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, Energy, etc. He also serves as a reviewer for esteemed scientific journals like Expert Systems with Applications, Artificial Intelligence Review, Research in International Business and Finance, Applied Economics Letters, and Applied Soft Computing.
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
Empirical asset pricing
North American Journal of Economics and Finance
We examine directional connectedness patterns from news and social media to financial market volatility using state-of-the-art natural language processing and high-frequency data. We find that media sentiment induces market volatility, but the magnitude of that connectedness is time-varying. In addition, news and social media sentiment pertinent to one market transmits volatility to other markets. Finally, we find that sentiment transmits sharp shocks to markets during major events. At other times, there are smaller spillover effects, indicating that the directional connectedness from sentiment to markets follows a spiky pattern over time. We conclude that news and social media play an important (but not constant) role in transmitting volatility across financial markets. This insight explains earlier divergent findings in the literature.