Incorporating stock prices and news sentiments for stock market prediction: A case of Hong Kong |
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| Institution: | 1. Korea Advanced Institute of Science and Technology College of Business, 85 Hoegiro Dongdaemoon-Gu, Seoul 130-722, Republic of Korea;2. Management Information Systems, Dongguk Business School, Dongguk University, 30, Pildong-ro, 1-gil, Jung-gu, Seoul, Republic of Korea;1. School of Software, Shandong University, Jinan 250101, People’s Republic of China;2. School of Information and Electrical Engineering, Ludong University, Yantai 264025, China;3. Shandong Co-Innovation Center of Future Intelligent Computing, Yantai 264025, People’s Republic of China;4. Digital Media Technology Key Lab of Shandong Province, Jinan 250014, People’s Republic of China;1. School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China;2. Southeast Academy of Information Technology, Beijing Institute of Technology, Fujian 351100, China;3. School of Engineering, Westlake University, Zhejiang 310024, China;4. Institute of Advanced Technology, Westlake Institute for Advanced Study, Zhejiang 310024, China |
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| Abstract: | Stock prediction via market data analysis is an attractive research topic. Both stock prices and news articles have been employed in the prediction processes. However, how to combine technical indicators from stock prices and news sentiments from textual news articles, and make the prediction model be able to learn sequential information within time series in an intelligent way, is still an unsolved problem. In this paper, we build up a stock prediction system and propose an approach that 1) represents numerical price data by technical indicators via technical analysis, and represents textual news articles by sentiment vectors via sentiment analysis, 2) setup a layered deep learning model to learn the sequential information within market snapshot series which is constructed by the technical indicators and news sentiments, 3) setup a fully connected neural network to make stock predictions. Experiments have been conducted on more than five years of Hong Kong Stock Exchange data using four different sentiment dictionaries, and results show that 1) the proposed approach outperforms the baselines in both validation and test sets using two different evaluation metrics, 2) models incorporating prices and news sentiments outperform models that only use either technical indicators or news sentiments, in both individual stock level and sector level, 3) among the four sentiment dictionaries, finance domain-specific sentiment dictionary (Loughran–McDonald Financial Dictionary) models the news sentiments better, which brings more prediction performance improvements than the other three dictionaries. |
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