herd behavior
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2022 ◽  
Vol 59 ◽  
pp. 101506
Author(s):  
Enkhbayar Choijil ◽  
Christian Espinosa Méndez ◽  
Wing-Keung Wong ◽  
João Paulo Vieito ◽  
Munkh-Ulzii Batmunkh

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Shilpi Gupta ◽  
Monica Shrivastava

PurposeThe study aims to understand the impact of loss aversion and herding on investment decision of retail investors. The study further evaluates the mediating role of fear of missing out (FOMO) in retail investors on these relationships.Design/methodology/approachThe study employed questionnaire survey to collect data from retail investors of Indian stock market. Total 323 data were collected. The collected data were examined using SmartPLS. Factor analysis and partial least square structural equation modeling were employed for fulfilling the objectives of the study.FindingsThe results of the study revealed that investment decisions of retail investors are significantly influenced by loss aversion, herd behavior as well as FOMO. Assessing the impact of herd behavior and loss aversion on investment decision in presence and absence of FOMO exposed that FOMO partially mediates these relations. The mediation was complementary in nature as the presence of FOMO increased the influence of loss aversion and herd behavior on retail investor's investment decisions.Practical implicationsBehavioral predispositions are accountable for numerous irregularities in stock markets. Thus, it is quite substantial to realize the stimulus of these partialities on investment decisions. The outcomes of this study would help financial planners and investors to keep in mind the different ways their decision outcomes could be biased and try to ignore them.Originality/valueThough there have been many studies conducted on behavioral biases and their impact on investment decisions, there are very few studies that have taken into account the FOMO factor in investment, in context of the behavioral biases. Theoretically, FOMO has been linked with herd behavior and greed of earning more, but there are very few empirical supports to this fact. Thus, this study is an attempt to fill this gap by examining the role of FOMO on investment decisions and the different biases associated with it.


2021 ◽  
Vol 31 (14) ◽  
Author(s):  
Rajat Kaushik ◽  
Sandip Banerjee

Bachelor herd behavior is very common among juvenile animals who have not become sexually matured but have left their parent groups. The complex grouping or schooling behavior provides vulnerable juveniles refuge from predation and opportunities for foraging, especially when their parents are not within the area to protect them. In spite of this, juvenile/immature prey may easily become victims because of their greenness while on the other hand, adult prey may be invulnerable to attack due to their tricky manoeuvring abilities to escape from the predators. In this study, we propose a stage-structured predator–prey model, in which predators attack only the bachelor herds of juvenile prey while adult prey save themselves due to small predator–prey size ratio and their fleeing capability, enabling them to avoid confrontation with the predators. Local and global stability analysis on the equilibrium points of the model are performed. Sufficient conditions for uniform permanence and the impermanence are derived. The model exhibits both transcritical as well as Hopf bifurcations and the corresponding numerical simulations are carried out to support the analytical results. Bachelor herding of juvenile prey as well as inaccessibility of adult prey restricts the uncontrolled predation so that prey abundance and predation remain balanced. This investigation on bachelor group defence brings out some unpredictable results, especially close to the zero steady state. Altogether, bachelor herding of the juvenile prey, which causes unconventional behavior near the origin, plays a significant role in establishing uniform permanence conditions, also increases richness of the dynamics in numerical simulations using the bifurcation theory and thereby, shapes ecosystem properties tremendously and may have a large influence on the ecosystem functioning.


2021 ◽  
Vol 33 (6) ◽  
pp. 0-0

Understanding how herd behavior occurs in the information systems context is important because such behavior influences many choice decisions, is the reason for some decision anomalies, and explains the reasons behind the rise or collapse of technology trends. Perceived uncertainty is a critical factor that triggers herding, but despite its influential role, prior research has not adequately investigated this broad concept. This research contributes to the literature by decomposing perceived uncertainty to its dimensions and analyzing the influence of each one on triggering individuals’ herd behavior. Our findings show that unlike state uncertainty, only effect and response uncertainty are the triggers herd behavior.


2021 ◽  
Vol 33 (6) ◽  
pp. 1-19
Author(s):  
Ali Vedadi ◽  
Timothy H. Greer

Understanding how herd behavior occurs in the information systems context is important because such behavior influences many choice decisions, is the reason for some decision anomalies, and explains the reasons behind the rise or collapse of technology trends. Perceived uncertainty is a critical factor that triggers herding, but despite its influential role, prior research has not adequately investigated this broad concept. This research contributes to the literature by decomposing perceived uncertainty to its dimensions and analyzing the influence of each one on triggering individuals’ herd behavior. Our findings show that unlike state uncertainty, only effect and response uncertainty are the triggers herd behavior.


Author(s):  
Yong Shi ◽  
Yuanchun Zheng ◽  
Kun Guo ◽  
Xinyue Ren

Herding has a great impact on stock market fluctuations, and it is possible for researchers to analyze the herding effect due to the recent popularity of mobile Internet and the development of big data analysis technology. In this paper, we propose both investor-based and stock-based sentiment propagation networks of Chinese stock markets based on the simple pairwise correlation of posts’ sentiment indexes. And the relationship between the herding effect and Chinese stock market fluctuations is studied by comparing the network indicators with the Shanghai Securities Composite Index (SSCI) and the Causeway International Value Index (CIVIX). Through the experimental results, we find that the indicators are indeed ahead of the Chinese stock market. This study is the first attempt to model stock market sentiment by using a complex network, and it proves that investor behavior has a great effect on the stock market.


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