Abstract
Researchers, practitioners, and standard setters emphasize the importance of disaggregating financial statements into operating and financial activities. However, there is a lack of research demonstrating that this disaggregation improves forecasts of profitability. In this study, we consider whether and when the operating/financial disaggregation improves forecasts of profitability. Contrary to the use of an aggregate forecasting approach by most related prior research, we first show that the operating/financial disaggregation only provides forecast improvement over a benchmark model incorporating aggregate information when the components forecasting approach is used. We also compare the operating/financial disaggregation to the unusual/infrequent disaggregation required by US GAAP. We find that the operating/financial disaggregation yields less accurate forecasts than the unusual/infrequent disaggregation. However, when using the components forecasting approach, we find that the combination of both disaggregations improves forecasts of profitability. Finally, we document that the incremental usefulness of the operating/financial disaggregation relative to a benchmark model incorporating aggregate information is a function of growth and accounting conservatism. Overall, our study provides timely evidence concerning how analysts and investors might best use the operating/financial disaggregation for forecasting profitability.
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Notes
Bernard (1995, p. 736) suggests that profitability prediction “is tantamount to the ability to approximate current value,” highlighting the importance of examining forecasts of profitability to evaluate any proposed disaggregation. In addition, Nissim and Penman (2001, p. 124) note that “the analysis of current financial statements should be guided by the ‘predictive ability’ criterion: any enhancement that improves forecasts is an innovation.”
Please see Penman (2013, p. 368) for further discussion of the derivation of this decomposition of ROE.
These studies rely on Feltham and Ohlson’s (1995) assertion that operating activities drive firm value and thus that financial profitability need not be forecasted. Our first research question empirically examines this assertion.
In additional analyses, we find generally greater absolute forecast errors for the OpFin models when including these observations.
We also conducted our primary analyses with 72,142 firm-year observations using quantile regression analysis. This form of analysis estimates the conditional median forecast improvement rather than the conditional mean forecast improvement and thus is more robust to outliers than regression analyses based on ordinary least squares. Our findings are inferentially the same using quantile regression analysis.
For example, the median improvement in accuracy of 0.01032 for the OpFin_COMP versus OpFin_AGG comparison implies that the OpFin_COMP model improves forecast accuracy of profitability by 1.032 % of average book value of equity relative to the OpFin_AGG model. The median year-ahead ROE is 15.5 %
In untabulated analyses, we restrict our sample to firm-year observations with nonzero other comprehensive income and find evidence consistent with a significant positive interaction effect of growth and accounting conservatism on improvements in forecast accuracy.
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Acknowledgments
We thank Brad Badertscher, Ted Christensen, Steven Crawford, Peter Easton, Patricia Fairfield, Stephen Penman, Tom Stober, and workshop participants at the Boston Accounting Research Colloquium, BYU Symposium, HEC Paris, Rice University, Texas Tech University, the University of Delaware, the University of Michigan, the University of Notre Dame, the University of Oregon, and the University of Toronto for helpful comments. Marlene Plumlee and Teri Yohn acknowledge the generous support of the David Eccles Faculty Fellowship and the PricewaterhouseCoopers Fellowship, respectively.
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Esplin, A., Hewitt, M., Plumlee, M. et al. Disaggregating operating and financial activities: implications for forecasts of profitability. Rev Account Stud 19, 328–362 (2014). https://doi.org/10.1007/s11142-013-9256-5
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DOI: https://doi.org/10.1007/s11142-013-9256-5