Predicting Stock Returns: ARMAX versus Machine Learning

Darya Lapitskaya (), Mustafa Eratalay () and Rajesh Sharma ()
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Darya Lapitskaya: University of Tartu
Rajesh Sharma: Institute of Computer Science, University of Tartu

A chapter in Advances in Econometrics, Operational Research, Data Science and Actuarial Studies, 2022, pp 453-464 from Springer

Abstract: 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.

Keywords: Sentiment analysis; Machine learning; ARMAX; Stock returns prediction; Deep learning; COVID-19 (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:spr:conchp:978-3-030-85254-2_27

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DOI: 10.1007/978-3-030-85254-2_27

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