Artificial Intelligence-based Analysis of Change in Public Finance between US and International Markets

ArXiv ID: 2403.18823 “View on arXiv”

Authors: Unknown

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.

Keywords: Public Finances, Neural Networks, International Correlation, Volatility Prediction

Complexity vs Empirical Score

  • Math Complexity: 3.5/10
  • Empirical Rigor: 4.0/10
  • Quadrant: Philosophers
  • Why: The paper uses basic neural network architecture (LSTM, dense layers) without advanced mathematical derivations or novel theory, while the empirical validation relies on a single MSE metric (2.79) and qualitative historical comparisons without robust backtesting or implementation details.
  flowchart TD
    A["<b>Research Goal</b><br>Model & Predict International Public Finance<br>Changes based on US Market"] --> B["<b>Methodology</b><br>Neural Network Model"]
    B --> C["<b>Data Inputs</b><br>US & International<br>Public Finance Data"]
    C --> D["<b>Computational Process</b><br>Training Network &<br>Optimizing (MSE: 2.79)"]
    D --> E["<b>Validation</b><br>Economic Analysis vs.<br>Historical Stock Market"]
    E --> F["<b>Key Findings/Outcomes</b><br>Significant US-Intl Correlation<br>Predictive Model for Investors"]