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Investor sentiment and multi-scale positive and negative stock market bubbles in a panel of G7 countries

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  • van Eyden, Reneé
  • Gupta, Rangan
  • Nielsen, Joshua
  • Bouri, Elie

Abstract

Firstly, we use the log-periodic power law singularity multi-scale confidence indicator (LPPLS-CI) approach to detect both positive and negative bubbles in the short-, medium- and long-term stock market indices of the G7 countries. Secondly, we apply heterogeneous coefficients panel data-based regressions to analyse the impact of investor sentiment, proxied by business and consumer confidence indicators, on the indicators of bubbles of the G7. Controlling for the impacts of output growth, inflation, monetary policy, stock market volatility, and growth in trading volumes, we find that investor sentiment increases the positive and reduces the negative LPPLS-CIs, primarily at the medium- and long-term scales for the G7, considered together, with the result being driven by at least five of the seven countries. Our results have important implications for both investors and policymakers, as the collapse (improvement) of investor sentiment can lead to a crash (recovery) in a bull (bear) market.

Suggested Citation

  • van Eyden, Reneé & Gupta, Rangan & Nielsen, Joshua & Bouri, Elie, 2023. "Investor sentiment and multi-scale positive and negative stock market bubbles in a panel of G7 countries," Journal of Behavioral and Experimental Finance, Elsevier, vol. 38(C).
    • Handle: RePEc:eee:beexfi:v:38:y:2023:i:c:s2214635023000187
    DOI: 10.1016/j.jbef.2023.100804
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    Cited by:

    1. Foglia, Matteo & Miglietta, Federica, 2024. "Does every cloud (bubble) have a silver lining? An investigation of ESG financial markets," Journal of Behavioral and Experimental Finance, Elsevier, vol. 42(C).
    2. Demirer, Riza & Gabauer, David & Gupta, Rangan & Nielsen, Joshua, 2024. "Gold, platinum and the predictability of bubbles in global stock markets," Resources Policy, Elsevier, vol. 90(C).
    3. Oguzhan Cepni & Rangan Gupta & Jacobus Nel & Joshua Nielsen, 2023. "Monetary Policy Shocks and Multi-Scale Positive and Negative Bubbles in an Emerging Country: The Case of India," Working Papers 202305, University of Pretoria, Department of Economics.
    4. Gupta, Rangan & Nielsen, Joshua & Pierdzioch, Christian, 2024. "Stock market bubbles and the realized volatility of oil price returns," Energy Economics, Elsevier, vol. 132(C).
    5. Huang, Leping & Zhang, Kuo & Wang, Jingxin & Zhu, Yingfu, 2023. "Examining the interplay of green bonds and fossil fuel markets: The influence of investor sentiments," Resources Policy, Elsevier, vol. 86(PA).
    6. Riza Demirer & David Gabauer & Rangan Gupta & Joshua Nielsen, 2023. "Gold-to-Platinum Price Ratio and the Predictability of Bubbles in Financial Markets," Working Papers 202317, University of Pretoria, Department of Economics.
    7. Dettoni, Robinson & Gil-Alana, Luis A. & Yaya, OlaOluwa S., 2024. "Stock market prices and Dividends in the US: Bubbles or Long-run equilibria relationships?," International Review of Financial Analysis, Elsevier, vol. 94(C).
    8. Rangan Gupta & Jacobus Nel & Joshua Nielsen & Christian Pierdzioch, 2023. "Stock Market Volatility and Multi-Scale Positive and Negative Bubbles," Working Papers 202310, University of Pretoria, Department of Economics.
    9. Renee van Eyden & Rangan Gupta & Xin Sheng & Joshua Nielsen, 2023. "Predicting Multi-Scale Positive and Negative Stock Market Bubbles in a Panel of G7 Countries: The Role of Oil Price Uncertainty," Working Papers 202332, University of Pretoria, Department of Economics.
    10. Tzika, Paraskevi & Pantelidis, Theologos, 2024. "Economic policy uncertainty as an indicator of abrupt movements in the US stock market," The Quarterly Review of Economics and Finance, Elsevier, vol. 94(C), pages 93-103.

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    More about this item

    Keywords

    Multi-scale bubbles and crashes; Investor sentiment; Business and consumer confidence; Panel regressions; G7 stock markets;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • G41 - Financial Economics - - Behavioral Finance - - - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making in Financial Markets

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