Stock market prediction analysis by incorporating social and news opinion and sentiment

• Main Authors: WANG, Zhaoxia, HO, Seng-Beng, LIN, Zhiping
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2019
• Subjects: enhanced learning-based method; Machine learning; sentiment analysis; stock market prediction; time series data prediction; Numerical Analysis and Scientific Computing; Portfolio and Security Analysis
• Online Access: https://ink.library.smu.edu.sg/sis_research/5482; https://ink.library.smu.edu.sg/context/sis_research/article/6485/viewcontent/Stock_Mkt_Prediction_pv.pdf
• Institution: Singapore Management University

The price of the stocks is an important indicator for a company and many factors can affect their values. Different events may affect public sentiments and emotions differently, which may have an effect on the trend of stock market prices. Because of dependency on various factors, the stock prices are not static, but are instead dynamic, highly noisy and nonlinear time series data. Due to its great learning capability for solving the nonlinear time series prediction problems, machine learning has been applied to this research area. Learning-based methods for stock price prediction are very popular and a lot of enhanced strategies have been used to improve the performance of the learning based predictors. However, performing successful stock market prediction is still a challenge. News articles and social media data are also very useful and important in financial prediction, but currently no good method exists that can take these social media into consideration to provide better analysis of the financial market. This paper aims to successfully predict stock price through analyzing the relationship between the stock price and the news sentiments. A novel enhanced learning-based method for stock price prediction is proposed that considers the effect of news sentiments. Compared with existing learning-based methods, the effectiveness of this new enhanced learning-based method is demonstrated by using the real stock price data set with an improvement of performance in terms of reducing the Mean Square Error (MSE). The research work and findings of this paper not only demonstrate the merits of the proposed method, but also points out the correct direction for future work in this area.