Herding Behavior from Loss Aversion Effect in the Stock Exchange of Thailand

2020 ◽  
pp. 317-332
Author(s):  
Kunsuda Nimanussornkul ◽  
Chaiwat Nimanussornkul
Keyword(s):  
Loss Aversion ◽  
Stock Exchange ◽  
Herding Behavior ◽  
Aversion Effect ◽  
Stock Exchange Of Thailand

Herd Behaviour: How Decisive is the Noise in the NSE and BSE Stock Markets?

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Paritosh Chandra Sinha
Keyword(s):  
Behavioral Finance ◽  
Stock Markets ◽  
High Frequency ◽  
Stock Exchange ◽  
Time Stamp ◽  
Trade Volume ◽  
Herding Behavior ◽  
Equilibrium Pricing ◽  
Indian Context ◽  
Herd Behaviour

Do investors in the stock markets act/react on true information or noise? Do they believe on their own information or simply herd? The study seeks to explore these typical research queries from the behavioral finance perspectives. In particular, it develops a new theory of herding behavior and extends the models of Banerjee (1992) and Bikhchandani, Hirshleifer, and Welch (1992). The study also empirically tests the same on the Indian context with the high frequency intraday trading data for the real trade-time or time-stamp, trade-volume, and trade-price of ten sample scripts listed for their trading in both markets - the Bombay Stock Exchange (BSE) and the National stock Exchange (NSE). The study contributes to the literature with original findings. It shows that investors in the two Indian stock markets show crowd of positive and negative herding as well significantly and there is huge noise along with information in the markets equilibrium pricing mechanism.

scholarly journals Revisiting Herding Investment Behavior on the Zagreb Stock Exchange: A Quantile Regression Approach

2018 ◽  
Vol 3 (2) ◽  
pp. 119-162 ◽  
Author(s):  
Tihana Škrinjarić
Keyword(s):  
Quantile Regression ◽  
Stock Exchange ◽  
Political Crisis ◽  
Investment Behavior ◽  
Investment Strategies ◽  
Herding Behavior ◽  
Policy Makers ◽  
Daily Data ◽  
Regression Approach ◽  
Behavior Models

Herding investment behavior on stock markets has consequences for practitioners, theorists, and policy makers. Thus, empirical research on this topic in the last couple of years has grown exponentially. However, there exist only a few papers dealing with herding behavior that consider the Croatian stock market. This study employs the quantile regression approach of estimating several herding investor behavior models of this market for the first time in the literature. Based upon daily data for the 37 most liquid stocks in the Zagreb Stock Exchange (ZSE) for the period September 22, 2014 to May 8, 2018, several model specifications are determined using quantile regression. Because the quantile regression approach deals with specific characteristics of financial data (stylized facts) better than the OLS method, more robust results can be achieved for evaluating if herding behavior is present in the Croatian market. The results indicate very weak to almost nonexistent evidence of herding behavior in the ZSE. Moreover, market volatility does not have any effect on herding behavior. Finally, the economic and political crisis (regarding concern Agrokor) in 2017 was controlled for in the model and the crisis was found insignificant. It seems that herding behavior does not need to be taken into account when tailoring investment strategies on the ZSE.

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