In the capital market, people suffer from many biases, which causes the emergence of irrational behaviors. For this reason, people are often exposed to a significant level of risk; to get rid of it, they behave like others. Based on this, the aim of this research is to answer the question "Does the loss aversion scam have a significant effect on the formation of herding behavior among the investors of the Tehran Stock Exchange?". To achieve this goal, an online analytical questionnaire with 411 investors was designed to collect primary data, and multiple regression analysis was used to analyze and interpret the data. The findings of this research show that loss aversion has a positive and significant effect on herding behavior and one of the reasons for such behavior among investors is that people who are loss averse always try to model herding behavior, assuming that others feel the same way as themselves. On the other hand, the results showed that those who rely on their own knowledge also show herding behavior, especially when people learn about the knowledge of others, they rely more on their perceived knowledge, and herding behavior gains strength. Another result indicates that investors who see their performance as high have the same performance as others and follow the behavior of others in the market.
Amareh, R. (2023). The effect of loss aversion on the herding behavior of Investors. Novel Explorations in Computational Science and Behavioral Management, 1(1), 33-44. doi: 10.22034/necsbm.2023.171765
MLA
Ruhollah Amareh. "The effect of loss aversion on the herding behavior of Investors". Novel Explorations in Computational Science and Behavioral Management, 1, 1, 2023, 33-44. doi: 10.22034/necsbm.2023.171765
HARVARD
Amareh, R. (2023). 'The effect of loss aversion on the herding behavior of Investors', Novel Explorations in Computational Science and Behavioral Management, 1(1), pp. 33-44. doi: 10.22034/necsbm.2023.171765
VANCOUVER
Amareh, R. The effect of loss aversion on the herding behavior of Investors. Novel Explorations in Computational Science and Behavioral Management, 2023; 1(1): 33-44. doi: 10.22034/necsbm.2023.171765
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