MONTE CARLO SIMULATIONS FOR ASSESSING THE IMPACT OF MARKET UNCERTAINTY ON INVESTMENT PORTFOLIOS

This study applies Monte Carlo simulations to assess the impact of market uncertainty on investment portfolios from 2020 to 2024. The objective is to evaluate the effectiveness of Monte Carlo methods in modeling financial volatility, analyze investment strategy sensitivity to market fluctuations, and identify key factors influencing portfolio performance. A quantitative research approach was employed, utilizing historical market data from Bloomberg and Reuters. The study generated 10,000 simulations under varying economic conditions, incorporating asset returns, volatility, and correlation factors. Key findings indicate that high market volatility significantly increases risk exposure, with a standard deviation of 15% compared to 3% in stable conditions. Regression analysis revealed a strong negative correlation (r ≈ -0.87, p < 0.05) between inflation and portfolio returns, confirming that inflation is a critical determinant of investment performance. The optimal asset allocation strategy was found to be a 50% stock and 50% bond portfolio, yielding the highest Sharpe ratio of 0.42. The study concludes that Monte Carlo simulations provide a robust framework for forecasting investment risk under uncertain market conditions. It recommends integrating behavioral finance insights and macroeconomic variables into financial models, adopting diversified risk management approaches, and leveraging high-performance computing for more accurate simulations. These findings offer practical implications for investors and policymakers in optimizing portfolio strategies amid financial uncertainty.