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Employing Google Trends and Deep Learning in Forecasting Financial Market Turbulence

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  • Anastasios Petropoulos
  • Vasileios Siakoulis
  • Evangelos Stavroulakis
  • Panagiotis Lazaris
  • Nikolaos Vlachogiannakis

Abstract

In this paper we apply text mining methodologies on a set of 10,000 Central Bank speeches to construct a financial dictionary, based on which we use Google Trends indices to measure people’s interest in financial news. Particularly, we investigate the relationship between these indices and financial market turbulence leveraging on Deep Learning techniques, which are benchmarked against a variety of Machine Learning algorithms and traditional statistical techniques. Our main finding is that Google queries convey information able to predict future market turbulence in a short time period (one month), and that Deep Learning algorithms clearly outperform over benchmark techniques. Google Trends can provide useful input in the creation of crisis Early Warning Systems, as social data are more responsive compared to official financial indicators, which are usually available with a lag of several weeks or months. Thus, such an Early Warning System (EWS) that is continuously updated with current social data can be a valuable tool for policymakers, as it can immediately identify signs of whether a crisis is imminent or not.

Suggested Citation

  • Anastasios Petropoulos & Vasileios Siakoulis & Evangelos Stavroulakis & Panagiotis Lazaris & Nikolaos Vlachogiannakis, 2022. "Employing Google Trends and Deep Learning in Forecasting Financial Market Turbulence," Journal of Behavioral Finance, Taylor & Francis Journals, vol. 23(3), pages 353-365, July.
    • Handle: RePEc:taf:hbhfxx:v:23:y:2022:i:3:p:353-365
    DOI: 10.1080/15427560.2021.1913160
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    Cited by:

    1. Guillaume Belly & Lukas Boeckelmann & Carlos Mateo Caicedo Graciano & Alberto Di Iorio & Klodiana Istrefi & Vasileios Siakoulis & Arthur Stalla‐Bourdillon, 2023. "Forecasting sovereign risk in the Euro area via machine learning," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(3), pages 657-684, April.
    2. Tang, Pan & Xu, Wei & Wang, Haosen, 2024. "Network-Based prediction of financial cross-sector risk spillover in China: A deep learning approach," The North American Journal of Economics and Finance, Elsevier, vol. 72(C).
    3. Yuqian Zhang, 2023. "Using Google Trends to track the global interest in International Financial Reporting Standards: Evidence from big data," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 30(2), pages 87-100, April.

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