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

Jan Alexander Fischer (), Philipp Pohl () and Dietmar Ratz ()
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Jan Alexander Fischer: University of Zurich
Philipp Pohl: Baden-Wuerttemberg Cooperative State University Karlsruhe
Dietmar Ratz: Baden-Wuerttemberg Cooperative State University Karlsruhe

Review of Quantitative Finance and Accounting, 2020, vol. 55, issue 4, No 1, 1163-1179

Abstract: 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.

Keywords: Quarterly earnings forecasting; ARIMA models; Support vector regression; Time-series regression; Machine learning (search for similar items in EconPapers)
JEL-codes: C22 C32 C51 C52 C53 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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DOI: 10.1007/s11156-020-00871-3

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