Predicting stock return and volatility with machine learning and econometric models - a comparative case study of the Baltic stock market

Anders Nõu, Darya Lapitskaya, M. Hakan Eratalay and Rajesh Sharma

International Journal of Computational Economics and Econometrics, 2023, vol. 13, issue 4, 446-489

Abstract: For stock market predictions, a crucial problem is predicting the prices as accurately as possible. There are different approaches (for example, econometrics and machine learning) for predicting stock returns. However, it is non-trivial to find an approach which works the best. In Baltic countries, the interest in stock market investment has grown due to the popularisation of investment and the favourable trading conditions of banks, however, there are very few attempts to apply machine learning and neural network models in the studies of the Baltic region. In this paper, we make a thorough analysis of the predictive accuracy of several machine learning and econometric approaches (ARMA, GARCH, random forest, SVR, KNN and GARCH-ANN) for predicting the returns and volatilities on the OMX Baltic Benchmark Price Index. Our results show that the machine learning methods predict the returns better than autoregressive moving average models for most of the metrics.

Keywords: machine learning; neural networks; autoregressive moving average; ARMA; generalised autoregressive conditionally heteroscedastic; GARCH. (search for similar items in EconPapers)
Date: 2023
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.inderscience.com/link.php?id=133923 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ids:ijcome:v:13:y:2023:i:4:p:446-489

Access Statistics for this article

More articles in International Journal of Computational Economics and Econometrics from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().