This study aims to evaluate the trend of stock price fluctuations in the Vietnamese banking industry in the period of 2019–2023 by integrating real-world data with quantitative models and modern machine learning techniques. Data is collected from reputable sources such as HOSE, Vietstock and CafeF, including financial indicators (EPS, ROE, P/E, ROA), macroeconomic factors (operating interest rates, exchange rates, foreign cash flows) and market sentiment index (Vietnam VIX). The applied analytical models include linear regression, logistic regression, GARCH, VAR, VaR and Random Forest. The research results show that the VIX index has a clear impact on bank stock prices, and the proposed system is capable of supporting investors in determining the appropriate trading time, classifying stocks according to risk and expected return. The article not only provides a quantitative perspective but also provides an effective visualization tool, contributing to improving the quality of investment decisions in the Vietnamese stock market.