Applying machine learning algorithms for stock price forecasting

Tóm tắt

The evolution of contemporary civilization is significantly influenced by the stock market. They make it possible to allocate financial resources. Variations in stock prices are a reflection of market movements. Deep learning is frequently employed in the financial industry for tasks including stock market prediction, optimum investing, and financial information processing due to its strong data processing capabilities in many domains. Put financial trading concepts into practice. For this reason, one of the most significant and well-liked professions in the financial industry is stock market prediction. In this research, I suggest using the Transformer - supervised deep learning method for predicting stock price. The supervised deep learning model for stock price prediction problems uses a structure consisting of many encoder layers to create a powerful and flexible system for stock price prediction. In this report, I will report on an experiment on CTG stock (Vietinbank) and some stocks of other banks in the top 4 large banks in Vietnam, which are stocks with a wide range of trading days and use them to try to predict the daily closing price. Experimental results show that my proposed Transformer for time series method can outperform a lot of other prediction algorithms in terms of stock price prediction. In addition, in this thesis, I also propose to build a web application to visualize research results and support users in predicting market stock prices from stock
 transactions at the top 4 banks in Vietnam that are operating in the market today. Experimental results show that the proposed model achieves good results on the data sets used for training and evaluation on all measures: The R-square index measures the model's level of explanation. for the dependent variable and measures include
 Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE).