Analysis of herding behavior using bayesian quantile regression

• Main Authors: Rungrapee Phadkantha, Woraphon Yamaka, Songsak Sriboonchitta
• Format: Book Series
• Published: 2019
• Subjects: Computer Science
• Online Access: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85065606882&origin=inward; http://cmuir.cmu.ac.th/jspui/handle/6653943832/65533
• Institution: Chiang Mai University

© Springer Nature Switzerland AG 2019. The purpose of this paper is to analyse the herding behavior of eight stock sectors of the Stock Exchange of Thailand consisting of Banking (BANK), Commerce (COM), Communications (COMUN), Energy and Utilities (ENERG), Food and Beverage (FOOD), Personal Products and Pharmaceuticals (PERS), Petrochemicals and Chemicals (PETRO), and Property Development (PROP), for the period from January of 2014 to May of 2018. Conventionally, the model used for finding the herding behavior is linear regression, which is based on the mean of the distribution. However, the result obtained from the linear mean regression model may not be consistent with the heterogeneity of investor behavior. Thus, in this study, we employ Bayesian quantile regression to empirically estimate the daily stock returns. The innovation of this paper is to examine the data conditional on different quantiles and test the behavioral relation between stock returns and market movements with different quantile distributions. The results show that five out of the eight sectors have herding behavior at quantile 0.25 and the remaining three sectors have no herding behavior.