the authors use Geweke (1982) type causality tests that allow authors to disentangle spurious herding from intentional herding via tests of bidirectional and instantaneous causality across portfolio positions in technology stocks.

After controlling information-based (spurious) herding, the tests show that 38 percent of large institutional investors tend to intentionally herd in technology stocks.

The findings support the existing literature that investment decisions by large institutional investors are not only driven by fundamental information, but also by cognitive bias that is characterized by intentional herding.

The purpose of this paper is to examine industry herding among the institutional investors and to find whether their herding behaviour is intentional or unintentional.

The study uses Lakonishok et al. (1992) model to examine the presence of industry herding behaviour among institutional investors. To determine whether the herding observed is intentional or unintentional, herding measure is regressed with volatility, volume, beta and return. The period of the study is from 1 April 2005-31 March 2015.

The findings of the study showed that though institutional investors have herding tendency towards most of the industries, in the overall period industry herding was not significant. The herding found in some industrial sectors was linked to economic performance of those sectors in India during the period of study and hence the herding was unintentional in nature.

This is the first attempt to study industry herding among institutional investors and their intent in Indian market ever since the country opened its market to foreign investors in a big way. Present study is limited to the use of only bulk/block data instead of the entire trading data for the period.

This study is the first attempt to investigate industry herding behaviour of institutional investors in the market using their bulk and block trading data. The herding observed in well performing industries has been shown to be unintentional and hence rational. The results indicate that the entry of big institutional investors, including foreign institutions into the Indian market has not destabilised the market by irrational herding.

The purpose of this paper is to examine the mimicking behaviour of institutional investors in the stock market.

The study focusses on examining the herding behaviour among institutional investors in the stock market by considering the bulk and block trade on the constituent NIFTY 50 index during the period 2005–2015 using Lakonishok–Schleifer–Vishny (1992) model. The study also aims to find out whether their herding behaviour is intentional or unintentional in nature.

The findings of the study showed no sign of herding behaviour in the market; out of 50 constituent stocks of NIFTY 50, there was significant herding in 15 stocks, with buy herding in 11 stocks and sell herding in four stocks, and remaining 35 stocks were totally free from herding behaviour. In addition, the results proved that the herding behaviour observed on the stocks is of unintentional in nature.

Present study is limited to the use of constituent stocks of the Benchmarking Index NIFTY 50.

This study is the first attempt to investigate the herding behaviour of institutional investors in the market using bulk and block trade and also to explore their intent in herding behaviour.

The purpose of this study is to investigate how the price delay of cryptocurrencies to market news affects the herding behavior of investors, particularly during turbulent events such as the COVID-19 period.

The paper investigates the presence of herding behavior by using Cross-Sectional Absolute Deviation (CSAD) measures. We also investigate the herding activity in the crypto traders’ behavior during up and down-market movements periods and under investor extreme sentiment conditions. The speed of cryptocurrencies’ price response to the information embedded in the market is assessed based on the price delay measure proposed by Hou and Moskowitz (2005).

Our findings suggest that cryptocurrencies characterized by high price delays exhibit more herding among investors, thereby highlighting higher degrees of market inefficiencies. This is also apparent during periods of extreme investor sentiment. We also document an asymmetric herding behavior across cryptocurrencies that present different levels of price speed adjustments to market news during bullish and bearish market conditions. Our results are consistent and robust across different sub-periods, various market return estimations and different price delay frequencies.

The study provides crucial guidelines for investors’ asset allocation and risk management strategies. This study is also valuable to regulators and policymakers, particularly in light of the increasing importance of financial reforms aimed at mitigating market distortions and enhancing the resilience of the cryptocurrency market. More specifically, regulations that improve the market’s information efficiency should be prioritized to speed up the response time of cryptocurrency prices to market information, which can help reduce the investors' herding behavior.

This paper makes a novel contribution to the academic literature by investigating the unexplored relationship between cryptocurrency price delays and the presence of herding behavior among investors, especially in times of uncertainty such as the COVID-19 pandemic.

We develop our theoretical framework from the viewpoint of the information asymmetry and the agency theory that the Chinese mutual funds exhibit herd behaviour, and provide empirical evidence by using cross-sectional data of all the Chinese mutual funds between 1999 and 2003. We find that the Chinese mutual funds show overall herding, buy herding and sell herding, and the degree of sell herding is higher than that of buy herding. The degree of Chinese herding is higher than their US counterpart from all the three perspectives. This may be largely due to the institutional factors rather than those firm-specific factors that influence the US mutual funds investment decision.

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.

This study aims to examine investors’ herd behaviour for various calendar events and size-based stock portfolios in Pakistan. The authors consider three calendar effects, crisis (COVID-19 and financial crisis 2018–19), announcement of political news and popular calendar anomalies (month-of-the-year and day-of-the-week), and investigate the impact of stock size on calendar effect in terms of investors’ herd behaviour.

The study uses non-linear specification to capture herd behaviour using firm-level daily data for 496 stocks listed on Pakistan Stock Exchange over the period 2001–2020.

The results indicate herd formation during periods of COVID-19, financial crisis, political news announcements and January (month-of-the-year). The authors also observe significant herding for the biggest and smallest size stocks over complete period. However, the authors find more pronounced herding in big stocks during January as compared to the more noticeable herding in small stocks over complete period. The findings suggest that herding in small stocks is not the main cause of January herding and hint on the prevalence of significant institutional herding during January.

The stock prices destabilize because of the mimicking behaviour during crisis periods, days of political announcements and month of January. Implementation of insider trading laws and transparent information environment can help in reducing these effects and increasing market efficiency.

The authors consider the recent COVID period in our analysis. In addition, we provide new evidence on the possible impact of stock size on calendar effect in terms of herd behaviour, which, to the best of the authors’ knowledge, has not yet been documented in literature.

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 test the presence of herding behavior in commodity markets, including energy, metals and agriculture. Additionally, the authors investigate the possible asymmetric effect of oil price changes on the herding behavior in these markets.

The authors examine herding based on the cross-sectional absolute deviation (CSAD) model in a static and time-varying perspective.

By using daily data over the period 2003–2017, the authors’ findings firstly support the dynamic nature of investor behavior in commodity markets, which oscillates between antiherding during the normal period and herding during and after the global financial crisis of 2008. Furthermore, results highlight that the asymmetric impact of oil shocks on herding differs across commodity sectors and periods. Additionally, herding seems to be more pronounced when the oil market declines, which may be due to the pessimistic investors' sentiments.

This study provides insight into what factors influence herd behavior in commodity markets. The understanding of factors driving herding aids investors to avoid the impact of this behavior and its consequences

To the authors’ knowledge, this study is the first to examine whether the level of herding depends on the oil price fluctuations, as well as the asymmetric effect of the oil price on herding behavior in commodity markets.