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Do investors trade too much? A laboratory experiment

Author

Listed:
  • Joao da Gama Batista
  • Domenico Massaro
  • Jean-Philippe Bouchaud
  • Damien Challet
  • Cars Hommes

Abstract

We run experimental asset markets to investigate the emergence of excess trading and the occurrence of synchronised trading activity leading to crashes in the artificial markets. The market environment favours early investment in the risky asset and no posterior trading, i.e. a buy-and-hold strategy with a most probable return of over 600%. We observe that subjects trade too much, and due to the market impact that we explicitly implement, this is detrimental to their wealth. The asset market experiment was followed by risk aversion measurement. We find that preference for risk systematically leads to higher activity rates (and lower final wealth). We also measure subjects' expectations of future prices and find that their actions are fully consistent with their expectations. In particular, trading subjects try to beat the market and make profits by playing a buy low, sell high strategy. Finally, we have not detected any major market crash driven by collective panic modes, but rather a weaker but significant tendency of traders to synchronise their entry and exit points in the market.

Suggested Citation

  • Joao da Gama Batista & Domenico Massaro & Jean-Philippe Bouchaud & Damien Challet & Cars Hommes, 2015. "Do investors trade too much? A laboratory experiment," Papers 1512.03743, arXiv.org.
  • Handle: RePEc:arx:papers:1512.03743
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    Cited by:

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    3. Fotini Economou & Konstantinos Gavriilidis & Bartosz Gebka & Vasileios Kallinterakis, 2022. "Feedback trading: a review of theory and empirical evidence," Review of Behavioral Finance, Emerald Group Publishing Limited, vol. 15(4), pages 429-476, February.
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    5. Ülkü, Numan & Rogers, Madeline, 2018. "Who drives the Monday effect?," Journal of Economic Behavior & Organization, Elsevier, vol. 148(C), pages 46-65.
    6. Taylor, Matthew P. & Wozniak, David, 2018. "Gender differences in asset information acquisition," Journal of Behavioral and Experimental Finance, Elsevier, vol. 20(C), pages 19-29.
    7. Francesco Cordoni, 2022. "Multi-Asset Bubbles Equilibrium Price Dynamics," Papers 2206.01468, arXiv.org, revised Mar 2023.
    8. Kallinterakis, Vasileios & Karaa, Rabaa, 2023. "From dusk till dawn (and vice versa): Overnight-versus-daytime reversals and feedback trading," International Review of Financial Analysis, Elsevier, vol. 85(C).
    9. Wang, Wei & Lan, Yingjie, 2022. "Robust one-way trading with limited number of transactions and heuristics for fixed transaction costs," International Journal of Production Economics, Elsevier, vol. 247(C).
    10. Olena Onishchenko & Numan Ülkü, 2022. "Investor types' trading around the short‐term reversal pattern," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(2), pages 2627-2647, April.

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    More about this item

    JEL classification:

    • C91 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Individual Behavior
    • C92 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Group Behavior
    • D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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