A multivariate time-series prediction model for cash-flow data

Kenneth S. Lorek, G. Lee Willinger

Research output: Contribution to journalArticle

66 Scopus citations

Abstract

This paper provides evidence on the time-series properties and predictive ability of cash-flow data. It employs a sample of firms on which the accuracy of one-step-ahead cash-flow predictions is assessed during the 1989-1991 holdout period. We develop a new multivariate, time-series prediction model that employs past values of earnings, short-term accruals and cash-flows as independent variables in a time-series regression. Our predictive results indicate that this model clearly outperforms firm-specific and common-structure ARIMA models as well as a multivariate, cross-sectional regression model popularized in the literature. These findings are robust across alternative cash-flow metrics (e.g., levels, per-share, and deflated by total assets) and are consistent with the viewpoint espoused by the FASB that cash-flow prediction is enhanced by consideration of earnings and accrual accounting data.

Original languageEnglish (US)
Pages (from-to)81-102
Number of pages22
JournalAccounting Review
Volume71
Issue number1
StatePublished - Jan 1996
Externally publishedYes

Keywords

  • ARIMA
  • Cash-flow
  • Time-series models

ASJC Scopus subject areas

  • Accounting
  • Finance
  • Economics and Econometrics

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