Economics & Sociology

ISSN: 2071-789X eISSN: 2306-3459 DOI: 10.14254/2071-789X
Index PUBMS: f5512f57-a601-11e7-8f0e-080027f4daa0
Article information
Title: Predictive power of aggregate corporate earnings and their components for future GDP growths: An international comparison
Issue: Vol. 12, No 1, 2019
Published date: 03-2019 (print) / 03-2019 (online)
Journal: Economics & Sociology
ISSN: 2071-789X, eISSN: 2306-3459
Authors: Sumiyana Sumiyana
University of Gadjah Mada

Sari Atmini
University of Gadjah Mada

Slamet Sugiri
University of Gadjah Mada
Keywords: aggregate earnings, earnings components, future GDP growths, international settings
DOI: 10.14254/2071-789X.2019/12-1/7
Index PUBMS: efbed532-5d4b-11e9-8b68-fa163e6feac6
Language: English
Pages: 125-142 (18)
JEL classification: M21, O16, P44
Website: https://www.economics-sociology.eu/?645,en_predictive-power-of-aggregate-corporate-earnings-and-their-components-for-future-gdp-growths-an-international-comparison
Licenses:
We are grateful to the Master and Doctoral Program, Faculty of Economics and Business, University of Gadjah Mada (UGM). The Public and Social Fund Item, Faculty of Economics and Business, University of Gadjah Mada supported and financed all research activities up to its reporting.
Abstract

This study investigates the predictive power of aggregate corporate earnings and their four components for future GDP growths. It splits aggregate earnings into operating and non-operating incomes as they have different degrees of permanence. It also splits aggregate earnings into operating cash flows and accruals since earnings management affects them distinctively. This study finds aggregate earnings, operating income, operating cash flows, and accruals as predictors for one- and two-years-ahead GDP growths. However, it does not find such predictive power for aggregate non-operating income. Furthermore, this study splits the research sample based on macroeconomic development level and documents how aggregate earnings have predictive power over the longer horizon in developed countries, while aggregate non-operating income is a good predictor only in developing countries. Meanwhile, when splitting the sample based on earnings quality degree, this study demonstrates that the predictive power of aggregate accruals in a high earnings quality subsample is higher than in the low one. In the context of high (low) earnings quality, the predictive power of aggregate accruals is higher (lower) than that of operating cash flows. Overall, besides supporting previous studies’ findings, this study also discovers that corporate earnings components are excellent predictors for future GDP growths.

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