Abstract:

This study develops a concise quantitative framework for forecasting Vietnamese stock prices by integrating econometric models with technical and fundamental indicators. Using data from 392 HOSE-listed stocks during 2020–2025 (over 45 million data points), the analysis incorporates Multiple Regression, GARCH(1,1), and VAR, along with MA, MACD, RSI, and valuation ratios. Results show that the banking sector leads overall market movements by 2–3 days, while foreign net buying Granger-causes VN-Index returns and explains 22.4% of their variance. The GARCH(1,1) model confirms persistent volatility clustering (α+β=0.95). Back-testing indicates 74.6% and 81.2% directional accuracy for RSI and MACD, with forecast errors (RMSE/MAE) improving by 12–18% over baseline models. These findings demonstrate that combining econometric and indicator-based analysis enhances short-term prediction and supports data-driven investment decisions in emerging markets.