Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/289022 
Year of Publication: 
2020
Citation: 
[Journal:] Review of Quantitative Finance and Accounting [ISSN:] 1573-7179 [Volume:] 55 [Issue:] 4 [Publisher:] Springer US [Place:] New York, NY [Year:] 2020 [Pages:] 1163-1179
Publisher: 
Springer US, New York, NY
Abstract: 
We propose our quarterly earnings prediction (QEPSVR) model, which is based on epsilon support vector regression (ε-SVR), as a new univariate model for quarterly earnings forecasting. This follows the recommendations of Lorek (Adv Account 30:315–321, 2014. https://doi.org/10.1016/j.adiac.2014.09.008), who notes that although the model developed by Brown and Rozeff (J Account Res 17:179–189, 1979) (BR ARIMA) is advocated as still being the premier univariate model, it may no longer be suitable for describing recent quarterly earnings series. We conduct empirical studies on recent data to compare the predictive accuracy of the QEPSVR model to that of the BR ARIMA model under a multitude of conditions. Our results show that the predictive accuracy of the QEPSVR model significantly exceeds that of the BR ARIMA model under 24 out of the 28 tested experiment conditions. Furthermore, significance is achieved under all conditions considering short forecast horizons or limited availability of historic data. We therefore advocate the use of the QEPSVR model for firms performing short-term operational planning, for recently founded companies and for firms that have restructured their business model.
Subjects: 
Quarterly earnings forecasting
ARIMA models
Support vector regression
Time-series regression
Machine learning
JEL: 
C22
C32
C51
C52
C53
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article
Document Version: 
Published Version

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