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

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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)

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.

Suggested Citation

  • Wai Khuen Cheng & Khean Thye Bea & Steven Mun Hong Leow & Jireh Yi-Le Chan & Zeng-Wei Hong & Yen-Lin Chen, 2022. "A Review of Sentiment, Semantic and Event-Extraction-Based Approaches in Stock Forecasting," Mathematics, MDPI, vol. 10(14), pages 1-20, July.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:14:p:2437-:d:861812
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    References listed on IDEAS

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    1. Ishu Gupta & Tarun Kumar Madan & Sukhman Singh & Ashutosh Kumar Singh, 2022. "HiSA-SMFM: Historical and Sentiment Analysis based Stock Market Forecasting Model," Papers 2203.08143, arXiv.org.
    2. De Long, J Bradford & Andrei Shleifer & Lawrence H. Summers & Robert J. Waldmann, 1990. "Noise Trader Risk in Financial Markets," Journal of Political Economy, University of Chicago Press, vol. 98(4), pages 703-738, August.
    3. Tej Bahadur Shahi & Ashish Shrestha & Arjun Neupane & William Guo, 2020. "Stock Price Forecasting with Deep Learning: A Comparative Study," Mathematics, MDPI, vol. 8(9), pages 1-15, August.
    4. Jiexia Ye & Juanjuan Zhao & Kejiang Ye & Chengzhong Xu, 2020. "Multi-Graph Convolutional Network for Relationship-Driven Stock Movement Prediction," Papers 2005.04955, arXiv.org, revised Oct 2020.
    5. Jireh Yi-Le Chan & Steven Mun Hong Leow & Khean Thye Bea & Wai Khuen Cheng & Seuk Wai Phoong & Zeng-Wei Hong & Yen-Lin Chen, 2022. "Mitigating the Multicollinearity Problem and Its Machine Learning Approach: A Review," Mathematics, MDPI, vol. 10(8), pages 1-17, April.
    6. Ahmet Murat Ozbayoglu & Mehmet Ugur Gudelek & Omer Berat Sezer, 2020. "Deep Learning for Financial Applications : A Survey," Papers 2002.05786, arXiv.org.
    7. Jireh Yi-Le Chan & Steven Mun Hong Leow & Khean Thye Bea & Wai Khuen Cheng & Seuk Wai Phoong & Zeng-Wei Hong & Jim-Min Lin & Yen-Lin Chen, 2022. "A Correlation-Embedded Attention Module to Mitigate Multicollinearity: An Algorithmic Trading Application," Mathematics, MDPI, vol. 10(8), pages 1-13, April.
    8. Verma, Rahul & Verma, Priti, 2007. "Noise trading and stock market volatility," Journal of Multinational Financial Management, Elsevier, vol. 17(3), pages 231-243, July.
    9. Deli Chen & Yanyan Zou & Keiko Harimoto & Ruihan Bao & Xuancheng Ren & Xu Sun, 2019. "Incorporating Fine-grained Events in Stock Movement Prediction," Papers 1910.05078, arXiv.org.
    10. Ronny Luss & Alexandre D'Aspremont, 2015. "Predicting abnormal returns from news using text classification," Quantitative Finance, Taylor & Francis Journals, vol. 15(6), pages 999-1012, June.
    11. Paul C. Tetlock, 2007. "Giving Content to Investor Sentiment: The Role of Media in the Stock Market," Journal of Finance, American Finance Association, vol. 62(3), pages 1139-1168, June.
    12. Wei, Yu-Chen & Lu, Yang-Cheng & Chen, Jen-Nan & Hsu, Yen-Ju, 2017. "Informativeness of the market news sentiment in the Taiwan stock market," The North American Journal of Economics and Finance, Elsevier, vol. 39(C), pages 158-181.
    13. Zeyd Boukhers & Azeddine Bouabdallah & Cong Yang & Jan Jurjens, 2022. "Beyond Trading Data: The Hidden Influence of Public Awareness and Interest on Cryptocurrency Volatility," Papers 2202.08967, arXiv.org, revised Oct 2024.
    14. Fama, Eugene F, 1970. "Efficient Capital Markets: A Review of Theory and Empirical Work," Journal of Finance, American Finance Association, vol. 25(2), pages 383-417, May.
    15. Wentao Xu & Weiqing Liu & Chang Xu & Jiang Bian & Jian Yin & Tie-Yan Liu, 2021. "REST: Relational Event-driven Stock Trend Forecasting," Papers 2102.07372, arXiv.org, revised Feb 2021.
    16. Tim Loughran & Bill Mcdonald, 2011. "When Is a Liability Not a Liability? Textual Analysis, Dictionaries, and 10‐Ks," Journal of Finance, American Finance Association, vol. 66(1), pages 35-65, February.
    17. Dev Shah & Haruna Isah & Farhana Zulkernine, 2019. "Stock Market Analysis: A Review and Taxonomy of Prediction Techniques," IJFS, MDPI, vol. 7(2), pages 1-22, May.
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