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

• 成果类型: Article
• 署名作者: Lorek, KS; Willinger, GL
• 署名单位: University of Oklahoma System; University of Oklahoma - Norman
• 刊物名称: ACCOUNTING REVIEW
• ISSN/ISSBN: 0001-4826
• 发表日期: 1996
• 页码: 81-102
• 关键词: incremental information-content; quarterly accounting earnings; components; accruals; ability

摘要

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.