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An ensemble model for stock index prediction based on media attention and emotional causal inference

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  • Juanjuan Wang
  • Shujie Zhou
  • Wentong Liu
  • Lin Jiang

Abstract

Electronic and digital trading models have made stock trading more accessible and convenient, leading to exponential growth in trading data. With a wealth of trading data available, researchers have found opportunities to extract valuable insights by uncovering patterns in stock price movements and market dynamics. Deep learning models are increasingly being employed for stock price prediction. While neural networks offer superior computational capabilities compared with traditional statistical methods, their results often lack interpretability, limiting their utility in explaining stock price volatility and investment behavior. To address this challenge, we propose a causality‐based method that incorporates a multivariate approach, integrating news event attention sequences and sentiment index sequences. The goal is to capture the intricate and multifaceted relationships among news events, media sentiment, and stock prices. We illustrate the application of this proposed approach using a Global Database of Events, Language, and Tone global event database, demonstrating its benefits through the analysis of attention sequences and media sentiment index sequences for news events across various categories. This research not only identifies promising directions for further exploration but also offers insights with implications for informed investment decisions.

Suggested Citation

  • 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.
  • Handle: RePEc:wly:jforec:v:43:y:2024:i:6:p:1998-2020
    DOI: 10.1002/for.3108
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    1. Calomiris, Charles W. & Mamaysky, Harry, 2019. "How news and its context drive risk and returns around the world," Journal of Financial Economics, Elsevier, vol. 133(2), pages 299-336.
    2. Paul C. Tetlock, 2011. "All the News That's Fit to Reprint: Do Investors React to Stale Information?," The Review of Financial Studies, Society for Financial Studies, vol. 24(5), pages 1481-1512.
    3. Fraiberger, Samuel P. & Lee, Do & Puy, Damien & Ranciere, Romain, 2021. "Media sentiment and international asset prices," Journal of International Economics, Elsevier, vol. 133(C).
    4. Dan Zhu & Qingwei Wang & John Goddard, 2022. "A new hedging hypothesis regarding prediction interval formation in stock price forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(4), pages 697-717, July.
    5. Bhattacharya, Utpal & Galpin, Neal & Ray, Rina & Yu, Xiaoyun, 2009. "The Role of the Media in the Internet IPO Bubble," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 44(3), pages 657-682, June.
    6. Nicky J. Ferguson & Jie Michael Guo & Nicky Herbert Y.T. Lam & Dennis Philip, 2011. "Media Sentiment and UK Stock Returns," Department of Economics Working Papers 2011_06, Durham University, Department of Economics.
    7. Laura L. Veldkamp, 2006. "Media Frenzies in Markets for Financial Information," American Economic Review, American Economic Association, vol. 96(3), pages 577-601, June.
    8. 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.
    9. Dash, Saumya Ranjan & Maitra, Debasish, 2019. "The relationship between emerging and developed market sentiment: A wavelet-based time-frequency analysis," Journal of Behavioral and Experimental Finance, Elsevier, vol. 22(C), pages 135-150.
    10. Gur Huberman & Tomer Regev, 2001. "Contagious Speculation and a Cure for Cancer: A Nonevent that Made Stock Prices Soar," Journal of Finance, American Finance Association, vol. 56(1), pages 387-396, February.
    11. Kim, Y. Han (Andy), 2013. "Self attribution bias of the CEO: Evidence from CEO interviews on CNBC," Journal of Banking & Finance, Elsevier, vol. 37(7), pages 2472-2489.
    12. Laura L. Veldkamp, 2011. "Information Choice in Macroeconomics and Finance," Economics Books, Princeton University Press, edition 1, number 9621.
    13. Brian J. Bushee & John E. Core & Wayne Guay & Sophia J.W. Hamm, 2010. "The Role of the Business Press as an Information Intermediary," Journal of Accounting Research, Wiley Blackwell, vol. 48(1), pages 1-19, March.
    14. Jiang, Minqi & Liu, Jiapeng & Zhang, Lu & Liu, Chunyu, 2020. "An improved Stacking framework for stock index prediction by leveraging tree-based ensemble models and deep learning algorithms," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 541(C).
    15. Paul C. Tetlock & Maytal Saar‐Tsechansky & Sofus Macskassy, 2008. "More Than Words: Quantifying Language to Measure Firms' Fundamentals," Journal of Finance, American Finance Association, vol. 63(3), pages 1437-1467, June.
    16. Aman, Hiroyuki & Moriyasu, Hiroshi, 2022. "Effect of corporate disclosure and press media on market liquidity: Evidence from Japan," International Review of Financial Analysis, Elsevier, vol. 82(C).
    17. Yash Thesia & Vidhey Oza & Priyank Thakkar, 2022. "A dynamic scenario‐driven technique for stock price prediction and trading," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(3), pages 653-674, April.
    18. Diego García, 2013. "Sentiment during Recessions," Journal of Finance, American Finance Association, vol. 68(3), pages 1267-1300, June.
    19. Joseph E. Engelberg & Christopher A. Parsons, 2011. "The Causal Impact of Media in Financial Markets," Journal of Finance, American Finance Association, vol. 66(1), pages 67-97, February.
    20. Leung, Mark T. & Daouk, Hazem & Chen, An-Sing, 2000. "Forecasting stock indices: a comparison of classification and level estimation models," International Journal of Forecasting, Elsevier, vol. 16(2), pages 173-190.
    21. Zhenni Jin & Kun Guo & Yi Sun & Lin Lai & Zhewen Liao, 2020. "The industrial asymmetry of the stock price prediction with investor sentiment: Based on the comparison of predictive effects with SVR," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(7), pages 1166-1178, November.
    22. Roman Kräussl & Elizaveta Mirgorodskaya, 2017. "Media, sentiment and market performance in the long run," The European Journal of Finance, Taylor & Francis Journals, vol. 23(11), pages 1059-1082, September.
    23. Cepoi, Cosmin-Octavian, 2020. "Asymmetric dependence between stock market returns and news during COVID-19 financial turmoil," Finance Research Letters, Elsevier, vol. 36(C).
    24. 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.
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