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Predicting Stock Returns: ARMAX versus Machine Learning

In: Advances in Econometrics, Operational Research, Data Science and Actuarial Studies

Author

Listed:
  • Darya Lapitskaya

    (University of Tartu)

  • Hakan Eratalay

    (University of Tartu)

  • Rajesh Sharma

    (Institute of Computer Science, University of Tartu)

Abstract

In the modern world, online social and news media significantly impact society, economy and financial markets. In this chapter, we compared the predictive performance of financial econometrics and machine learning and deep learning methods for the returns of the stocks of the S&P 100 index. The analysis is enriched by using COVID-19-related news sentiments data collected for a period of 10 months. We analysed the performance of each model and found the best algorithm for such types of predictions. For the sample we analysed, our results indicate that the autoregressive–moving-average model with exogenous variables (ARMAX) has a comparable predictive performance to the machine and deep learning models, only outperformed by the extreme gradient boosted trees (XGBoost) approach. This result holds both in the training and testing datasets.

Suggested Citation

  • Darya Lapitskaya & Hakan Eratalay & Rajesh Sharma, 2022. "Predicting Stock Returns: ARMAX versus Machine Learning," Contributions to Economics, in: M. Kenan Terzioğlu (ed.), Advances in Econometrics, Operational Research, Data Science and Actuarial Studies, pages 453-464, Springer.
    • Handle: RePEc:spr:conchp:978-3-030-85254-2_27
    DOI: 10.1007/978-3-030-85254-2_27
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    Cited by:

    1. Kolte, Ashutosh & Roy, Jewel Kumar & Vasa, László, 2023. "The impact of unpredictable resource prices and equity volatility in advanced and emerging economies: An econometric and machine learning approach," Resources Policy, Elsevier, vol. 80(C).

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