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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 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.

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.

In the stock market, herding behavior occurs when investors mimic the actions of others in their investment decisions. As a result, the market becomes inefficient and speculative bubbles form. This study aims to investigate the relationship between information, overconfidence, market sentiment, experience and national culture, and herding behavior among Malaysian investors. A total of 400 questionnaires are distributed to bank institutions' investors. The survey design based on cross-sectional data is analyzed using the Partial Least Squares Structural Equation Model. The results indicate that information, market sentiment, experience, and national culture are positively related to herding behavior, while overconfidence has no effect. With this, the government should strengthen regulations to prevent the dissemination of misleading information. Moreover, investors are encouraged to overcome narrow thinking by expanding their understanding of different cultures when making investment decisions.

This chapter examines herd behavior across national borders. A dynamic latent factor model with Gibbs sampling is used to decompose the national herd behavior into the world, regional, and country-specific components. Testing the daily data from 2000 through 2014 for 47 countries, we find that the impact of world factor on national herd behavior is short-lived. This study indicates that world and regional factors play a significant role in explaining the variations of national herd behavior, constituting 33% of the herding variability. The significance of world and regional components is likely to produce a biased herding estimator.

We introduce the idea that informational cascades can explain the observed regularity that emigrants from the same location tend to choose the same foreign location. Thus, informational cascades generate herd behavior. Herd behavior is compared with the network-externalities explanation of the same phenomenon of migration clustering.

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.

Networks are natural tools for understanding social and economic phenomena. For example, all markets are characterized by agents connected by complex, multilateral information networks, and the network structure influences economic outcomes. In an earlier study, we undertook an experimental investigation of learning in various three-person networks, each of which gives rise to its own learning patterns. In the laboratory, learning in networks is challenging and the difficulty of solving the decision problem is sometimes massive even in the case of three persons. We found that the theory can account surprisingly well for the behavior observed in the laboratory. The aim of the present paper is to investigate important and interesting questions about individual and group behavior, including comparisons across networks and information treatments. We find that in order to explain subjects’ behavior, it is necessary to take into account the details of the network architecture as well as the information structure. We also identify some “black spots” where the theory does least well in interpreting the data.