Dynamic jump intensities and news arrival in oil futures markets

Ensor, Katherine B.; Yu, Han; Ostdiek, Barbara; Turnbull, Stuart M.

doi:10.1057/s41260-020-00168-z

Cited by 2 publications

(1 citation statement)

References 60 publications

(71 reference statements)

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“…Another issue to focus on is the asymmetric effect of the news on returns. It has been documented that the negative return shocks affect the returns and volatilities differently than the positive return shocks (See Maheu and McCurdy (2004), Puzanova and Eratalay (2020), Ensor et al (2020), Engle and Ng (1993)). The models in this paper incorporate this asymmetric effect of the news by considering positive and negative news sentiment scores separately.…”

Section: Methodsmentioning

confidence: 99%

Predicting Stock Returns: ARMAX versus Machine Learning

Lapitskaya

Eratalay

Sharma

2022

Contributions to Economics

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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.

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