Deep Learning Approaches in the Effects of Recession and FOMC Minutes on Oil Prices

Abstract

The financial industry is increasingly interested in predictive analysis and forecasting using time series data. Understanding the relationship between recessions and oil markets is crucial for developing financial forecasts and strategic decisions. This study uses advanced deep learning models to examine the interaction between recession signals and crude oil prices. Data covering recession periods include key economic indicators such as Gross Domestic Product (GDP) fluctuations, unemployment rates, consumer spending trends, business investments, and housing market dynamics. Additionally, Federal Open Market Committee (FOMC) minutes are used to capture economic assessments and monetary policy decisions by the Federal Reserve during recessions, providing insights into policymakers' expectations and responses. Data is augmented using Time-series Generative Adversarial Networks (TimeGAN) to capture intricate patterns in oil prices. By focusing on feature selection, this study aims to identify patterns from historical data and relationships between recession signals and oil price movements. Long Short-Term Memory networks (LSTMs), Gated Recurrent Units (GRUs), Transformer, and ensemble learning techniques are used to predict crude oil prices during recessions. This research provides insights into how recession signals and Federal Reserve policy decisions influence crude oil prices, offering a comprehensive view of the dynamics between economic downturns and the energy market.