Short-Term Stock Price Prediction Based on Single and Stacking Machine Learning Models

As the investment environment improves, people are increasingly eager to purchase financial products with idle funds. Securities companies have become the preferred choice for buying financial products. The current accuracy of stock predictions relies on the comprehensive models utilized by each securities firm, including the stock market trading model, the data model, and the stock pricing model. However, securities companies have not adequately explored a suitable single model for stock predictions and have rarely assessed the effectiveness of stacking and ensemble models to enhance these predictions. JEL Codes: G17, C53, G11 Keywords: Long short-term memory, random forest model, stacking model, stock prediction, support vector machine, XGBoost model.