Herding Behavior

The purpose of the present study is to investigate the effect of behavioral variables on overconfidence in management, herding behavior and investors' emotional tendency on stock return. To this end, by using the data of firms listed in the Iran's Stock Exchange during the seven-year period of 2010-2016, overconfidence index in management, the herding behavior of the investors and the emotional tendency of the investors were calculated and their impact on stock return was examined. The data of the research are of a panel type and for analyzing the data and testing the hypotheses; a multiple linear regression model has been used. Evidence from the experimental results of the research showed that the behavioral variables studied in the research has a significant and inverse effect on the stock return of the companies.

The stock market is influenced by other parallel markets and macroeconomic variables; such variables have impacts on the behavior of financial market players. Accordingly, this study first examines the existence of herding behavior among investors in the Tehran Stock Exchange (TSE). It then investigates the effects of exogenous economic variables on this industry-level behavior in the Tehran Stock Exchange (TSE) throughout 2008- 2018. For this purpose, 12 different industries were studied, and the results of nonlinear regression analysis showed the existence of herding behavior in all selected industries in the bearish market. However, this is not true for the bullish market. The findings also indicate that the effects of exogenous variables are not the same in different industries.

Although theoretical and empirical literature regarding the stylized facts shows evidence of their correlations to herding behavior in financial markets, the causes of such phenomena are still unknown. Using an agent-based model strengthened by the competition co-evolution algorithm (STGP) technique, this study provides laboratory evidence on capital market dynamics and analyses the behavioral foundations of stylized facts such as fat tails, leverage effects, and volatility clustering. The simulated stock markets consist of two groups; the “Best agents”, which are a small portion of artificial agents, and the “Residual agents”, which are the main group of artificial agents. The best performance in terms of breeding fitness returns is the main feature of the “Best agents”. More, the size of the “Best Agents” group is specified as 2.5%, 5%, 10% &20% of the total population size. An agent-based model consists of two portions, a two thousand population of trader agents that each has its decision-making strategy, and a virtual market that creates the trading strategies. Then the model evolved step by step using a feed with real quotes of the financial instruments by Adaptive Modeler. A training period is considered 2500 bars (started in November 2003), and the test period started in December 2013. The observation shows that the herding behavior in the price series created by the “Residual agents” is less than the “Best agents” series. Therefore, the greater diversity of trade strategies as the genetic differences of artificial agents leads to less herding. The observations exhibit that the volatility clustering, leverage effects, and nonlinear dependence are more likely to experience in the price series generated by “Best gents”. Furthermore, observations indicate that if the population is well diversified in terms of trading strategies, the efficiency of the market increases.

Due to overlap between stock markets and financial markets, this study was an attempt to examine the herding behavior in the Iranian stock market and the crude oil, foreign exchange and gold markets. For this purpose, in this research, monthly data between 2011 and 2020 for Tehran Stock Foreign exchange were used. The results of the study based on two criteria explaining herding behavior indicate the existence of herding behavior of the stock market and crude oil, gold and foreign exchange markets. The results also show that it has had different ef-fects on herding behavior in different periods. This issue has also been different in increasing and decreasing market periods. Therefore, gold is introduced as an important asset that influences herding behavior. Also, during the decreasing period of the stock market, herding behavior is not affected by the exchange and crude oil market, and in this period, the behavior of investors and investment risks in the stock market can be predicted without considering the exchange and crude oil market.