Title page for ETD etd-042399-112528

Type of Document Dissertation
Author Pritamani, Mahesh
Author's Email Address mahesh@vt.edu
URN etd-042399-112528
Title Return Predictability Conditional on the Characteristics of Information Signals
Degree PhD
Department Finance
Advisory Committee
Advisor Name Title
Singal, Vijay Committee Chair
Billingsley, Randall S. Committee Member
Kadlec, Gregory B. Committee Member
Keown, Arthur J. Committee Member
Kumar, Raman Committee Member
  • Information Quality
  • Return Predictability
  • Information Signals
Date of Defense 1999-04-12
Availability unrestricted
This dissertation examines whether simultaneously conditioning on the

multidimensional characteristics of information signals can help predict returns

that are of economic significance. We use large price changes, public

announcements, and large volume increases to proxy for the magnitude,

dissemination, and precision of information signals. Abnormal returns following

large price change events are found to be unimportant. As we condition on other

characteristics of information signals, the abnormal returns become large.

Large price change events accompanied by both a public announcement and an

increase in volume have a 20-day abnormal return of almost 2% for positive

events and -1.68% for negative events. The type of news provides further

refinement. If the news relates to earnings announcements, management earnings

forecasts, or analyst recommendations then the 20-day abnormal returns becomes

much larger: ranging from 3% to 4% for positive events and about -2.25% for

negative events. For these news events, we also find that the underreaction is

greater for positive (negative) event firms that underperformed (overperformed)

the market in the prior period, earning 20-day post-event abnormal returns of

4.85% (-3.50%). This evidence is consistent with the Barberis, Shleifer, and

Vishny (1998) model of investor sentiment that suggests that investors are slow

to change their beliefs. The evidence from our sample does not provide much

support for strategic trading models under information asymmetry. Finally, an

out-of-sample trading strategy generates 20-day post-event statistically

significant abnormal return of 2.18% for positive events and -2.40% for negative

events. Net of transaction costs, the abnormal returns are a statistically

significant 1.04% for positive events and a statistically significant -1.51% for

negative events.

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