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A Bayesian approach to experimental analysis: trading in a laboratory financial market

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  • Marco Cipriani

    ()

  • Riccardo Costantini

    ()

  • Antonio Guarino

    ()

Abstract

We employ a Bayesian approach to analyze financial markets experimental data. We estimate a structural model of sequential trading in which trading decisions are classified in five types: private-information based, noise, herd, contrarian and irresolute. Through Monte Carlo simulation, we estimate the posterior distributions of the structural parameters. This technique allows us to compare several non-nested models of trade arrival. We find that the model best fitting the data is that in which a proportion of trades stems from subjects who do not rely only on their private information once the difference between the number of previous buy and sell decisions is at least two. In this model, the majority of trades stem from subjects following their private information. There is also a large proportion of noise trading activity, which is biased towards buying the asset. We observe little herding and contrarianism, as theory suggests. Finally, we observe a significant proportion of (irresolute) subjects who follow their own private information when it agrees with public information, but abstain from trading when it does not. Copyright Springer-Verlag 2012

Suggested Citation

  • Marco Cipriani & Riccardo Costantini & Antonio Guarino, 2012. "A Bayesian approach to experimental analysis: trading in a laboratory financial market," Review of Economic Design, Springer;Society for Economic Design, vol. 16(2), pages 175-191, September.
  • Handle: RePEc:spr:reecde:v:16:y:2012:i:2:p:175-191
    DOI: 10.1007/s10058-012-0124-8
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    References listed on IDEAS

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    1. Avery, Christopher & Zemsky, Peter, 1998. "Multidimensional Uncertainty and Herd Behavior in Financial Markets," American Economic Review, American Economic Association, vol. 88(4), pages 724-748, September.
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    Cited by:

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    2. Rolando Gonzales & Gabriela Aguilera-Lizarazu & Andrea Rojas-Hosse & Patricia Aranda, 2016. "Preference for women but less preference for indigenous women: A lab-field experiment of loan discrimination in a developing economy," Working Papers PIERI 2016-24, PEP-PIERI.
    3. Rolando Gonzales Martínez & Gabriela Aguilera‐Lizarazu & Andrea Rojas‐Hosse & Patricia Aranda Blanco, 2020. "The interaction effect of gender and ethnicity in loan approval: A Bayesian estimation with data from a laboratory field experiment," Review of Development Economics, Wiley Blackwell, vol. 24(3), pages 726-749, August.
    4. Nicolas Vallois & Dorian Jullien, 2017. "Estimating Rationality in Economics: A History of Statistical Methods in Experimental Economics," Working Papers halshs-01651070, HAL.
    5. Nicolas Vallois & Dorian Jullien, 2017. "Estimating Rationality in Economics: A History of Statistical Methods in Experimental Economics," GREDEG Working Papers 2017-20, Groupe de REcherche en Droit, Economie, Gestion (GREDEG CNRS), Université Côte d'Azur, France.
    6. Seuk Yen Phoong, 2013. "Rubber Price Effect on Exchange Rate: A Bayesian Mixture Model Approach," Information Management and Business Review, AMH International, vol. 5(6), pages 263-269.

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    More about this item

    Keywords

    Experimental economics; Herd behavior; Contrarian behavior; Bayesian methods; C92; D8; G14;
    All these keywords.

    JEL classification:

    • C92 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Group Behavior
    • D8 - Microeconomics - - Information, Knowledge, and Uncertainty
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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