Abstract:
This working paper investigates the application of modern artificial intelligence techniques to financial time-series forecasting, with a specific focus on crude oil futures markets. Building on advances in deep learning and natural language processing, the study evaluates the predictive performance and economic relevance of several neural network architectures, including univariate and multivariate LSTM, CNN, and N-HiTS models. In addition to statistical accuracy, the models are assessed through trading-based performance metrics and factor regressions to examine the presence of economically and statistically significant returns. The paper contributes to the growing literature on AI-driven asset price forecasting by demonstrating that multivariate deep learning models incorporating additional market information and sentiment measures can improve both forecast precision and trading performance in commodity markets.
