The Changing Nature of Trading Volume Reactions to Earnings Announcements: Further Evidence of an Increase in Pre-Announcement Private Information

2008 ◽  
Author(s):  
Richard A. Schneible Jr. ◽  
Douglas E. Stevens
Keyword(s):  
Private Information ◽  
Trading Volume ◽  
Earnings Announcements

Evidence That Investors Trade on Private Event-Period Information around Earnings Announcements

2005 ◽  
Vol 80 (2) ◽  
pp. 403-421 ◽  
Author(s):  
Orie E. Barron ◽  
David G. Harris ◽  
Mary Stanford
Keyword(s):  
Empirical Evidence ◽  
Private Information ◽  
Trading Volume ◽  
Earnings Announcements ◽  
Theoretical Models ◽  
Earnings Announcement

Holthausen and Verrecchia's (1990) and Kim and Verrecchia's (1997) theoretical models predict that private information inferred at the time of an earnings announcement (private event-period information) is associated with greater trading volume. We provide empirical evidence consistent with these theories. Specifically, announcements that increase analysts' private information (as measured by Barron et al.'s [1998] empirical proxies) are associated with increased trading volume, consistent with some investors similarly acquiring private event-period information. In addition, announcements that decrease analysts' consensus are associated with more trading volume. Because consensus declines when private information increases, this finding provides reinforcing evidence that investors trade following earnings announcements because of private information that becomes useful only in conjunction with the information in the announcement and that this information is important enough to spur trading.

Investors overconfidence behaviour at Bombay Stock Exchange

2018 ◽  
Vol 14 (5) ◽  
pp. 613-632 ◽  
Author(s):  
Venkata Narasimha Chary Mushinada ◽  
Venkata Subrahmanya Sarma Veluri
Keyword(s):  
Private Information ◽  
Trading Volume ◽  
Negative Impact ◽  
Stock Exchange ◽  
Public Information ◽  
Response Analysis ◽  
Content Type ◽  
Daily Data ◽  
Level Data ◽  
Attribution Bias

PurposeThe purpose of the paper is to empirically test the overconfidence hypothesis at Bombay Stock Exchange (BSE).Design/methodology/approachThe study applies bivariate vector autoregression to perform the impulse-response analysis and EGARCH models to understand whether there is self-attribution bias and overconfidence behavior among the investors.FindingsThe study shows the empirical evidence in support of overconfidence hypothesis. The results show that the overconfident investors overreact to private information and underreact to the public information. Based on EGARCH specifications, it is observed that self-attribution bias, conditioned by right forecasts, increases investors’ overconfidence and the trading volume. Finally, the analysis of the relation between return volatility and trading volume shows that the excessive trading of overconfident investors makes a contribution to the observed excessive volatility.Research limitations/implicationsThe study focused on self-attribution and overconfidence biases using monthly data. Further studies can be encouraged to test the proposed hypotheses on daily data and also other behavioral biases.Practical implicationsInsights from the study suggest that the investors should perform a post-analysis of each investment so that they become aware of past behavioral mistakes and stop continuing the same. This might help investors to minimize the negative impact of self-attribution and overconfidence on their expected utility.Originality/valueTo the best of the authors’ knowledge, this is the first study to examine the investors’ overconfidence behavior at market-level data in BSE, India.

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