Forecasting ACB Stock Prices using Machine Learning Models and Vietnamese News Sentiment Analysis
Keywords:Decision Tree, Deep learning, LSTM, Random Forest, Sentiment analysis
This paper presents a study on forecasting the stock close price of ACB bank from 2012 to 2022 using various learning machine models. The models used in this study include Decision Tree, Random Forest, and LSTM, which are combined with sentiment analysis for Vietnamese news using the Pho Bert approach. To evaluate the performance of the models, R2 and RMSE are employed as evaluation metrics. The results indicate that the LSTM model with news sentiment analysis provides the best performance in both evaluation metrics. This study contributes to the understanding of the effectiveness of combining machine learning models with sentiment analysis for forecasting stock prices.
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