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Asymmetric Effect of Investors Sentiments on Herding Behavior and Stock Returns: Pre and Post Covid-19 Analysis
Montenegrin Journal of Economics ; 19(1):43-55, 2023.
Article in English | Scopus | ID: covidwho-2238926
ABSTRACT
The study investigates the asymmetric effect of investors sentiments on herding behavior and stock returns of S&P 500 markets during pre and post covid 19. We analyze daily data from May 15, 2000 (Pre Covid) to 20 Feb 2020 and form 20 Feb to –13 May, 2022 (Post Covid). We conduct Modified multiple regression Analysis by introducing investors sentiments proxy i.e., trading volume into the Chang et al., (2000) herding model named as cross-sectional absolute deviation along with Vector Autoregressive Regression and Granger Causality tests. We establish that trading volume increases herding asymmet-ric. Post COVID-19 has significant negative effects on herding behaviour. The findings illustrate that COVID-19 increased herding behavior in S&P 500 markets and became more intensified during COVID-19, which contributes to ac-centuate and elongate it. The study also documents significant positive effect of investor sentiment on stock returns, whereas COVID-19 has negative effect on S&P 500 stock returns. We propose that investor sentiments may present extrapolative or predictive feature of herding behaviour. The study will be ben-eficial to shape an understanding of different dynamics associated with portfolio and market in-efficiency, trading strategies as well as risk management perspective. © 2023, Economic Laboratory for Transition Research. All rights reserved.
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Full text: Available Collection: Databases of international organizations Database: Scopus Type of study: Experimental Studies Topics: Long Covid Language: English Journal: Montenegrin Journal of Economics Year: 2023 Document Type: Article

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Full text: Available Collection: Databases of international organizations Database: Scopus Type of study: Experimental Studies Topics: Long Covid Language: English Journal: Montenegrin Journal of Economics Year: 2023 Document Type: Article