DOI: 10.14457/tu.the.2018.1682
title: Aggregate herd behavior: alternative methodologies and interpretations

description: This dissertation mainly focuses on “Herd Behavior” which is referred as a situation in which individuals act coherently as part of a group. Besides explaining the behavior of animals, the term is also used to describe some kinds of human phenomena. When applied to human behavior particularly financial decision-making processes, herding is likely to be one of the main causes of major events occurred in financial markets such as investment bubbles or stock market crashes. Moreover, according to the 2015 CFA Institute’s survey, herding is the most influential behavioral bias that affects investment decisions. While it is important and also interesting to comprehend more about herd behavior and its impact on the investors’ decision-making, yet, there are a limited number of studies that focused on such matter. In order to foster better understanding of herd behavior and its implications for financial markets, three scopes of the issue have been chosen. Those three scopes involve information environment viewing from different perspectives. Such information is recognized as a potential factor behind herd behavior. In an effort to portray an overall picture of the phenomenon, this dissertation merely focuses on an aggregate market herd behavior regardless of investor type.While the number of studies of return discontinuities in financial markets, so-called jumps, is countless, yet, most of them solely focus on developing measures to detect the occurrence of jumps. There have been studies suggesting that arrivals of jumps represent a utilization of private information implying that it eventually increases investors’ motivation to imitate such trade. However, in order to explore and identify the impact of return jumps on herd behavior, this dissertation develops and applies a new herding detection model by incorporating jump effect. Not only developing the new model, it also demonstrates the impact of private information arrival on herd behavior. The result mostly indicates that herding increases upon the existence of jumps during negative market return circumstances. Literatures suggest that one of the causes of herd behavior is uncertainty. Different forms of uncertainty can be identified and one of them is “information uncertainty” as a result of information asymmetry. Thus, corporate transparency, particularly in relation to financial information disclosure, is the main focus of this dissertation. Even there has been a research on an association between corporate transparency and herd behavior on aggregate level, however, a limitation caused by a proxy of corporate transparency still keeps us from making the most of research findings. In order to tackle such limitation, this dissertation applies a corporate transparency indicator, a market model R-squared, instead to demonstrate the impact of a firm characteristic, such as corporate transparency, on herd behavior. Especially for decile portfolio technique, the result mostly suggests that herding decreases when holding portfolio of firms with high corporate transparency, and vice versa.Without a doubt, every one of us has a very limited attention span and that affects our ability to receive information, quantitatively and qualitatively, and to decide accordingly. However, as herding is a convergence of behavior, a number of psychological studies suggest that the size of a crowd affects the possibility of herd behavior. On the other hand, it is also likely that herding attracts the crowd’s attention. Accordingly, this dissertation applies a VAR model together with a Google search volume index to explore, unlike previous studies, the association between investor attention and herd behavior. The result shows that there is a tendency that herding in the past can attract investors’ attention. However, the influence of investor attention on herd behavior is inconclusive.