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Using social media mining technology to improve stock price forecast accuracy

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  • Jia‐Yen Huang
  • Jin‐Hao Liu

Abstract

Many stock investors make investment decisions based on stock‐price‐related chip indicators. However, in addition to quantified data, financial news often has a nonnegligible impact on stock price. Nowadays, as new reviews are posted daily on social media, there may be value in using web opinions to improve the performance of stock price prediction. To this end, we use logistic regression to screen the chip indicators and establish a basic stock price prediction model. Then, we employ text mining technology to quantify the unstructured data of social media opinions on stock‐related news into sentiment scores, which are found to correlate significantly with the change extent of the stock price. Based on the findings that the higher the sentiment scores, the lower the prediction accuracy of the logistic regression model, we propose an improved prediction approach that integrates sentiment scores into the logistic regression model. Our results show that the proposed model can improve the prediction accuracy for stock prices, and can thus provide a new reference for investment strategies for stock investors.

Suggested Citation

  • Jia‐Yen Huang & Jin‐Hao Liu, 2020. "Using social media mining technology to improve stock price forecast accuracy," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(1), pages 104-116, January.
    • Handle: RePEc:wly:jforec:v:39:y:2020:i:1:p:104-116
    DOI: 10.1002/for.2616
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    References listed on IDEAS

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    Cited by:

    1. Erdinc Akyildirim & Oguzhan Cepni & Shaen Corbet & Gazi Salah Uddin, 2023. "Forecasting mid-price movement of Bitcoin futures using machine learning," Annals of Operations Research, Springer, vol. 330(1), pages 553-584, November.
    2. Juanjuan Wang & Shujie Zhou & Wentong Liu & Lin Jiang, 2024. "An ensemble model for stock index prediction based on media attention and emotional causal inference," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(6), pages 1998-2020, September.
    3. M. Eren Akbiyik & Mert Erkul & Killian Kaempf & Vaiva Vasiliauskaite & Nino Antulov-Fantulin, 2021. "Ask "Who", Not "What": Bitcoin Volatility Forecasting with Twitter Data," Papers 2110.14317, arXiv.org, revised Dec 2022.
    4. Weiguo Zhang & Xue Gong & Chao Wang & Xin Ye, 2021. "Predicting stock market volatility based on textual sentiment: A nonlinear analysis," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(8), pages 1479-1500, December.
    5. Steven Buigut and Burcu Kapar, 2022. "Do COVID-19 Incidence and Government Intervention Influence Media Indices?," Bulletin of Applied Economics, Risk Market Journals, vol. 9(2), pages 79-100.
    6. Chorong Youn & Hye Jung Jung, 2021. "Semantic Network Analysis to Explore the Concept of Sustainability in the Apparel and Textile Industry," Sustainability, MDPI, vol. 13(7), pages 1-17, March.
    7. Farnoush Ronaghi & Mohammad Salimibeni & Farnoosh Naderkhani & Arash Mohammadi, 2021. "COVID19-HPSMP: COVID-19 Adopted Hybrid and Parallel Deep Information Fusion Framework for Stock Price Movement Prediction," Papers 2101.02287, arXiv.org, revised Jul 2021.

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