  Public finances are one of the fundamental mechanisms of economic governance
that refer to the financial activities and decisions made by government
entities to fund public services, projects, and operations through assets. In
today's globalized landscape, even subtle shifts in one nation's public debt
landscape can have significant impacts on that of international finances,
necessitating a nuanced understanding of the correlations between international
and national markets to help investors make informed investment decisions.
Therefore, by leveraging the capabilities of artificial intelligence, this
study utilizes neural networks to depict the correlations between US and
International Public Finances and predict the changes in international public
finances based on the changes in US public finances. With the neural network
model achieving a commendable Mean Squared Error (MSE) value of 2.79, it is
able to affirm a discernible correlation and also plot the effect of US market
volatility on international markets. To further test the accuracy and
significance of the model, an economic analysis was conducted that aimed to
correlate the changes seen by the results of the model with historical stock
market changes. This model demonstrates significant potential for investors to
predict changes in international public finances based on signals from US
markets, marking a significant stride in comprehending the intricacies of
global public finances and the role of artificial intelligence in decoding its
multifaceted patterns for practical forecasting.
