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Emotions in hybrid financial markets

Author

Listed:
  • Lorenzo Cominelli
  • Gianluca Rho
  • Caterina Giannetti
  • Federico Cozzi
  • Alberto Greco
  • Graziano A. Manduzio
  • Philipp Chapkovski
  • Michalis Drouvelis
  • Enzo Pasquale Scilingo

Abstract

We investigate whether human traders experience milder emotions when participating in a financial market populated by artificial agents as opposed to a market comprising solely humans. In particular, by manipulating across conditions the number of artificial players, we assess how much emotions vary along with price dynamics (i.e. the occurrence of price bubbles). Notably, to ensure robustness, we evaluate emotions using three distinct methods: self-reporting, physiological responses, and facial expressions. Results show larger bubbles and milder emotional reactions in conditions with a higher count of artificial agents. Furthermore, negative emotions indirectly contribute to the mitigation of price bubbles. Ultimately, we observe a moderate degree of consistency across emotional measurements, with self-reported data being the least consistent among them.

Suggested Citation

  • Lorenzo Cominelli & Gianluca Rho & Caterina Giannetti & Federico Cozzi & Alberto Greco & Graziano A. Manduzio & Philipp Chapkovski & Michalis Drouvelis & Enzo Pasquale Scilingo, 2024. "Emotions in hybrid financial markets," Discussion Papers 2024/311, Dipartimento di Economia e Management (DEM), University of Pisa, Pisa, Italy.
    • Handle: RePEc:pie:dsedps:2024/311
    Note: ISSN 2039-1854
    as

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    References listed on IDEAS

    as
    1. March, Christoph, 2021. "Strategic interactions between humans and artificial intelligence: Lessons from experiments with computer players," Journal of Economic Psychology, Elsevier, vol. 87(C).
    2. John Duffy & M. Ünver, 2006. "Asset price bubbles and crashes with near-zero-intelligence traders," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 27(3), pages 537-563, April.
    3. Smith, Vernon L & Suchanek, Gerry L & Williams, Arlington W, 1988. "Bubbles, Crashes, and Endogenous Expectations in Experimental Spot Asset Markets," Econometrica, Econometric Society, vol. 56(5), pages 1119-1151, September.
    4. Stefan Palan, 2009. "Bubbles and Crashes in Experimental Asset Markets," Lecture Notes in Economics and Mathematical Systems, Springer, number 978-3-642-02147-3, July.
    5. Darren Duxbury & Tommy Gärling & Amelie Gamble & Vian Klass, 2020. "How emotions influence behavior in financial markets: a conceptual analysis and emotion-based account of buy-sell preferences," The European Journal of Finance, Taylor & Francis Journals, vol. 26(14), pages 1417-1438, September.
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    More about this item

    Keywords

    Emotions; Financial Bubbles; Artificial Players;
    All these keywords.

    JEL classification:

    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • 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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