Abstract
Abstract Traditional empirical models analyze impact of sentiments on financial market volatility using macroeconomic fundamentals or financial indicators. In this paper, recent methods of text based sentiment analysis of market from relevant news articles regarding economy and financial market are used. Two distinct market sentiments namely, positive and negative sentiments are constructed using different emotions which are identified through standard natural language processing methods. Further, the paper aims at proposing an augmented version of asymmetric GARCH model of conditional volatility for Indian stock exchange, Sensex, during the time period of April 19, 2007 to January 10, 2020 by incorporating aforementioned market sentiments. Empirical findings suggest dominant impact of negative market sentiment over positive one and it also provides evidence of noise trading in financially immature Indian stock market.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
