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
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.

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.


1. Anilowski, C., Feng, M., & Skinner, D. J. (2007). Does Earnings Guidance Affect Market Returns? The Nature and Information Content of Aggregate Earnings Guidance. Journal of Accounting and Economics, 44(1-2), 36-63. doi:10.1016/j.jacceco.2006.09.002

2. Ball, R., Sadka, G., & Sadka, R. (2009). Aggregate Earnings and Asset Prices. Journal of Accounting Research, 47(5), 1097-1133.

3. Ball, R., & Shivakumar, L. (2005). Earnings Quality in UK Private Firms: Comparative Loss Recognition Timeliness. Journal of Accounting and Economics, 39(1), 83-128. doi:10.1016/j.jacceco.2004.04.001

4. Barth, M. E., Beaver, W. H., Hand, J. R., & Landsman, W. R. (1999). Accruals, Cash Flows, and Equity Values. Review of Accounting Studies, 4(3-4), 205-229.

5. Barth, M. E., Beaver, W. H., & Wolfson, M. A. (1990). Components of Earnings and the Structure of Bank Share Prices. Financial Analysts Journal, 46(3), 53-60.

6. Barth, M. E., Cram, D. P., & Nelson, K. K. (2001). Accruals and the Prediction of Future Cash Flows. The Accounting Review, 76(1), 27-58.

7. Bowen, R. M. (1981). 1979 Competitive Manuscript Award: Valuation of Earnings Components in the Electric Utility Industry. Accounting Review, 1-22.

8. Bratten, B. M. (2009). Analysts’ Use of Earnings Components in Predicting Future Earnings. Dissertation., The University of Texas at Austin.

9. Dechow, P., Ge, W., & Schrand, C. (2010). Understanding Earnings Quality: A Review of the Proxies, Their Determinants and Their Consequences. Journal of Accounting and Economics, 50(2-3), 344-401.

10. Dechow, P. M., & Dichev, I. D. (2002). The Quality of Accruals and Earnings: The Role of Accrual Estimation Errors. The Accounting Review, 77(s-1), 35-59.

11. Fairfield, P. M., Sweeney, R. J., & Yohn, T. L. (1996). Accounting Classification and the Predictive Content of Earnings. Accounting Review, 337-355.

12. Finger, C. A. (1994). The Ability of Earnings to Predict Future Earnings and Cash Flow. Journal of Accounting Research, 210-223.

13. Foster, L., Haltiwanger, J., & Krizan, C. J. (2006). Market selection, reallocation, and restructuring in the US retail trade sector in the 1990s. The Review of Economics and Statistics, 88(4), 748-758.

14. Foster, L., Haltiwanger, J. C., & Krizan, C. J. (2001). Aggregate productivity growth: Lessons from microeconomic evidence New developments in productivity analysis (pp. 303-372): University of Chicago Press.

15. Gonedes, N. J. (1973). Properties of Accounting Numbers: Models and Tests. Journal of Accounting Research, 212-237.

16. Gonedes, N. J. (1975). Risk, Information, and the Effects of Special Accounting Items on Capital Market Equilibrium. Journal of Accounting Research, 220-256.

17. Griffin, P. A. (1977). The Time-series Behavior of Quarterly Earnings: Preliminary Evidence. Journal of Accounting Research, 71-83.

18. Harvey, C. R. (1989). Forecasts of Economic Growth from the Bond and Stock Markets. Financial Analysts Journal, 45(5), 38-45.

19. Konchitchki, Y., & Patatoukas, P. N. (2014a). Accounting Earnings and Gross Domestic Product. Journal of Accounting and Economics, 57(1), 76-88.

20. Konchitchki, Y., & Patatoukas, P. N. (2014b). Taking the Pulse of the Real Economy Using Financial Statement Analysis: Implications for Macro Forecasting and Stock Valuation. The Accounting Review, 89(2), 669-694. doi:10.2308/accr-50632

21. Kothari, S., Lewellen, J., & Warner, J. B. (2006). Stock Returns, Aggregate Earnings Surprises, and Behavioral Finance. Journal of Financial Economics, 79(3), 537-568.

22. Kothari, S., Shivakumar, L., & Urcan, O. (2013). Aggregate Earnings Surprises and Inflation Forecasts. Unpublished Paper, MIT Sloan School of Management and London Business School.

23. Lilien, D. M. (1982). Sectoral shifts and cyclical unemployment. Journal of political economy, 90(4), 777-793.

24. Lipe, R. C. (1986). The Information Contained in the Components of Earnings. Journal of Accounting Research, 24, 37-64. doi:10.2307/2490728

25. Lucas, R. E., & Prescott, E. C. (1974). Equilibrium search and unemployment. Journal of Economic theory, 7(2), 188-209.

26. McCloskey, D. N. (1993). Schelling's Five Truths of Economics. Eastern Economic Journal, 19(1), 109-111.

27. Patatoukas, P. N. (2014). Detecting News in Aggregate Accounting Earnings: Implications for Stock Market Valuation. Review of Accounting Studies, 19(1), 134-160.

28. Richardson, S. A., Sloan, R. G., Soliman, M. T., & Tuna, I. (2005). Accrual Reliability, Earnings Persistence and Stock Prices. Journal of Accounting and Economics, 39(3), 437-485.

29. Schneider, F. (2005). Shadow Economies Around the World: What Do We Really Know? European Journal of Political Economy, 21(3), 598-642.

30. Schumpeter, J. A. (2003). Capitalism, Socialism and Democracy: Taylor & Francis e-Library.

31. Shivakumar, L. (2007). Aggregate Earnings, Stock Market Returns and Macroeconomic Activity: A Discussion of ‘Does Earnings Guidance Affect Market Returns? The Nature and Information Content of Aggregate Earnings Guidance’. Journal of Accounting and Econom

32. Sloan, R. G. (1996). Do Stock Prices Fully Reflect Information in Accruals and Cash Flows about Future Earnings? Accounting Review, 289-315.

33. Wang, H., Cao, F., Li, S., & Liu, X. (2015). Can Accounting Earnings Predict Future GDP Growth? Evidence from China. Frontiers of Business Research in China, 9(1), 30-43.