Oil price volatility and new evidence from news and Twitter
Permanent lenke
https://hdl.handle.net/10037/29978Dato
2023-05-05Type
Journal articleTidsskriftartikkel
Peer reviewed
Forfatter
Abdollahi, HoomanSammendrag
In this paper, we develop semantic-based sentiment indices through relevant news and Twitter feeds for oil
market using a state-of-the-art natural language processing technique. We investigate the predictability of crude
oil price volatility using the novel sentiment indices through a hybrid structure consisting of generalized
autoregressive conditional heteroskedasticity and bidirectional long short-term memory models. Findings show
that media sentiment considerably enhances forecasting quality and the proposed framework outperforms
existing benchmark models. More importantly, we compare the predictive power of news stories with Twitter
feeds and document the superiority of the news sentiment index over the counterpart. This is an important
contribution as this paper is the first study that compares the impact of regular press with that of social media, as
an alternative informative medium, on oil market dynamics.
Er en del av
Abdollahi, H. (2024). Market volatility and new evidence from media sentiment: An AI-driven approach. (Doctoral thesis). https://hdl.handle.net/10037/35326Forlag
ElsevierSitering
Abdollahi. Oil price volatility and new evidence from news and Twitter. Energy Economics. 2023;122Metadata
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