This study undertakes a comprehensive analysis of Vietnam’s VN-Index by employing advanced econometric techniques, including Lasso regression and Ordinary Least Squares (OLS), to forecast market indices amidst the intricate challenges posed by both global and domestic economic conditions. Central to the analysis are the influences of crude oil prices, gold prices, the S&P 500 index, and uncertainties stemming from geopolitical and monetary policies. Each of these factors exhibits complex interactions with the VN-Index, creating significant challenges in predicting market movements due to their multifaceted and dynamic impacts. For instance, fluctuations in oil and gold prices serve as broader economic indicators, which can exert varying effects on market indices depending on the prevailing economic environment. Similarly, shifts in the U.S. stock market, alongside uncertainties such as Geopolitical Risk (GPR) and Monetary Policy Uncertainty (MPU), play a critical role in shaping market sentiment and investment behavior, particularly in emerging markets like Vietnam. Utilizing an extensive dataset spanning January 2001 to April 2024, this study demonstrates that Lasso regression, renowned for its ability to address multicollinearity and simplify predictive models by penalizing less significant variables, surpasses OLS in enhancing the accuracy and interpretability of market forecasts. These findings provide valuable insights into the interdependencies of financial indicators and highlight the efficacy of advanced econometric approaches in navigating and predicting complex market dynamics.