Dr. Gabriel A. Abeyie

This paper enhances text-based forecasting models by incorporating validated collocations and improved attention mechanisms, moving beyond traditional TF-IDF approaches to better capture the semantic content of news articles for economic forecasting.

This paper analyzes heterogeneous timing patterns in oil market reactions to EIA inventory announcements using high-frequency data from 2012-2024. I develop a novel three-regime classification system (FAST, NORMAL, SLOW) and show that NORMAL regime reactions exhibit the strongest surprise-return relationships (R² = 15.5%). The findings reveal that recent regime history dominates prediction over surprise characteristics, suggesting time-varying market efficiency driven by strategic trading behavior and rational inattention.

This paper uses textual analysis to identify and measure the impact of OPEC news shocks on oil markets and the broader economy, developing novel methods to extract information from OPEC announcements and communications.

This research examines the welfare consequences of asset price bubble cycles, analyzing how boom-bust patterns in asset markets affect economic efficiency and social welfare.

This paper develops a novel forecasting methodology combining principal component analysis with weighted elastic net regularization to improve crude oil price return predictions using high-dimensional predictor sets.