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

Author

Listed:
  • Jan Alexander Fischer

    (University of Zurich)

  • Philipp Pohl

    (Baden-Wuerttemberg Cooperative State University Karlsruhe)

  • Dietmar Ratz

    (Baden-Wuerttemberg Cooperative State University Karlsruhe)

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.

Suggested Citation

  • Jan Alexander Fischer & Philipp Pohl & Dietmar Ratz, 2020. "A machine learning approach to univariate time series forecasting of quarterly earnings," Review of Quantitative Finance and Accounting, Springer, vol. 55(4), pages 1163-1179, November.
    • Handle: RePEc:kap:rqfnac:v:55:y:2020:i:4:d:10.1007_s11156-020-00871-3
    DOI: 10.1007/s11156-020-00871-3
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    References listed on IDEAS

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    1. Nicholas Dopuch & Chandra Seethamraju & Weihong Xu, 2008. "An empirical assessment of the premium associated with meeting or beating both time-series earnings expectations and analysts’ forecasts," Review of Quantitative Finance and Accounting, Springer, vol. 31(2), pages 147-166, August.
    2. Sung Kwon & Jennifer Yin, 2015. "A comparison of earnings persistence in high-tech and non-high-tech firms," Review of Quantitative Finance and Accounting, Springer, vol. 44(4), pages 645-668, May.
    3. Taisier A. Zoubi & Feras Salama & Mahmud Hossain & Yass A. Alkafaji, 2016. "The Value Relevance of Components of Other Comprehensive Income When Net Income Is Disaggregated," Review of Pacific Basin Financial Markets and Policies (RPBFMP), World Scientific Publishing Co. Pte. Ltd., vol. 19(04), pages 1-36, December.
    4. Dechow, Patricia M. & Kothari, S. P. & L. Watts, Ross, 1998. "The relation between earnings and cash flows," Journal of Accounting and Economics, Elsevier, vol. 25(2), pages 133-168, May.
    5. Brown, Ld & Rozeff, Ms, 1979. "Univariate Time-Series Models Of Quarterly Accounting Earnings Per Share - Proposed Model," Journal of Accounting Research, Wiley Blackwell, vol. 17(1), pages 179-189.
    6. Lorek, Kenneth S., 2014. "A critical assessment of the time-series literature in accounting pertaining to quarterly accounting numbers," Advances in accounting, Elsevier, vol. 30(2), pages 315-321.
    7. Helena Isidro & José G. Dias, 2017. "Earnings quality and the heterogeneous relation between earnings and stock returns," Review of Quantitative Finance and Accounting, Springer, vol. 49(4), pages 1143-1165, November.
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    Cited by:

    1. Tomas Kliestik & Alena Novak Sedlackova & Martin Bugaj & Andrej Novak, 2022. "Stability of profits and earnings management in the transport sector of Visegrad countries," Oeconomia Copernicana, Institute of Economic Research, vol. 13(2), pages 475-509, June.

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    More about this item

    Keywords

    Quarterly earnings forecasting; ARIMA models; Support vector regression; Time-series regression; Machine learning;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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