Stock Price Analysis with Natural Language Processing and Machine Learning

Stock Price Analysis with Natural Language Processing and Machine Learning

연구 분야: Artificial Intelligence

학회: IAIT '20: Proceedings of the 11th International Conference on Advances in Information Technology

Finding stock price classification based on Thai news corporate is a challenging task. In this research, we try to build machine learning models that capture the relationship of news and stock prices of several companies. In this work, eight companies were selected randomly from Industry Group Index and Sectoral Index. Corporate news articles from the eight selected companies were collected along with their stock prices. Two of traditional machine learning models and two deep learning models were used in this study for comparison purpose. The models were based on Support Vector Machine (SVM), Multilayer Perceptron (MLP), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). Using news articles as inputs, the models were trained to classify stock prices into two classes: Up and Down of the stock closing price. For classification performance, Accuracy, Precision, Recall and F1 were used. The results showed that GRU had highest average accuracy, precision, recall and F1 higher than other model values with 0.79, 0.79, 0.79, 0.79, respectively.