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Traders’ heterogeneity and bubble-crash patterns in experimental asset markets

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  • Baghestanian, S.
  • Lugovskyy, V.
  • Puzzello, D.

Abstract

We propose a heterogeneous agent model for experimental closed-book call markets with speculators, fundamental and noise traders. We provide structural estimates of the parameters of the model using new experimental data, which allow us to track individual behavior, cognitive reflection abilities, and accuracy of price forecasts. Based on the model's predictions for individual behavior we identify different types of traders in the data. We find that fundamental traders and speculators have higher terminal wealth and perform better on a cognitive reflection test and price forecasting than noise traders. More importantly, we find that all three types of traders are important to understand the mechanics of bubbles and crashes. In the initial period, fundamental traders buy from noise traders. Next, speculators buy from fundamental traders during the boom. Finally, speculators generate the crash by selling to noise traders. Our model predicts smaller bubbles if the cash and asset endowments are higher, keeping the cash-to-asset ratio constant. Our theory has predictive power as we confirm this prediction with additional out-of-sample data.

Suggested Citation

  • Baghestanian, S. & Lugovskyy, V. & Puzzello, D., 2015. "Traders’ heterogeneity and bubble-crash patterns in experimental asset markets," Journal of Economic Behavior & Organization, Elsevier, vol. 117(C), pages 82-101.
  • Handle: RePEc:eee:jeborg:v:117:y:2015:i:c:p:82-101
    DOI: 10.1016/j.jebo.2015.06.007
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    2. Dickinson, David L. & Chaudhuri, Ananish & Greenaway-McGrevy, Ryan, 2017. "Trading While Sleepy? Circadian Mismatch and Excess Volatility in a Global Experimental Asset Market," IZA Discussion Papers 10984, Institute of Labor Economics (IZA).
    3. Neugebauer, Tibor & Shachat, Jason & Szymczak, Wiebke, 2023. "A test of the Modigliani-Miller theorem, dividend policy and algorithmic arbitrage in experimental asset markets," Journal of Banking & Finance, Elsevier, vol. 154(C).
    4. Antoine Kopp & Rebecca Westphal & Didier Sornette, 2022. "Agent-based model generating stylized facts of fixed income markets," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 17(4), pages 947-992, October.
    5. Zhengyang Bao & Andreas Leibbrandt & ple391, 2019. "Thar she resurges: The case of assets that lack positive fundamental value," Monash Economics Working Papers 12-19, Monash University, Department of Economics.
    6. Rebecca Westphal & Didier Sornette, 2019. "Market Impact and Performance of Arbitrageurs of Financial Bubbles in An Agent-Based Model," Swiss Finance Institute Research Paper Series 19-29, Swiss Finance Institute.
    7. Xu, Hai-Chuan & Zhang, Wei & Xiong, Xiong & Wang, Xue & Zhou, Wei-Xing, 2021. "The double-edged role of social learning: Flash crash and lower total volatility," Journal of Economic Behavior & Organization, Elsevier, vol. 182(C), pages 405-420.
    8. Aragón, Nicolás & Roulund, Rasmus Pank, 2020. "Confidence and decision-making in experimental asset markets," Journal of Economic Behavior & Organization, Elsevier, vol. 178(C), pages 688-718.
    9. Dirk-Jan Janssen & Sascha Füllbrunn & Utz Weitzel, 2019. "Individual speculative behavior and overpricing in experimental asset markets," Experimental Economics, Springer;Economic Science Association, vol. 22(3), pages 653-675, September.
    10. Lu, Dong & Zhan, Yaosong, 2022. "Over-the-counter versus double auction in asset markets with near-zero-intelligence traders," Journal of Economic Dynamics and Control, Elsevier, vol. 143(C).
    11. Francesco Cordoni, 2022. "Multi-Asset Bubbles Equilibrium Price Dynamics," Papers 2206.01468, arXiv.org, revised Mar 2023.
    12. Nicolas Eber & Patrick Roger & Tristan Roger, 2024. "Finance and intelligence: An overview of the literature," Journal of Economic Surveys, Wiley Blackwell, vol. 38(2), pages 503-554, April.
    13. Hong, Jieying & Moinas, Sophie & Pouget, Sébastien, 2021. "Learning in speculative bubbles: Theory and experiment," Journal of Economic Behavior & Organization, Elsevier, vol. 185(C), pages 1-26.
    14. Cerruti, Gianluca & Lombardini, Simone, 2022. "Financial bubbles as a recursive process lead by short-term strategies," International Review of Economics & Finance, Elsevier, vol. 82(C), pages 555-568.
    15. David L. Dickinson & Ananish Chaudhuri & Ryan Greenaway-McGrevy, 2020. "Trading while sleepy? Circadian mismatch and mispricing in a global experimental asset market," Experimental Economics, Springer;Economic Science Association, vol. 23(2), pages 526-553, June.
    16. Bao, Zhengyang & Kalaycı, Kenan & Leibbrandt, Andreas & Oyarzun, Carlos, 2020. "Do regulations work? A comprehensive analysis of price limits and trading restrictions in experimental asset markets with deterministic and stochastic fundamental values," Journal of Economic Behavior & Organization, Elsevier, vol. 178(C), pages 59-84.
    17. Westphal, Rebecca & Sornette, Didier, 2020. "Market impact and performance of arbitrageurs of financial bubbles in an agent-based model," Journal of Economic Behavior & Organization, Elsevier, vol. 171(C), pages 1-23.

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