Document Type : Article-Based Dissertations


1 Associate Professor of Finance, Department of Finance and Insurance, Faculty of Management and Accounting, Shahid Beheshti University, Tehran, Iran .

2 Visiting Professor of Finance, Department of Finance and Insurance, Faculty of Management and Accounting, Shahid Beheshti University, Tehran, Iran.

3 Professor of Business, Department of Business, Faculty of Management and Accounting, Shahid Beheshti University, Tehran, Iran.

4 PhD candidate in Finance, department of finance, faculty of management and accounting, Shahid Beheshti University, Tehran, Iran.


Although herding behavior is mainly defined on the basis of imitation and repetition in existing theories, it is difficult to provide a mathematical model which is able to identify this phenomenon.
Therefore, in this paper, using the Monte Carlo method and stock price data of Tehran Stock Exchange and OTC companies, during the years 2011 to 2019, the herding behavior among the sample companies is investigated. Given that the Iranian capital market is facing the phenomenon of closure, and this can affect the values ​​of price herding, the results are examined with the New York Stock Exchange as a market developed.
The first finding indicates the presence of herding behavior in 29.6% of possible cases in the sample. The second finding indicates the presence of herding at an average of 4.07%. The third finding reflects the increase in the amount of herding along with the increase in absolute return, which shows that as the stock prices change, the values of herding also increases.
Also, the results show that the herding behaves almost symmetrically with increasing the absolute amount of stock returns, the amount of herding behavior is first decreasing and then increasing. Accordingly, when price change are slight, the amount of herding is small; But with drastic increases, the average herding behavior also becomes positive and upwards, reaching 16%. This means that as the price of one stock rises, the prices of other stocks also tend to rise, and the sharper the price increase, the greater the amount of imitation of price behavior. A similar trend is observed when prices fall. As prices fall and negative returns intensify, the average rate of herding behavior also increases, and the higher the decline in prices, the higher the rate of herding behavior. As a result, the higher the absolute amount of price return, the greater the amount of herding behavior.
The fourth finding of the study indicates the possibility of a relationship between the herding and trading volume. To examine this relationship in the face of a sharp increase in the volume and number of trades, price data are classified into 20 groups based on the number and volume of trades. Then the amount of herding behavior of each group was calculated. The results show that the relationship between both indices of trading and herding is positive and significant and with a sharp increase in the number or volume of trades, herding measure approaches its maximum value. Similar results on the New York Stock Exchange are described below.
Evidence of herding behavior in the New York Stock Exchange also shows that this phenomenon occurs almost twice as much as in the Iranian capital market. Faster and more coordinated dissemination of news and faster reactions to them in NYSE can be the main reasons for this. These results are consistent with a study by Hwang and Salmon (2004) in which the amount of herding behavior in the US market was higher than the South Korean stock exchange. It seems that due to the less trading halts in NYSE, the significant values ​​of price herding in that market mean that the values ​​obtained for the Tehran market are probably not affected by the trading halts and the results can be reliable.


Main Subjects

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