A thorough examination of factors impacting the VN-Index is presented. This study highlights the strong effect of the exchange rate (rex), the Geopolitical Risk Index (GPR), the Global Economic Policy Uncertainty Index (GEPU), and specific market risks like volatility (vvni) and skewness (skvni). Lasso regression was used to handle problems like multicollinearity and heteroscedasticity, improving the model's strength and prediction reliability. Important findings show a negative relationship between a stronger VND against the USD and the VN-Index. This stresses the importance of the exchange rate for managing market risks and its effect on industries that rely on foreign investment. Also, increases in GPR and GEPU are linked to drops in the VN-Index. This shows how political and policy uncertainties can negatively affect market feelings, especially in growing markets. The model effectively shows the impact of specific market risks, providing insights into how likely the VN-Index is to have sudden drops. The use of Lasso regression was very important. The regularization parameter lambda was carefully selected to make the model simpler and prevent overfitting, while keeping good prediction accuracy. This study adds to the understanding of VN-Index changes and provides a useful way to analyze similar financial markets. This information is valuable for investors and policymakers who want to understand the complexities of the Vietnamese stock market.