Deep Learning Models for Forecasting Treasury Bill Rates Using Gasoline Prices and Gold EPI

1659 

Contributed Abstract 

Poster 

JaNiah Harris (1), Mary Akinyemi (2)

(1) N/A, N/A, (2) Austin Peay State University, N/A

Mary Akinyemi  
Austin Peay State University

JaNiah Harris  
N/A

JaNiah Harris  
N/A

Artificial intelligence has given researchers access to a broader range of historical data, enabling the development of more accurate predictive models in finance. This study investigates the relationship between treasury bill rates and two high-demand commodities: gasoline and gold. We compiled a comprehensive dataset spanning ten years of historical prices: July 2014-July 2024, consisting of treasury bill rates, gasoline prices, gold-EPI, and natural gas consumption, and vital economic indicators such as GDP, inflation rates, and interest rates. Our analysis reveals that natural gas consumption exhibits the highest positive correlation (0.64) with treasury bill rates, while gasoline prices and the export price index of gold show moderate positive correlations (0.51 and 0.52, respectively). Training the data on a machine learning model (Artificial Neural Network) and three deep learning models (Recurrent Neural Network, Convolutional Neural Network, and 3D Convolutional Neural Network). Their performance was assessed using mean absolute error, mean squared error, root mean squared error (RMSE), and R-sq score. The Recurrent Neural Network model performed best with an RMSE of 0.08.

Deep Learning|Recurrent Neural Network|Treasury Bill Rate|3D Convolutional Neural Network|Artificial Neural Network|Machine Learning

Section on Statistical Learning and Data Science

Machine Learning