The contextual nature of the predictive power of statistically-based quarterly earnings models

Abstract

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 (F) 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).