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

Kenneth S Lorek, G. Lee Willinger

Research output: Contribution to journalArticle

63 Citations (Scopus)

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 - 1996
Externally publishedYes

Fingerprint

Prediction model
Multivariate time series
Cash flow
Cash flow prediction
Regression model
Accruals
Accrual accounting
Accounting data
Assets
Predictive ability
Cross-sectional regression
ARIMA models

Keywords

  • ARIMA
  • Cash-flow
  • Time-series models

ASJC Scopus subject areas

  • Finance
  • Accounting
  • Economics and Econometrics

Cite this

A multivariate time-series prediction model for cash-flow data. / Lorek, Kenneth S; Willinger, G. Lee.

In: Accounting Review, Vol. 71, No. 1, 1996, p. 81-102.

Research output: Contribution to journalArticle

Lorek, Kenneth S ; Willinger, G. Lee. / A multivariate time-series prediction model for cash-flow data. In: Accounting Review. 1996 ; Vol. 71, No. 1. pp. 81-102.
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