A machine learning approach to univariate time series forecasting of quarterly earnings
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A machine learning approach to univariate time series forecasting of quarterly earnings Jan Alexander Fischer1 · Philipp Pohl2 · Dietmar Ratz2
© The Author(s) 2020
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 Q EPSVR model to that of the BR ARIMA model under a multitude of conditions. Our results show that the predictive accuracy of the Q EPSVR 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 JEL Classification C22 · C32 · C51 · C52 · C53
* Philipp Pohl pohl@dhbw‑karlsruhe.de Jan Alexander Fischer [email protected] Dietmar Ratz ratz@dhbw‑karlsruhe.de 1
University of Zurich, Rämistrasse 71, 8006 Zurich, Switzerland
2
Baden-Wuerttemberg Cooperative State University Karlsruhe, Erzbergerstrasse 121, 76133 Karlsruhe, Germany
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1 Introduction The quarterly earnings reported by a company is an accounting figure of great significance. Quarterly earnings can be used to track performance in the context of management and debt contracts (Dechow et al. 1998), and are reflective of corporate governance (Chen et al. 2015). Isidro and Dias (2017) also show that earnings are strongly related to stock returns in volatile market conditions, while Zoubi et al. (2016) consider disaggregated earnings to better explain variation in stock returns. Furthermore, differences between forecasted and actual earnings have been used to calculate a firm’s market premium (Dopuch et al. 2008). The prediction of future quarterly earnings using univariate statistical models has been the subject of extensive research. Lorek and Willinger (2011) and Lorek (2014) claim that the autoregressive integrated moving average (ARIMA) model proposed by Brown and Rozeff (1979), denoted by BR ARIMA, is the premier univariate statistical model for the prediction of quarterly earnings. Its functional form is ) ) ( ( Yq = Yq−4 + 𝜙 Yq−1 − Yq−5 + 𝜖q − 𝜃 𝜖q−4 ,
where Yq is the earnings of a company for a qu
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