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Information dissemination in an experimentally based agent-based stock market

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Abstract

This paper builds an agent-based model to reproduce the results of an experimental stock market that studies how the market aggregates private information. The aim is to use experiments and agent-based modeling to analyze the trading behavior in experimental stock markets. Using the experimental environment and results, it is possible to formulate a hypothesis about the subjects’ behavior and thereby formalize (algorithmically) the trading behavior in an agent-based model. This may lead to a better understanding of how the market converges to an equilibrium and of the mechanism that allows dissemination of private information in the market.

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Notes

  1. A detailed description of the algorithms can be found in Cliff and Bruten (1997). The (Python) code is available on request.

  2. In some experimental sessions the traders could trade more than one asset, but always a given number of assets. This procedure is useful as it simplifies the traders’ decision making and provides a definition of the demand and supply schedule.

  3. \(R_i\) is distributed as a uniform random variable \(U(1,1.05)\) and \(e_i\) is distributed as a uniform random variable U(0,0.05).

  4. \(R_i\) is distributed as a uniform random variable \(U(0.95,1)\) and \(e_i\) is distributed as a uniform random variable U(\(-\)0.05,0).

  5. The value of the parameters are the following: initial value for \(\mu \sim U(0.30,0.45)\) for sellers and \(\mu \sim U(-0.45,-0.30)\) for buyers; \(\beta \sim U(0.1,0.5),\,\gamma \sim U(0.2,0.8)\). During each simulated period each agent will issue an order—if she is active—on average every 20 (simulated) seconds. Each trader has therefore the same given probability to issue an order in each time step. One period lasts 500 s.

  6. Also the Augmented Dickey Fuller test rejects the null-hypothesis of non-stationarity of the price time series.

  7. The author thanks Barner, Feri and Plott for providing the experimental data.

  8. Francs are the experimental currency which will be converted into $ at a fixed rate (1,000 francs = 1$) at the end of the experiment.

  9. The market consists of 22 traders. The code is available on request.

  10. Resulting \(\alpha \) from the first to the fifth minute: 14.6, 18.1, 12.8, 1,414,213,552.47, 1,055,280.66.

  11. All orders below 200 and above 1,000. Of 1,221 orders, only 74 are eliminated. For example there is a sell order of 100 billion francs (this is probably an error) and many buy orders of almost 0.

  12. Period 1: 32.14; period 2: 20.77; period 3: 9.63.

  13. The traders’ private value is distributed as a Normal with expected value equal to the theoretical expected value and variance equal to 5. Note that the variance cannot be computed from the data since the private values are not observable. Different values of the variance influences the volatility of the price around the equilibrium price. The code is available on request.

  14. The code is available on request.

  15. Barner et al. (2005) call it ITRRH: Informed Traders Rat Race Hypothesis.

  16. Note that Fig. 11 shows the only wrong non-compatible order placed by an informed trader in the first minute of a bubble. It comes after two transactions in the wrong direction and just before a non-informed trader accepts an (informed) sell proposal at a price of 575, which actually triggers the bubble.

  17. The condition resulting from the definition of the behavioral hypothesis on the learning mechanism are \(\omega ^{up} > \omega ^{down},\,\omega ^{up}<1\) and \(\omega ^{down}>0\). Given these conditions, different values of the parameters have very little effect on the learning mechanism.

  18. Non-compatibility has a subjective definition since it derives from the comparison between the private value and the price of the order.

  19. In both simulations the learning parameters are \(\omega ^{up} = 0.4\) and \(\omega ^{down}=0.1\). The code is available on request.

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Authors and Affiliations

  1. Institute of Economic Theory and Quantitative Methods, Catholic University of Milan, via Necchi 5, 20123 , Milano, Italy

    Jakob Grazzini

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  1. Jakob Grazzini
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Correspondence to Jakob Grazzini.

Additional information

The research leading to these results has received funding from the European Union, Seventh Framework Programme FP7/2007-2013 under grant agreement n CRISIS-ICT-2011-288501. The author wishes to thank the two anonymous referees for their stimulating comments.

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Grazzini, J. Information dissemination in an experimentally based agent-based stock market. J Econ Interact Coord 8, 179–209 (2013). https://doi.org/10.1007/s11403-013-0109-x

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