Predicting Stock Returns: ARMAX versus Machine Learning

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 SP100 index. The analysis is enriched by using COVID-19 related news sentiments data collected for a period of 10 months. We analyzed the performance of each model and found the best algorithm for such types of predictions. For the sample we analyzed, 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.

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

“…The main reason behind this trend is that usually, machine learning usage overcomes limitations of traditional econometric models (Rossi, 2018). In addition, we can observe that some machine learning algorithms demonstrate high accuracy and high predictive powers compared to traditional econometric models when used for stock returns predictions (Lapitskaya et al, 2021). In this approach, various learning methods are used by researchers.…”