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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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    1. Jia-Yen Huang, 2014. "Innovative replenishment management for perishable items using logistic regression and grey analysis," International Journal of Business Performance Management, Inderscience Enterprises Ltd, vol. 15(2), pages 138-157.
    2. Liam A. Gallagher & Mark P. Taylor, 2002. "Permanent and Temporary Components of Stock Prices: Evidence from Assessing Macroeconomic Shocks," Southern Economic Journal, John Wiley & Sons, vol. 69(2), pages 345-362, October.
    3. Sanjiv R. Das & Mike Y. Chen, 2007. "Yahoo! for Amazon: Sentiment Extraction from Small Talk on the Web," Management Science, INFORMS, vol. 53(9), pages 1375-1388, September.
    4. Steve Y. Yang & Sheung Yin Kevin Mo & Anqi Liu, 2015. "Twitter financial community sentiment and its predictive relationship to stock market movement," Quantitative Finance, Taylor & Francis Journals, vol. 15(10), pages 1637-1656, October.
    5. G. Rubbaniy & Robel Asmerom & Syed Kumail Abbas Rizvi & Bushra Naqvi, 2014. "Do fear indices help predict stock returns?," Quantitative Finance, Taylor & Francis Journals, vol. 14(5), pages 831-847, May.
    6. Rechenthin, Michael & Street, W. Nick & Srinivasan, Padmini, 2013. "Stock chatter: Using stock sentiment to predict price direction," Algorithmic Finance, IOS Press, vol. 2(3-4), pages 169-196.
    7. Kanas, Angelos & Yannopoulos, Andreas, 2001. "Comparing linear and nonlinear forecasts for stock returns," International Review of Economics & Finance, Elsevier, vol. 10(4), pages 383-398, December.
    8. Zhang, Guoqiang & Eddy Patuwo, B. & Y. Hu, Michael, 1998. "Forecasting with artificial neural networks:: The state of the art," International Journal of Forecasting, Elsevier, vol. 14(1), pages 35-62, March.
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    Cited by:

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    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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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