A Contextual Evaluation of Composite Forecasts of Annual Earnings

Pieter T. Elgers (), May H. Lo, Wenjuan Xie () and Le Emily Xu ()
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Pieter T. Elgers: Department of Accounting, University of Massachusetts, Amherst, USA
May H. Lo: Department of Accounting and Finance, Western New England University, USA
Wenjuan Xie: Department of Accounting and Finance, University of New Hampshire, USA
Le Emily Xu: Department of Accounting and Finance, University of New Hampshire, USA

Review of Pacific Basin Financial Markets and Policies (RPBFMP), 2016, vol. 19, issue 03, 1-40

Abstract: This study addresses the impact of firm- and time-specific attributes on the accuracy of composite forecasts of annual earnings, constructed from time-series, price-based, and analysts' forecasts. The attributes examined include firm size, analysts' coverage, and time periods pre-dating and following the implementation of regulation fair disclosure. Our results indicate that the relative accuracy of the composite forecasts is time-specific. In the pre-regulation fair disclosure period, composite forecasts significantly outperform each of the three individual forecast sources. Moreover, the extent of improvement in accuracy of composite forecasts is significantly higher for the smaller and lightly-covered firms. Collectively, these results suggest that the predictive accuracy of composite forecasts is contextual.

Keywords: Composite forecasts; analysts' forecasts; forecast accuracy; contextual factors; regulation fair disclosure (search for similar items in EconPapers)
Date: 2016
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DOI: 10.1142/S0219091516500144

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