The contextual nature of the predictive power of statistically-based quarterly earnings models |
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Authors: | Kenneth S Lorek G Lee Willinger |
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Institution: | (1) College of Business Administration, Northern Arizona University, P.O. Box 15066, Flagstaff, AZ 86011-5066, USA;(2) Price College of Business, University of Oklahoma, Norman, OK 73019, USA |
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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-à-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).
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