Analyzing Impact of Economic Indicators on Vietnam Stock Market Using Machine Learning Techniques

Abstract

Many people find interest in stock markets because of the potential financial gains. Understanding the fundamentals of each stock market is crucial as each has its own unique traits and driving factors. Traditionally, statistical methods are commonly used to find the relationship between various economic indicators and the stock markets. This study aims to utilize a different approach, namely machine learning techniques, a widely used tool for data analytics, to analyze the impact of economic indicators on the Vietnam stock index, which is a rising market during the past decade. The investigated machine learning algorithms include tree-based algorithms such as Decision Tree, Random Forest, and XGBoost. Monthly data, totaling 257 observations from August 2000 to December 2021, were used in this study. The results reveal that the XGBoost algorithm achieves the highest accuracy at 96.67% and the five most influential variables affecting the Vietnam stock market are S&P 500 index, consumer price index, exports, imports, and oil price, respectively, all with a positive relationship, while the relationships of the exchange rate, unemployment rate, and GDP with the Vietnam stock market are unclear.