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Big data, news diversity and financial market crash

Author

Listed:
  • Sabri Boubaker

    (Métis Lab EM Normandie - EM Normandie - École de Management de Normandie, VNU - Vietnam National University [Hanoï])

  • Zhenya Liu

    (CERGAM - Centre d'Études et de Recherche en Gestion d'Aix-Marseille - AMU - Aix Marseille Université - UTLN - Université de Toulon, Renmin University of China = Université Renmin de Chine)

  • Ling Zhai

    (Renmin University of China = Université Renmin de Chine)

Abstract

A vast quantity of high-dimensional, unstructured textual news data is produced every day, more than two decades after the launch of the global Internet. These big data have a significant influence on the way that decisions are made in business and finance, due to the cost, scalability, and transparency benefits that they bring. However, limited studies have fully exploited big data to analyze changes in news diversity or to predict financial market movements, specifically stock market crashes. Based on modern methods of textual analysis, this paper investigates the relationship between news diversity and financial market crashes by applying the change-point detection approach. The empirical analysis shows that (1) big data is a relatively new and useful tool for assessing financial market movements, (2) there is a relationship between news diversity and financial market movements. News diversity tends to decline when the market falls and volatility soars, and increases when the market is on an upward trend and in recovery, and (3) the multiple structural breaks detected improve the ability to forecast stock price movements. Therefore, changes to news diversity, embedded in big data, can be a useful indicator of financial market crashes and recoveries.

Suggested Citation

  • Sabri Boubaker & Zhenya Liu & Ling Zhai, 2021. "Big data, news diversity and financial market crash," Post-Print hal-03511405, HAL.
    • Handle: RePEc:hal:journl:hal-03511405
    DOI: 10.1016/j.techfore.2021.120755
    Note: View the original document on HAL open archive server: https://hal.science/hal-03511405
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    Cited by:

    1. Boubaker, Sabri & Liu, Zhenya & Sui, Tianqing & Zhai, Ling, 2022. "The mirror of history: How to statistically identify stock market bubble bursts," Journal of Economic Behavior & Organization, Elsevier, vol. 204(C), pages 128-147.
    2. Shi, Tao & Li, Chongyang & Wanyan, Hong & Xu, Ying & Zhang, Wei, 2022. "The lending risk predicting of the folk informal financial organization from big data using the deep learning hybrid model," Finance Research Letters, Elsevier, vol. 50(C).
    3. Kanzari, Dalel & Nakhli, Mohamed Sahbi & Gaies, Brahim & Sahut, Jean-Michel, 2023. "Predicting macro-financial instability – How relevant is sentiment? Evidence from long short-term memory networks," Research in International Business and Finance, Elsevier, vol. 65(C).

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    More about this item

    Keywords

    Big data; News diversity; Textual analysis; Change-point; Financial crisis;
    All these keywords.

    JEL classification:

    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • G01 - Financial Economics - - General - - - Financial Crises
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • O16 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Financial Markets; Saving and Capital Investment; Corporate Finance and Governance

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