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An analysis of the accuracy of long-term earnings predictions

โœ Scribed by Kenneth S. Lorek; G. Lee Willinger

Publisher
Elsevier Science
Year
2002
Tongue
English
Weight
662 KB
Volume
19
Category
Article
ISSN
0882-6110

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โœฆ Synopsis

This paper provides information on the long-term predictive ability of annual earnings numbers. We obtained a sample of 486 calendar, yearend firms that had complete quarterly earnings-per-share (eps) before extraordinary items available from 1978 to 1998. Firm-specific, quarterly, autoregressive-integrated-moving-average (ARIMA) time-series models were used to generate one through five year-ahead annual eps predictions across the 1994-1998 holdout period. Analysis of mean absolute percentage errors indicates : (1) firm-specific ARIMA models outperform so-called, common-structure, "premier" ARIMA models, (2) forecast errors from the firm-specific ARIMA time-series models ranged from 0 .358 to 0 .547 for one through five year-ahead annual eps predictions, (3) longterm earnings forecast accuracy is linked to firm size and earnings persistence, and (4) further research is needed to develop more powerful, long-term earnings prediction models suitable for use in conjunction with the abnormal earnings valuation model.

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