Title

News Content, Investor Misreaction, and Stock Return Predictability

Date of Award

August 2016

Degree Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Finance

Advisor(s)

David Weinbaum

Keywords

Investor Misreaction, News, Stock Predictability, Textual Analysis

Subject Categories

Business

Abstract

Using a large dataset of news releases, we study instances of investors’ mistaken

reaction, or misreaction, to news. We define misreaction as stock prices moving in the

direction opposite to the news when it is released. We find that news tone predicts returns

in the cross-section only upon the occurrence of misreaction. Stocks that are larger, more

liquid, more visible, and more covered, by analysts or by the media, are less likely to

exhibit misreaction. On the other hand, the ambiguity and complexity of news content,

and variables that proxy for investor distraction, are all associated with more misreaction

and greater predictability.

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