Dr. Gabriel A. Abeyie

This paper proposes a method for enhancing text-based forecasting models, with a specific focus on predicting crude oil prices. Utilizing advanced techniques, including pattern validation and attention mechanisms, the study demonstrates notable improvements in predictive power over traditional approaches. One key finding is that considering the full text of news articles, rather than limiting the analysis to news headlines, leads to significant gains in forecasting accuracy. Furthermore, the model featuring verb-noun and noun-verb collocation pattern validation consistently outperforms benchmarks and models based solely on news headlines across various forecasting horizons. The results suggest that the presence of such collocations as ’price fell,’ ’prices tumbled,’ and ’price dropped’ in crude-oil-related news articles is associated with a decrease in oil price returns. Additionally, integrating macroeconomic data with text-based features enhances predictive performance, demonstrating that combining structured economic indicators with textual features improves forecasting accuracy.

Traditional event studies assume that all announcements of a given type generate price reactions at the same speed, justifying fixed event windows. Using high-frequency data on crude oil futures and 612 weekly EIA inventory announcements from 2012-2024, we show this assumption is violated in economically meaningful ways. We develop a two-stage framework that first predicts reaction timing using machine learning, then estimates regime-conditional price impacts. Applying an incremental returns method to isolate genuine price reactions from drift, we identify three distinct regimes: FAST (0-30 minutes, 24% of announcements), NORMAL (35-90 minutes, 45%), and SLOW (95+ minutes, 31%). The NORMAL regime achieves highly significant surprise-return relationships. FAST reactions are immediate but statistically insignificant, while SLOW reactions show no systematic response to inventory surprises. Our two-stage model nearly replicates oracle performance, demonstrating that accounting for heterogeneous timing substantially improves event study measurement.

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.