Phản ứng của thị trường chứng khoán trước các bản tin kinh tế vĩ mô - Huỳnh Tấn, Đạt; Từ Hà, Phúc; Đặng Anh, Như (JOURNAL OF ASIAN BUSINESS AND ECONOMIC STUDIES, 2025)
This study examines the VN-Index's response to macroeconomic news through advanced natural language processing (NLP) and machine learning techniques. The authors analyzed 18,253 economic news articles from Cafef.vn, collected from September 1, 2021, to August 31, 2024, to evaluate the impact of 12 key economic factors and overall sentiment. Utilizing Large Language Models (LLMs) such as GPT-4o-mini (for primary sentiment scoring) and Gemini-1.5-flash (for validation with 70% correlation), the authors quantified sentiment and integrated it with VN-Index historical data to build an XGBoost model. The model predicts VN-Index closing prices with an R² of 0.9879, demonstrating superior forecasting accuracy. Explainable AI (XAI) tools like SHAP and LIME were applied to interpret results, identifying “Earnings Reports and Dividends”, “Foreign Investment Policies”, and “Political Stability” as the main drivers of VN-Index volatility. This research confirms the strong impact of macroeconomic news on stock market dynamics, enriches the “noise trader behavior” theory by illustrating category-specific investor reactions, and provides actionable insights for market dynamics in emerging economies like Vietnam, offering investors a transparent AI framework for decision-making.
Keywords: Stock market reaction; Economic news; Sentiment analysis; Large language models; Explainable AI