Learning-based stock trending prediction by incorporating technical indicators and social media sentiment

• Main Authors: WANG, Zhaoxia, HU, Zhenda, LI, Fang, HO, Seng-Beng, CAMBRIA, Erik
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2023
• Subjects: Deep learning; Machine learning; Social media sentiment analysis; Stock market trending; Technical indicators; Artificial Intelligence and Robotics; Finance and Financial Management; Numerical Analysis and Scientific Computing; Social Media
• Online Access: https://ink.library.smu.edu.sg/sis_research/7805; https://ink.library.smu.edu.sg/context/sis_research/article/8808/viewcontent/Stock_Trending_Prediction__Submitted_version.pdf
• Institution: Singapore Management University

Stock trending prediction is a challenging task due to its dynamic and nonlinear characteristics. With the development of social platform and artificial intelligence (AI), incorporating timely news and social media information into stock trending models becomes possible. However, most of the existing works focus on classification or regression problems when predicting stock market trending without fully considering the effects of different influence factors in different phases. To address this gap, this research solves stock trending prediction problem utilizing both technical indicators and sentiments of the social media text as influence factors in different situations. A 3-phase hybrid model is proposed where daily sentiment values and technical indicators are considered when predicting the trends of the stocks. The proposed method leverages both traditional learning and deep learning methods as the core predictors in different phases. Accuracy and F1-score are used to evaluate the performance of the proposed method. Incorporating the technical indicators and social media sentiments, the performance of the proposed method with different learning-based methods as core predictors is analyzed and compared in different situations. Specifically, multi-layer perceptron (MLP), naïve bayes (NB), decision tree (DT), logistic regression (LR), random forest (RF), extreme gradient boosting (XGBoost), long short-term memory (LSTM), and convolutional neural networks (CNN) are leveraged as the core learning predictor module, with different combinations of the degree of involvement of technical and sentiment information. The result demonstrates the effectiveness of the proposed method with an accuracy of 73.41% and F1-score of 84.19%. The result also shows that various learning-based methods perform differently for the prediction of different stocks. This research not only demonstrates the merits of the proposed method, it also shows that integrating social opinions with technical indicators is a right direction for enhancing the performance of learning-based stock market trending analysis methods.