Artificial Intelligence-based Analysis of Change in Public Finance between US and International Markets
Kapil Panda
TL;DR
The paper tackles how shifts in US public finances propagate to international markets. It employs an LSTM-based neural network to map temporal correlations between US and international public finance indicators and to forecast international changes from US signals. The model achieves a Mean Squared Error of $2.79$, and its analysis aligns with major volatility episodes such as the 2011 crash, 2013 shutdown, 2015-16 selloff, 2018 crypto crash, and the 2020 pandemic. These results establish a foundation for scenario analysis and risk management for investors and policymakers in a globally interconnected fiscal environment, with future work aimed at real-time deployment and improved interpretability.
Abstract
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.