Differences in Commercial Database Reported Earnings: Implications for Empirical Research
Jeffery S. Abarbanell
University of North Carolina (UNC) at Chapel Hill - Finance Area
University of Michigan, Stephen M. Ross School of Business
Prominent properties of distributions of differences in earnings reported by forecast data providers (FDPs), i.e., I/B/E/S, Zacks, and First Call, and Compustat drive statistical inferences drawn in extant research concerning the relative information content and value relevance of alternative reported earnings numbers (e.g., "Street" or pro forma versus GAAP earnings). These properties include, 1) the existence of an extreme negative tail in such distributions (representing cases in which Compustat earnings is below FDPs' earnings by extreme amounts), 2) a higher frequency of cases in which Compustat earnings exceed FDP earnings by small amounts than cases in which FDP earnings exceed Compustat earnings by small amounts accompanied by a high concentration of zero earnings differences, 3) systematic changes in the shape of such distributions over time attributable to the application of stable formulae for excluding items from reported earnings by the FDPs while recognition of these items by firms in the cross-section changes. Relying on knowledge of these properties we show that many statistical inferences and interpretations concerning market reliance/fixation on FDP (Street or pro forma) earnings versus Compustat (GAAP) earnings in the cross section and over time are driven by a small number of extreme negative tail observations and a regime shift in the mean earnings differences in 1990, respectively. These properties have similar impacts on inferences in the value relevance literature. Our findings highlight the value of understanding the properties of distributions of earnings differences and the composition of earnings related to these properties for identifying potential factors that can confound inferences, and for uncovering evidence that generates new lines of investigation and improves test designs.
Number of Pages in PDF File: 51
JEL Classification: G14, G29, M41
Date posted: November 1, 2000
© 2016 Social Science Electronic Publishing, Inc. All Rights Reserved.
This page was processed by apollobot1 in 0.250 seconds