Description

• Title:
  INVESTORS’ OVERCONFIDENCE IN THE STOCK MARKET
• Creator:
  Rajasekharan Ganesh;
  S. Thiyagarajan;
  Gopala Vasudevan;
  G. Naresh
• Description:
  An investor would normally depend on technical or/and fundamental analysis to make his/her investment decision in the secondary market. But in most cases the investor may not have time to do these analyses, understand the market or stock and then make the decision, therefore, they often end up taking irrational decisions. In some cases, the investors take these irrational decisions on the basis of the overconfidence they have concerning the information they possess. These investors are termed to bear overconfidence bias. The study aims to examine the influence of overconfidence bias in the Indian stock market. The study employed Vector Autoregression (VAR) methodology and impulse response function to know how long the bias persists in the market once the overconfidence bias is influenced by the investor. The results of the study show enough evidence to point out the influence of overconfidence bias in the market and it persists for more than 110 days. The study also finds out Efficient Market Hypothesis does not hold good. Our study period includes the time period since globalization of the Indian stock market and it also covers several periods of stress including the global financial crisis of 2007–08 and COVID-19 period.
• Object availability:
  free access
• Rights: https://creativecommons.org/licenses/by-nd/4.0
• Date:
  2023.07.19
• Type:
  info:eu-repo/semantics/article;
  info:eu-repo/semantics/publishedVersion
• Language:
  English
• Source:
  Copernican Journal of Finance & Accounting; Vol. 11 No. 4 (2022); 107-123;
  Copernican Journal of Finance & Accounting; Tom 11 Nr 4 (2022); 107-123;
  2300-3065;
  2300-1240
• Format:
  application/pdf
• Publisher:
  Uniwersytet Mikołaja Kopernika w Toruniu
• Relation: https://apcz.umk.pl/CJFA/article/view/45188/36087
• Subject:
  Overconfidence Bias;
  Mental Shortcuts;
  Vector autoregression (VAR);
  Impulse Response Function;
  Nifty 50 Index
• Identifier: https://apcz.umk.pl/CJFA/article/view/45188
• Data provider:
  Akademicka Platforma Czasopism
• Can I use it?:
  check license
• Type:
  text