A machine learning approach to univariate time series forecasting of quarterly earnings

Artikel

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.

Angaben zum Objekt

• Sprache: Englisch

Verweise und Beziehungen

• Erschienen in: Journal: Review of Quantitative Finance and Accounting ; ISSN: 1573-7179 ; Volume: 55 ; Year: 2020 ; Issue: 4 ; Pages: 1163-1179 ; New York, NY: Springer US

Klassifikation und Themen

• Klassifikation: Wirtschaft ; Single Equation Models; Single Variables: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes ; Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models ; Model Construction and Estimation ; Model Evaluation, Validation, and Selection ; Forecasting Models; Simulation Methods
• Thema: Quarterly earnings forecasting ; ARIMA models ; Support vector regression ; Time-series regression ; Machine learning

Beteiligte, Orts- und Zeitangaben

• Ereignis: Geistige Schöpfung
• (wer): Fischer, Jan Alexander ; Pohl, Philipp ; Ratz, Dietmar
• Ereignis: Veröffentlichung
• (wer): Springer US
• (wo): New York, NY
• (wann): 2020

Weitere Informationen

• DOI: doi:10.1007/s11156-020-00871-3
• Letzte Aktualisierung: 10.03.2025, 11:42 MEZ