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Sentiment and Financial Market Connectedness: The Role of Investor Happiness

Author

Listed:
  • Elie Bouri

    (Holy Spirit University of Kaslik, USEK Business School, Jounieh, Lebanon)

  • Riza Demirer

    (Department of Economics and Finance, Southern Illinois University Edwardsville, Edwardsville, USA)

  • David Gabauer

    (Institute of Applied Statistics, Johannes Kepler University, Linz, Austria)

  • Rangan Gupta

    (Department of Economics, University of Pretoria, Pretoria, South Africa)

Abstract

We examine the relationship between investor sentiment and connectedness patterns across global stock markets within a quantile-on-quantile framework. Our findings show that investor happiness, built on Twitter feed data as a proxy for investor sentiment, has a significant effect on both the return and volatility spillovers across major global stock markets. While the sentiment effect is found to be relatively stronger on volatility spillovers, we observe that the relationship between sentiment and connectedness is asymmetric for return and volatility connectedness and displays quantile specific patterns with distinctly different effects observed for sentiment shocks. The findings suggest that both investors and policy makers should be particularly vigilant against sentiment shocks, in either direction, as these shocks can have significant risk effects, contributing to volatility spillovers globally.

Suggested Citation

  • Elie Bouri & Riza Demirer & David Gabauer & Rangan Gupta, 2020. "Sentiment and Financial Market Connectedness: The Role of Investor Happiness," Working Papers 202022, University of Pretoria, Department of Economics.
  • Handle: RePEc:pre:wpaper:202022
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    References listed on IDEAS

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    1. Niţoi, Mihai & Pochea, Maria Miruna, 2022. "The nexus between bank connectedness and investors’ sentiment," Finance Research Letters, Elsevier, vol. 44(C).

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    More about this item

    Keywords

    Advanced Equity Markets; Returns and Volatility; TVP-VAR; Dynamic Connectedness; Investor Happiness; Quantile-on-Quantile Regression;
    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
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

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