Abstract
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.
Highlights
In the past 20 years, the research on herding investment behavior has grown exponentially
The results indicate very weak to almost nonexistent evidence of herding behavior in the Zagreb Stock Exchange (ZSE)
If we focus on markets similar to Croatia and research which employs quantile regression (QR) methodology in observing herding behavior, a few conclusions can be drawn
Summary
In the past 20 years, the research on herding investment behavior has grown exponentially. The studies of Christie and Huang (1995) and Chang et al (2000) are probably among the most cited papers with empirical research measuring herding behavior as well as estimating its variability under different stock market conditions. The most common approach to empirically evaluating herding behavior in a stock market is ordinary least squares (OLS) estimation using one of several popular models. OLS estimation is not always the best method of estimating a financial model due to the strong variable distribution assumptions that OLS requires, and because OLS focuses only on the conditional mean. A natural extension to OLS estimation is quantile regression (QR), a semi-parametric method of estimation which evaluates the whole distribution of the dependent variable ( the mean). Its popularity has increased for finance applications in the last couple of years because it deals efficiently with characteristics of financial data
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