The effect of time series properties on the predictive value of quarterly earnings for forecasting annual earnings.

Persistent Link:
http://hdl.handle.net/10150/185262
Title:
The effect of time series properties on the predictive value of quarterly earnings for forecasting annual earnings.
Author:
Lee, Kyung Joo
Issue Date:
1990
Publisher:
The University of Arizona.
Rights:
Copyright © 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.
Abstract:
This study provides further evidence regarding the predictive value of quarterly earnings for improving the forecasts of annual earnings. Using an analytical model, it is shown that for a specific class of time-series models, the predictive values are determined by the time-series properties, as measured by parameter value, of quarterly earnings. In particular, the model demonstrates that the accuracy of annual earnings forecasts increases as additional quarterly reports become available, and that the time-series model parameter value is positively related to both total improvement and the first quarter's relative improvement in annual earnings forecasts. These theoretical predictions are empirically tested using a sample of 235 firms over a five year period from 1980 to 1984. Empirical results are consistent with the theoretical predictions. First, annual earnings forecasts become increasingly accurate as additional quarterly reports are available, suggesting that quarterly earnings are useful for improving the forecasts of annual earnings. Second, there are cross-sectional variations in the degree of the improved accuracy in forecasts. More importantly, time-series properties (parameter value) of quarterly earnings are an important determinant of the variations in both total and relative predictive values. This result is robust with respect to different time-series models, forecast error metrics, and statistical methods.
Type:
text; Dissertation-Reproduction (electronic)
Keywords:
Business administration.
Degree Name:
Ph.D.
Degree Level:
doctoral
Degree Program:
Business Administration; Graduate College
Degree Grantor:
University of Arizona
Advisor:
Dhaliwal, Dan S.

Full metadata record

DC FieldValue Language
dc.language.isoenen_US
dc.titleThe effect of time series properties on the predictive value of quarterly earnings for forecasting annual earnings.en_US
dc.creatorLee, Kyung Jooen_US
dc.contributor.authorLee, Kyung Jooen_US
dc.date.issued1990en_US
dc.publisherThe University of Arizona.en_US
dc.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.en_US
dc.description.abstractThis study provides further evidence regarding the predictive value of quarterly earnings for improving the forecasts of annual earnings. Using an analytical model, it is shown that for a specific class of time-series models, the predictive values are determined by the time-series properties, as measured by parameter value, of quarterly earnings. In particular, the model demonstrates that the accuracy of annual earnings forecasts increases as additional quarterly reports become available, and that the time-series model parameter value is positively related to both total improvement and the first quarter's relative improvement in annual earnings forecasts. These theoretical predictions are empirically tested using a sample of 235 firms over a five year period from 1980 to 1984. Empirical results are consistent with the theoretical predictions. First, annual earnings forecasts become increasingly accurate as additional quarterly reports are available, suggesting that quarterly earnings are useful for improving the forecasts of annual earnings. Second, there are cross-sectional variations in the degree of the improved accuracy in forecasts. More importantly, time-series properties (parameter value) of quarterly earnings are an important determinant of the variations in both total and relative predictive values. This result is robust with respect to different time-series models, forecast error metrics, and statistical methods.en_US
dc.typetexten_US
dc.typeDissertation-Reproduction (electronic)en_US
dc.subjectBusiness administration.en_US
thesis.degree.namePh.D.en_US
thesis.degree.leveldoctoralen_US
thesis.degree.disciplineBusiness Administrationen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.grantorUniversity of Arizonaen_US
dc.contributor.advisorDhaliwal, Dan S.en_US
dc.contributor.committeememberKroner, Kenneth F.en_US
dc.contributor.committeememberAtkins, Allen B.en_US
dc.identifier.proquest9111947en_US
dc.identifier.oclc709914861en_US
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