A Review of Sentiment, Semantic and Event-Extraction-Based Approaches in Stock Forecasting

Wai Khuen Cheng, Khean Thye Bea, Steven Mun Hong Leow, Jireh Yi-Le Chan, Zeng-Wei Hong and Yen-Lin Chen
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Wai Khuen Cheng: Faculty of Information and Communication Technology, Universiti Tunku Abdul Rahman, Kampar 31900, Malaysia
Khean Thye Bea: Faculty of Business and Finance, Universiti Tunku Abdul Rahman, Kampar 31900, Malaysia
Steven Mun Hong Leow: Faculty of Business and Finance, Universiti Tunku Abdul Rahman, Kampar 31900, Malaysia
Jireh Yi-Le Chan: Faculty of Business and Finance, Universiti Tunku Abdul Rahman, Kampar 31900, Malaysia
Zeng-Wei Hong: Department of Information Engineering and Computer Science, Feng Chia University, Taichung 40724, Taiwan
Yen-Lin Chen: Department of Computer Science and Information Engineering, National Taipei University of Technology, Taipei 10608, Taiwan

Mathematics, 2022, vol. 10, issue 14, 1-20

Abstract: Stock forecasting is a significant and challenging task. The recent development of web technologies has transformed the communication channel to allow the public to share information over the web such as news, social media contents, etc., thus causing exponential growth of web data. The massively available information might be the key to revealing the financial market’s unexplained variability and facilitating forecasting accuracy. However, this information is usually in unstructured natural language and consists of different inherent meanings. Although a human can easily interpret the inherent messages, it is still complicated to manually process such a massive amount of textual data due to the constraint of time, ability, energy, etc. Due to the different properties of text sources, it is crucial to understand various text processing approaches to optimize forecasting performance. This study attempted to summarize and discuss the current text-based financial forecasting approaches in the aspect of semantic-based, sentiment-based, event-extraction-based, and hybrid approaches. Afterward, the study discussed the strength and weakness of each approach, followed with their comparison and suitable application scenarios. Moreover, this study also highlighted the future research direction in text-based stock forecasting, where the overall discussion is expected to provide insightful analysis for future reference.

Keywords: natural language processing; deep learning; stock forecasting; sentiment analysis; event extraction (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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