The Predictive Ability of Statistically-Based Cash-Flow Models: Working Paper Series--09-02

We assess the inter-temporal predictive ability of statistically-based, cash-flow prediction models by extending extant work on annual cash-flow prediction models. Our empirical results consistently underscore the superiority of quarterly cash-flow prediction models estimated on a time-series basis versus cross-sectional models. The superiority of relatively parsimonious, time-series models is consistent with the need to incorporate the firm-specific variability of parameters into expectations rather than restricting such parameters to be constant across firms and time when models are estimated cross-sectionally. Additionally, parsimonious models that employ aggregate earnings data are superior to more complex, disaggregated accrual models. The above results are similar regardless of whether models are estimated using undeflated or deflated variables. These results are particularly salient to researchers and users interested in generating accurate multi-step ahead cash-flow forecasts

The Contextual Nature of the Predicitve Power of Statistically-Based Quarterly Earnings Model: Working Paper Series--05-18

We present new empirical evidence on the contextual nature of the predictive power of five statistically-based quarterly earnings expectation models evaluated on a holdout period spanning the twelve quarters from 2000-2002. In marked contrast to extant time-series work, the random walk with drift (RWD) model provides significantly more accurate pooled, one-step-ahead quarterly earnings predictions for a sample of high-technology firms (n=202). In similar predictive comparisons, the Griffin-Watts (GW) ARIMA model provides significantly more accurate quarterly earnings predictions for a sample of regulated firms (n=218). Finally, the RWD and GW ARIMA models jointly dominate the other expectation models (i.e., seasonal random walk with drift, the Brown-Rozeff (BR) and Foster ARIMA models) for a default sample of firms (n=796). We provide supplementary analyses that document the: 1) increased frequency of the number of loss quarters experienced by our sample firms in the holdout period (2000-2002) vis-a-vis the identification period (1990-1999); 2) reduced levels of earnings persistence for our sample firms relative to earnings persistence factors computed by Baginski et al. (2003) during earlier time periods (1970s - 1980s); 3) relative impact on the predictive ability of the five expectation models conditioned upon the extent of analyst coverage of sample firms (i.e., no coverage, moderate coverage, and extensive coverage); and 4) sensitivity of predictive performance across subsets of regulated firms with the BR ARIMA model providing the most accurate predictions for utilities (n=87) while the RWD model is superior for financial institutions (n=131)

The Time-Series Properties of Quarterly Cash Flows: Working Paper Series--09-12

Considerable advancements in the structural modeling of annual cash flow prediction models have been accomplished in recent years [Dechow et al. (1998) and Barth et al. (2001), among others].Yet, the modeling of quarterly cash flow data has not been as forthcoming due to: (1) the unavailability of sufficiently long time-series data bases of quarterly cash flows reported in accordance with SFAS No. 95 and (2) the presence of seasonality. We provide new empirical findings supportive of the Brown-Rozeff ARIMA model as a candidate statistically-based expectation model for multi-period ahead projections of quarterly cash flows. The Brown-Rozeff ARIMA model provides one-thru-twenty step-ahead projections of quarterly cash flows that are significantly more accurate than those generated by a quarterly time-series, disaggregated-accrual regression model originally popularized by Lorek and Willinger (1996). Although both quarterly earnings and quarterly cash flow from operations are modeled by the same ARIMA structure, we find that the autoregressive and seasonal moving-average parameters of the quarterly earnings model are significantly larger than those of the cash-flow prediction model. This finding is consistent with Beaver (1970) who argues that short-term and long-term accruals induce incremental amounts of serial correlation in the quarterly earnings time series vis-a-vis the time series of quarterly cash flows. These findings are of interest to standard-setting bodies seeking to understand the linkages between accruals and cash flows, analysts who wish to derive multi-step ahead cash flow predictions, and accounting researchers attempting to adopt a statistical proxy for the market's expectation of quarterly cash flows

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

Modeling of Nonseasonal Quarterly Earnings Data: Working Paper Series--05-17

We present new empirical evidence on the predictive power of statistically-based quarterly earnings expectation models for firms which exhibit nonseasonal quarterly earnings patterns. In marked contrast to extant work we find: 1) a considerably greater frequency of nonseasonal firms (36%) when compared to Lorek and Bathke (1984) (12%) and Brown and Han (2000) (17%), 2) the random walk model (RW) provides significantly more accurate pooled, one-step ahead quarterly earnings predictions across 40 quarters in the 1994-2003 holdout period than the first-order autoregressive model (AR1) popularized by Lorek and Bathke and Brown and Han, and 3) the RW model provides significantly more accurate quarterly earnings predictions for large nonseasonal firms than smaller nonseasonal firms. The latter finding documents a size-effect with respect to predictive ability for nonseasonal firms similar to that evidenced for seasonal firms. These findings are particularly salient to researchers in search of efficient statistically-based quarterly earnings expectation models since 129 of 296 (43.6%) sample firms are not covered by security analysts

Predictive ability of alternative income concepts / 268

Includes bibliographical references

Preliminary evidence on the descriptive and predictive properties of GPPA earnings data in the airlines industry / BEBR no. 711

Title page includes summary.Includes bibliographical references (p. 28-30)

A comparative analysis of the predictive ability of adaptive forecasting, reestimation and reidentification using Box-Jenkins time series analysis / BEBR No.327

Includes bibliographical references (leaf 16)