Time-Series Properties and Predictive Ability of Quarterly Cash Flows: Working Paper Series--06-10

We provide descriptive and predictive evidence on the time-series properties and predictive ability of quarterly cash flows from operations (CFO) reported in accordance with Statement of Financial Accounting Standards (SFAS) No. 95. Previous work such as Hopwood and McKeown (1992) and Lorek, Schaefer and Willinger (1993), among others, has analyzed a proxy series (PCFO) for quarterly cash flows constructed via a relatively simplistic algorithm. We provide new evidence documenting that: (1) the time-series properties of quarterly CFO are at variance with the exclusively seasonal characterization of quarterly PCFO exhibiting both adjacent (quarter-to-quarter) and seasonal (quarter-by-quarter) relationships, (2) the Brown-Rozeff ARIMA model significantly outpredicts the random walk with drift model, seasonal random walk with drift model, and the multivariate time-series regression model (MULT) originally popularized by Lorek and Willinger (1996), (3) the forecast errors of larger firms are significantly smaller than the forecast errors of smaller firms, and (4) the predictive ability of the Brown-Rozeff model is robust. Specifically, we tested it upon an expanded sample of firms (n=745) obtained by eliminating the considerable data requirements of MULT and it exhibited superior predictive power