Predicting long-term earnings growth from multiple information sources

Gao, Zhan and Wu, Wan-Ting (2014) Predicting long-term earnings growth from multiple information sources. International Review of Financial Analysis, 32. pp. 71-84. ISSN 1057-5219

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Abstract

While expected long-term earnings growth plays a pivotal role in valuation and investment applications, its common proxy, analysts’ long-term growth forecasts (LTG), is well known for being over-optimistic. Guided by a stylized growth model, this paper uses three information sources to improve growth prediction—analysts’ forecasts, stock prices, and financial statements. We find that the growth model using LTG, past earnings growth, the forward earnings-to-price ratio and past returns as predictors is unbiased and most accurate among the models considered in this paper. We further show that this growth prediction results in higher trading profits, more accurate equity predictions, and more reliable estimates of cost of equity. The findings suggest that this improvement in growth prediction leads to economically significant consequences in valuation and investment applications.

Item Type:
Journal Article
Journal or Publication Title:
International Review of Financial Analysis
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/aacsb/disciplinebasedresearch
Subjects:
ID Code:
68523
Deposited By:
Deposited On:
10 Feb 2014 08:59
Refereed?:
Yes
Published?:
Published
Last Modified:
01 Jan 2020 08:48