Stock Price Prediction by Machine Learning Technique: Case Study of Ho Chi Minh Stock Exchange

The stock price is a principal component by investors. Stock prices need to be predicted in determining further stability. Predictive value is particularly challenging in marketing efficiency. This study aims to make precise forecasting. The method used in this study is the Artificial Neural Network (ANN) algorithm. Data processed sourced from the Ho Chi Minh Stock Exchange (HOSE) includes all securities in the VN30 portfolio from 28 July 2000 to 30 July 2021. The results obtained are two layers with an average Root Means Square Error (RMSE) in the training set of 0.185 and 0.21 in the test set. The results of the T-test show that the model is still overfitting.