Event study with imperfect competition and private information: earnings announcements revisited

Event study with imperfect competition and private information: earnings announcements revisited Although prior research documents that prices respond to earnings announcements, only a little of the price variation is explained by these announcements. To further investigate the properties of the information environment around these announcements we use NYSE TAQ data and compute the maximum likelihood estimates (MLEs) of the primitive parameters of a Kyle (Econometrica 53(6):1315–1336, 1985) type model within and around earnings announcement windows. These include the precision of fundamentals given only public information, the precision of private signals, and the variance of uninformed liquidity trading (noise). We find that liquidity noise is higher while the precision of beliefs given only public information is lower within an earnings announcement window. The precision of private information is higher in an event window, consistent with greater information acquisition to try and interpret a public announcement. We also document that Kyle’s λ is higher in an event window, showing an overall increase in information asymmetry. Our overall findings suggest that the earnings announcement window is distinguished from the preceding and subsequent windows not by being a period with more public information but as a period with different public information. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Review of Quantitative Finance and Accounting Springer Journals

Event study with imperfect competition and private information: earnings announcements revisited

Loading next page...
 
/lp/springer_journal/event-study-with-imperfect-competition-and-private-information-0dc355Kl91
Publisher
Springer US
Copyright
Copyright © 2009 by Springer Science+Business Media, LLC
Subject
Finance; Corporate Finance; Accounting/Auditing; Econometrics; Operation Research/Decision Theory
ISSN
0924-865X
eISSN
1573-7179
D.O.I.
10.1007/s11156-009-0136-9
Publisher site
See Article on Publisher Site

Abstract

Although prior research documents that prices respond to earnings announcements, only a little of the price variation is explained by these announcements. To further investigate the properties of the information environment around these announcements we use NYSE TAQ data and compute the maximum likelihood estimates (MLEs) of the primitive parameters of a Kyle (Econometrica 53(6):1315–1336, 1985) type model within and around earnings announcement windows. These include the precision of fundamentals given only public information, the precision of private signals, and the variance of uninformed liquidity trading (noise). We find that liquidity noise is higher while the precision of beliefs given only public information is lower within an earnings announcement window. The precision of private information is higher in an event window, consistent with greater information acquisition to try and interpret a public announcement. We also document that Kyle’s λ is higher in an event window, showing an overall increase in information asymmetry. Our overall findings suggest that the earnings announcement window is distinguished from the preceding and subsequent windows not by being a period with more public information but as a period with different public information.

Journal

Review of Quantitative Finance and AccountingSpringer Journals

Published: Jul 5, 2009

References

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 12 million articles from more than
10,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Unlimited reading

Read as many articles as you need. Full articles with original layout, charts and figures. Read online, from anywhere.

Stay up to date

Keep up with your field with Personalized Recommendations and Follow Journals to get automatic updates.

Organize your research

It’s easy to organize your research with our built-in tools.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

DeepDyve Freelancer

DeepDyve Pro

Price
FREE
$49/month

$360/year
Save searches from
Google Scholar,
PubMed
Create lists to
organize your research
Export lists, citations
Read DeepDyve articles
Abstract access only
Unlimited access to over
18 million full-text articles
Print
20 pages/month
PDF Discount
20% off