The filtering of transitory noise in earnings numbers

Z. Lieber*, E. I. Melnick, J. Ronen

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


This paper applies the Kalman filtering procedure to estimate persistent and transitory noise components of accounting earnings. Designating the transitory noise component separately (under a label such as extraordinary items) in financial reports should help users predict future earnings. If a firm has no foreknowledge of future earnings, managers can apply a filter to a firm's accounting earnings more efficiently than an interested user. If management has foreknowledge of earnings, application of a filtering algorithm can result in smoothed variables that convey information otherwise not available to users. Application of a filtering algorithm to a sample of firms revealed that a substantial number of firms exhibited a significant transitory noise component of earnings. Also, for those firms whose earnings exhibited a significant departure from the random walk process, the paper shows that filtering can be fruitfully applied to improve predictive ability.

Original languageEnglish
Pages (from-to)331-350
Number of pages20
JournalJournal of Forecasting
Issue number4
StatePublished - 1983


  • Annual earnings
  • Kalman filter
  • Persistent and transitory components
  • Predictive ability


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