Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/289022 
Erscheinungsjahr: 
2020
Quellenangabe: 
[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
Verlag: 
Springer US, New York, NY
Zusammenfassung: 
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.
Schlagwörter: 
Quarterly earnings forecasting
ARIMA models
Support vector regression
Time-series regression
Machine learning
JEL: 
C22
C32
C51
C52
C53
Persistent Identifier der Erstveröffentlichung: 
Creative-Commons-Lizenz: 
cc-by Logo
Dokumentart: 
Article
Dokumentversion: 
Published Version

Datei(en):
Datei
Größe





Publikationen in EconStor sind urheberrechtlich geschützt.