PhD candidate in Finance at UiT The Arctic University of Norway
Hooman's Ph.D. project measures the connection between financial markets and media using AI-driven techniques, such as natural language processing and time series forecasting models. His research has been published in reputable journals, including Energy Economics. He also serves as a reviewer for esteemed scientific journals like Research in International Business and Finance, Applied Economics Letters, Applied Soft Computing, and Artificial Intelligence Review.
BED 2032 Corporate Finance (Spring 2023)
B.Sc. level (10 ECTS), composed of 4 modules on:
Risk & Return
Options & International Finance
Sustainability & Corporate Finance
AI-driven modeling in finance
Media and market
Oil price volatility and new evidence from news and Twitter
Energy Economics, 2023
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.