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A Multivariate Time-Series Prediction Model For Cash-Flow Data.

Kenneth S. Lorek¹; G. Lee Willinger²

¹ Florida State University 1 · ² University of Oklahoma 2

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 mode! 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.

DOI

10.2308/tar-9602190350

Volume

71 (1)

Pages

81-102

Language

en

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