An analysis of the accuracy of long-term earnings predictions

Kenneth S. Lorek, G. Lee Willinger

Research output: Chapter in Book/Report/Conference proceedingChapter

2 Scopus citations


This paper provides information on the long-term predictive ability of annual earnings numbers. We obtained a sample of 486 calendar, year-end 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, "primier" 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) long-term 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.

Original languageEnglish (US)
Title of host publicationAdvances in Accounting
PublisherJAI Press
Number of pages15
ISBN (Print)0762308710, 9780762308712
StatePublished - 2002

ASJC Scopus subject areas

  • Accounting
  • Finance


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