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The value of information in a multi-agent market model

Author

Listed:
  • Toth, Bence
  • Scalas, Enrico
  • Huber, Juergen
  • Kirchler, Michael

Abstract

We present an experimental and simulated model of a multi-agent stock market driven by a double auction order matching mechanism. Studying the effect of cumulative information on the performance of traders, we find a non monotonic relationship of net returns of traders as a function of information levels, both in the experiments and in the simulations. Particularly, averagely informed traders perform worse than the non informed and only traders with high levels of information (insiders) are able to beat the market. The simulations and the experiments reproduce many stylized facts of stock markets, such as fast decay of autocorrelation of returns, volatility clustering and fat-tailed distribution of returns. These results have an important message for everyday life. They can give a possible explanation why, on average, professional fund managers perform worse than the market index.

Suggested Citation

  • Toth, Bence & Scalas, Enrico & Huber, Juergen & Kirchler, Michael, 2006. "The value of information in a multi-agent market model," MPRA Paper 341, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:341
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    2. Robin Nicole & Aleksandra Alori'c & Peter Sollich, 2020. "Fragmentation in trader preferences among multiple markets: Market coexistence versus single market dominance," Papers 2012.04103, arXiv.org, revised Aug 2021.
    3. Jacobovic, Royi & Levy, Yehuda John & Solan, Eilon, 2026. "Bayesian games with nested information," Theoretical Economics, Econometric Society, vol. 21(1), January.
    4. Kirchler, Michael, 2010. "Partial knowledge is a dangerous thing - On the value of asymmetric fundamental information in asset markets," Journal of Economic Psychology, Elsevier, vol. 31(4), pages 643-658, August.
    5. Andreas Gronlund & Il Gu Yi & Beom Jun Kim, 2012. "Fractal Profit Landscape of the Stock Market," Papers 1205.0505, arXiv.org.
    6. James T. Wilkinson & Jacob Kelter & John Chen & Uri Wilensky, 2024. "A Network Simulation of OTC Markets with Multiple Agents," Papers 2405.02480, arXiv.org.
    7. Mathieu, Philippe & Morvan, Rémi, 2019. "A deterministic behaviour for realistic price dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 33-49.
    8. Kirchler, Michael & Huber, Jürgen, 2009. "An exploration of commonly observed stylized facts with data from experimental asset markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(8), pages 1631-1658.
    9. Scalas, Enrico & Rapallo, Fabio & Radivojević, Tijana, 2017. "Low-traffic limit and first-passage times for a simple model of the continuous double auction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 485(C), pages 61-72.
    10. Andreas Grönlund & Il Gu Yi & Beom Jun Kim, 2012. "Fractal Profit Landscape of the Stock Market," PLOS ONE, Public Library of Science, vol. 7(4), pages 1-5, April.
    11. Aleksandra Alorić & Peter Sollich & Peter McBurney & Tobias Galla, 2016. "Emergence of Cooperative Long-Term Market Loyalty in Double Auction Markets," PLOS ONE, Public Library of Science, vol. 11(4), pages 1-26, April.

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    Keywords

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    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • C00 - Mathematical and Quantitative Methods - - General - - - General
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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