A Smart Approach For Budget Deficits Prediction Under Economic Shocks
DOI:
https://doi.org/10.56830/IJAMS07202403Keywords:
Economic Shocks, Uncertainty, Budget Reliability, Financial Distress Prediction, Artificial Intelligence.Abstract
Historically, the main behavior of fiscal policy is to distribute resources, income, and expenditures, which are interconnected functions of economic stability. Recently, the scope of public sector economics has expanded beyond budgetary components in parallel with the development of public finance. While the budget reached the economic division form of budget items based on program and performance theories, Budget monitoring, and financial risk management are currently challenging, particularly in the face of monetary policy uncertainty. Financial institutions are crucially concerned with the stability of public finance in low-income countries (LICs) as it contributes to improved investor confidence and fiscal decision-making. Hence, economists investigated uncertainty shocks and contributed to managing financial risks with global and energy uncertainty indices. Furthermore, the maturity of digital transformation and artificial intelligence financial applications catalyzed scholars to examine its contributions in the fiscal distress prediction field. Hence, this research aims to integrate artificial intelligence into financial performance analysis to bridge the gap in budget forecasting. The study was aimed at proposing an Economics Division Uncertainty approach (EDUA), which combined (ARIMA) and (LSTM) models for time series analysis of Nuclear Material Authority expenditures over the previous five years divided into quarterly periods, to achieve efficiency in spending. The (ARIMA) model's (ADF) results showed that uncertainty indicators are highly significant. The best (p-value) in the first and second differences in (ARIMA) models is (0.0001) for petroleum items, (0.0001) for solar price rates, (0.001) for the US exchange rate, and (0.003) for electricity price rates, when compared to (EDU_LSTM). Both models have similar accuracy rates, with the best being (EDU_ARIMA) (solar price 97%, USD exchange rate 84%). The second study proposed a composite model of four machine-learning tools to enhance financial performance during financial distress. The study collected (12) indicators from general financial literature and corporate studies, utilizing the (XGBOOST, Random Forest, KNN, and Naïve Bayes) models. Comparing the accuracy results for each model presented different accuracy results in the deep learning models over five years of data. The best accuracy score was for Random Forest at (69%), XGBOOT at (68%), and KNN at (68%). We recommended explainable AI as future research to interpret the budget deficit during the fiscal year period.
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