Publication Type

Journal Article

Publication Date

4-2017

Abstract

In this paper, we propose and empirically test a cross-sectional profitability forecasting model which incorporates two major improvements relative to extant models. First, in terms of model construction, we incorporate mean reversion through the use of a two-stage partial adjustment model and inclusion of a number of additional relevant determinants of profitability. Second, in terms of model estimation, we employ least absolute deviation (LAD) analysis instead of ordinary least squares (OLS) because the former approach is able to better accommodate outliers. Results reveal that forecasts from our model are more accurate than three extant models at every forecast horizon considered and more accurate than consensus analyst forecasts at forecast horizons of two through five years. Further analysis reveals that LAD estimation provides the greatest incremental accuracy improvement followed by the inclusion of income subcomponents as predictor variables, and implementation of the two-stage partial adjustment model. In terms of economic relevance, we find that forecasts from our model are informative about future returns, incremental to forecasts from other models, analysts’ forecasts, and standard risk factors. Overall, our results are important because they document the increased accuracy and economic relevance of a cross-sectional profitability forecasting model which incorporates improvements to extant models in terms of model construction and estimation.

Keywords

Earnings Forecasts, Financial Statement Analysis, Security Analysts

Discipline

Econometrics | Statistics and Probability

Research Areas

Accounting Information System

Publication

Contemporary Accounting Research

ISSN

0823-9150

Identifier

10.1111/1911-3846.12307

Publisher

Canadian Academic Accounting Association

Copyright Owner and License

Authors

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Additional URL

http://doi.org/10.1111/1911-3846.12307

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