Return to search

Differential earnings response coefficients to accounting information: The case of revisions of financial analysts' forecasts.

This dissertation extends previous studies on firms' differential earnings response coefficients. It provides further theoretical explanation and empirical evidence for the differential earnings response coefficients across firms and time. The empirical evidence found by Ball & Brown (1968) that the sign of unexpected earnings is positively correlated with the sign of market reactions is used to improve the control of measurement errors on investors' prior belief. Revisions of financial analysts' forecasts (FAFs) for firms' future earnings per share (EPS) are used as the event information. Both the impact of FAFs quality on investors' earnings belief revision and the mapping from EPS to security price are considered. Investors are assumed to be Bayesians who are homogeneous in belief. They use FAFs as information for making portfolio investment decisions. FAFs with smaller contemporary dispersion relative to the variance of investors' prior belief are considered to have higher quality. It is proposed that investors have stronger faith on the forecasts with higher information quality. A non-normative approach is used to map EPS into security prices. The market price over (expected) earnings ratio (P/E) is used as a linear approximation for the security valuation function. The major advantage of this approach is that non-earnings factors that have price effect on securities are implicitly controlled. The model predicts that ceteris paribus, the earnings response coefficient adjusted for the differential P/E is positively correlated with the quality of FAFs. Cross-sectional and time series samples of 1097 FAFs revisions from Standard & Poor's Earnings Forecaster in the years 1981 to 1985 are used in the empirical test. The empirical results are consistent with the theoretical implication. The quality of FAFs is found to be positively correlated with the P/E adjusted earnings response coefficient at one percent significance level. The results are robust across event day windows, the estimation periods for market model parameters and the price reaction measurements.

Identiferoai:union.ndltd.org:arizona.edu/oai:arizona.openrepository.com:10150/184712
Date January 1989
CreatorsGuo, Miin Hong.
ContributorsDhaliwal, Dan S., Salatka, William K., Pfeiffer, Glenn
PublisherThe University of Arizona.
Source SetsUniversity of Arizona
LanguageEnglish
Detected LanguageEnglish
Typetext, Dissertation-Reproduction (electronic)
RightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.

Page generated in 0.0019 seconds