Informing Policy and Strategy: The Nexus of Macroeconomic Indicators and Financial Metrics in North Macedonia

Abstract

This study employs machine learning and exponential smoothing techniques in Power BI to analyse the relationship between financial indicators and macroeconomic variables in North Macedonia. By rigorously examining correlations and forecasting tools for exponential smoothing, it uncovers a significant relationship between unemployment rates and the financial performance of banks. Positive correlations indicate that higher unemployment rates strengthen banks’ financial resilience, while negative correlations highlight challenges during periods of elevated unemployment. Furthermore, it identifies key influencers affecting the unemployment rate, enhancing forecasting insights. The paper forecasts North Macedonia’s average annual unemployment rate for 2022-2026, complete with a 95% confidence interval. These forecasts empower policymakers, researchers, and stakeholders with precise economic navigation tools. This research is vital for informed decision-making and strategic planning, benefiting policymakers, investors, and financial institutions. It also calls for deeper exploration of macroeconomic and financial metric relationships, promising improved forecasting and decision-making capabilities.

DOI: https://doi.org/10.21863/ijbri/2024.12.2.003

Keywords: Financial Indicators, Macroeconomic Indicators, Exponential Smoothing, Machine Learning, Regression Analysis, Forecasting

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