Forecasting stock market indices is crucial for informed decision-making in finance and economics. This research focuses on the VN-Index, incorporating factors like the USD/VND exchange rate, gold prices, the U.S. S&P 500 index, geopolitical risks, and market-specific risks such as volatility and skewness. Utilizing data from January 2001 to April 2024, we employ Lasso regression to address the complexities of predicting the VN-Index due to its susceptibility to local and international economic influences. Our findings reveal significant impacts from the exchange rate and international markets on the VN-Index, where a stronger VND correlates with lower market returns, suggesting challenges for export-oriented sectors. Additionally, increases in geopolitical risks and economic policy uncertainties generally dampen market performance, reflecting investor sensitivity to external uncertainties. The application of Lasso regression, enhanced by its ability to penalize less significant predictors, proves superior to Ordinary Least Squares (OLS) in managing multicollinearity and improving model interpretability and robustness. This study provides a nuanced understanding of the factors influencing the VN-Index. It showcases the effectiveness of advanced statistical techniques in financial modeling, offering substantial insights for investors and policymakers navigating the complexities of emerging markets.