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

Jan Alexander Fischer,Dietmar Ratz,Philipp Pohl

doi:10.1007/s11156-020-00871-3

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.

Highlights

The quarterly earnings reported by a company is an accounting figure of great significance
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
In this paper we introduce a new model based on epsilon support vector regression (ε-SVR) (Smola and Schölkopf 2004; Vapnik 1995), termed the quarterly earnings prediction (QEPSVR) model

Summary

Introduction

The quarterly earnings reported by a company is an accounting figure of great significance. 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). 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.

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Conclusion