Overconfidence bias in the Indian stock market in diverse market situations: an empirical study

Jitender Kumar () and Neha Prince ()
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Jitender Kumar: Deenbandhu Chhotu Ram University of Science and Technology, Murthal
Neha Prince: Deenbandhu Chhotu Ram University of Science and Technology, Murthal

International Journal of System Assurance Engineering and Management, 2022, vol. 13, issue 6, No 18, 3047 pages

Abstract: Abstract Market situations affect investors’ sentiments. Sudden and sharp changes in the market may make investors adjust their beliefs and change their viewpoints. To quote, such sharp changes include the stock market crash of 2008 and the COVID-19 pandemic, which significantly impacted investors' emotions. In this direction, this study attempts to find investors’ overconfidence bias in different market situations, i.e., precrash period (2006–2008), crash period (2008–2010), and post-crash period (2010–2015, 2015–2020, 2020–2021). We have obtained secondary data for this study from the BSE 100 index (as a proxy of the Indian stock market). Econometric techniques used for data analysis include Vector Auto Regression (VAR), Granger causality VAR/Block Exogeneity Wald Test, and Impulse Response Function (IRF). The study finds that the investors were overconfident during pre-crash periods, i.e., before the global market crash of 2008 and the duration between 2015 and March 2020. In postcrash periods, i.e., during 2008–2010, 2010–2015, and 2020–2021, investors were not overconfident.

Keywords: Behavioral finance; Granger causality; Investor overconfidence; Impulse Response Function (IRF); Turnover; Vector Auto Regression (VAR) (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s13198-022-01792-1

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