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

Kenneth S Lorek, G. Lee Willinger

Research output: Contribution to journalArticle

9 Citations (Scopus)

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).

Original languageEnglish (US)
Pages (from-to)1-22
Number of pages22
JournalReview of Quantitative Finance and Accounting
Volume28
Issue number1
DOIs
StatePublished - Jan 2007

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Predictive power
ARIMA models
Random walk
Prediction
Earnings persistence
Empirical evidence
Analyst coverage
Financial institutions
High-technology firms
Predictive ability
Factors

ASJC Scopus subject areas

  • Business, Management and Accounting(all)
  • Economics, Econometrics and Finance(all)

Cite this

The contextual nature of the predictive power of statistically-based quarterly earnings models. / Lorek, Kenneth S; Willinger, G. Lee.

In: Review of Quantitative Finance and Accounting, Vol. 28, No. 1, 01.2007, p. 1-22.

Research output: Contribution to journalArticle

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