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A theoretical framework for trading experiments


  • Maxence Soumare
  • J{o}rgen Vitting Andersen
  • Francis Bouchard
  • Alain Elkaim
  • Dominique Gu'egan
  • Justin Leroux
  • Michel Miniconi
  • Lars Stentoft


A general framework is suggested to describe human decision making in a certain class of experiments performed in a trading laboratory. We are in particular interested in discerning between two different moods, or states of the investors, corresponding to investors using fundamental investment strategies, technical analysis investment strategies respectively. Our framework accounts for two opposite situations already encountered in experimental setups: i) the rational expectations case, and ii) the case of pure speculation. We consider new experimental conditions which allow both elements to be present in the decision making process of the traders, thereby creating a dilemma in terms of investment strategy. Our theoretical framework allows us to predict the outcome of this type of trading experiments, depending on such variables as the number of people trading, the liquidity of the market, the amount of information used in technical analysis strategies, as well as the dividends attributed to an asset. We find that it is possible to give a qualitative prediction of trading behavior depending on a ratio that quantifies the fluctuations in the model.

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  • Maxence Soumare & J{o}rgen Vitting Andersen & Francis Bouchard & Alain Elkaim & Dominique Gu'egan & Justin Leroux & Michel Miniconi & Lars Stentoft, 2013. "A theoretical framework for trading experiments," Papers 1306.2073,
  • Handle: RePEc:arx:papers:1306.2073

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    References listed on IDEAS

    1. Chavez-Demoulin, V. & Embrechts, P. & Neslehova, J., 2006. "Quantitative models for operational risk: Extremes, dependence and aggregation," Journal of Banking & Finance, Elsevier, vol. 30(10), pages 2635-2658, October.
    2. Bakhodir Ergashev, 2012. "A Theoretical Framework for Incorporating Scenarios into Operational Risk Modeling," Journal of Financial Services Research, Springer;Western Finance Association, vol. 41(3), pages 145-161, June.
    3. Ganegoda, Amandha & Evans, John, 2013. "A scaling model for severity of operational losses using generalized additive models for location scale and shape (GAMLSS)," Annals of Actuarial Science, Cambridge University Press, vol. 7(01), pages 61-100, March.
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    JEL classification:

    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations
    • C0 - Mathematical and Quantitative Methods - - General

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