Redundant information and predictable stock price returns / Michael Philip Carniol.

Format:

Book

Manuscript

Thesis/Dissertation

Author/Creator:

Carniol, Michael Philip, author.

Contributor:

Schrand, Catherine M., degree supervisor.

Bushee, Brian, degree committee member.

Fischer, Paul E., degree committee member.

Taylor, Daniel, degree committee member.

University of Pennsylvania. Department of Accounting, degree granting institution.

Language:

English

Subjects (All):

Penn dissertations--Accounting.

Accounting--Penn dissertations.

Local Subjects:

Penn dissertations--Accounting.

Accounting--Penn dissertations.

Physical Description:

vii, 148 leaves : illustrations ; 29 cm

Production:

[Philadelphia, Pennsylvania] : University of Pennsylvania, 2017.

Summary:

How well do investors distinguish information that already is priced from genuinely novel and exclusive private information? This paper examines whether investors misweight information that already is in stock prices ("redundant information") in making their trading decisions, and whether this misweighting is associated with investors' information processing frictions or behavioral biases. I extend the Kyle (1985) model to allow for non-Bayesian updating and transaction costs. The model predicts that price changes exhibit a state space process, in which the parameter for investors' non-Bayesian weighting of redundant information is estimable distinctly from information asymmetry and transaction costs. Using this model, I estimate a firm-quarter measure of investors' misweighting of redundant information. I find that, on average, investors behave as if the information content in the immediately prior price change is private information. This overweighting of redundant information appears higher when investors have less time to process information, stock prices are less informative, and industry-wide information is less costly to obtain. Overall, these results suggest one way that information processing frictions contribute to momentum and mean reversion in stock price returns.

Notes:

Ph. D. University of Pennsylvania 2017.

Department: Accounting.

Supervisor: Catherine M. Schrand.

Includes bibliographical references.

OCLC:

1312241171