European stock market dynamics: implications of overconfidence and the disposition effect for turnover

Moatemri Ouarda and Abdelfeteh El Bori

International Journal of Behavioural Accounting and Finance, 2014, vol. 4, issue 2, 133-152

Abstract: The main objective of this study is to determine whether the effects of overconfidence bias and the disposition effect continue to exist on the European market. This implies focusing more on the analysis of the possible implications of these biases on the transaction volume. The effects of overconfidence bias will be examined in the interaction of stock returns and trading volume. Vector autoregression (VAR) models and impulse-response functions are used to detect the effects of overconfidence bias. However, distinguishing the effects of overconfidence from the disposition effect is achieved in a more robust dynamic panel data approach. The main rediscovery of our analysis shows that excessive trading volume is attributed to the overconfidence bias more than to the disposition effect. Nevertheless, the effects of these two anomalies on the movements of trading volume are different depending on whether a situation is characterised by a high or low volatility.

Keywords: turnover; overconfidence; disposition effect; volatility; panel data approach; European stock markets; stock market dynamics. (search for similar items in EconPapers)
Date: 2014
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.inderscience.com/link.php?id=61436 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ids:ijbeaf:v:4:y:2014:i:2:p:133-152

Access Statistics for this article

More articles in International Journal of Behavioural Accounting and Finance from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().