A Multivariate Time Series Prediction Model For Cash Flow Data
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.