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Programmed Trading Agents and Market Microstructure in an Artificial Futures Market

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Part of the Evolutionary Economics and Social Complexity Science book series (EESCS,volume 28)

Abstract

This chapter is organized as follows: first, it introduces how U-Mart, an artificial futures market testbed, is used at a graduate school of engineering to teach economics/financial markets as well as computer programming and system modeling. Second, it reports a strategy experiment with human subjects and their submitted trading agents by focusing on market microstructure to see the relations between the evolution of their trading strategy and the characteristics of order book. The empirical results confirm that although there are locally mispricing effects of several trading agents; in most cases, market liquidity improves. Future perspectives of related fields are also discussed.

Keywords

  • Teaching economics in classroom
  • Programming education
  • Artificial market
  • Trading contest
  • Market microstructure

This work has been revised and extended from the earlier version presented in 2008 Winter Workshop on Economics with Heterogeneous Interacting Agents (WEHIA 2008), in Taoyuan, Taiwan on December 5–7, 2008.

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Notes

  1. 1.

    A wide variety of contributions are found in https://www.economicsnetwork.ac.uk/themes/games.

  2. 2.

    For example, https://www.howthemarketworks.com/.

  3. 3.

    https://scratch.mit.edu/.

  4. 4.

    https://www.springin.org/.

  5. 5.

    https://www.viscuit.com/.

  6. 6.

    https://developers.google.com/blockly.

  7. 7.

    For more information, see official website (U-Mart project: http://www.u-mart.org/) or the book (Shiozawa et al. 2008).

  8. 8.

    Other strategy experiments in experimental economics include Brandts and Charness (2011), Linde et al. (2014) and Zhao et al. (2018), for example.

  9. 9.

    Bao et al. and Sunder give a comprehensive survey in this field (Bao et al. 2021; Sunder 1992).

  10. 10.

    The other courses are “Adaptive System,” “Discrete System,” and “Dynamic System.”

  11. 11.

    They were as follows: one trend follower, one contrarian, two random walkers, two RSI traders, two moving average strategies, one arbitrager (he/she focuses on the spread between spot price and futures price), and one stop loss trader. For more details, see the textbook (Shiozawa et al. 2008).

  12. 12.

    Yamada et al. analyze this in greater detail (Yamada et al. 2008).

  13. 13.

    For more details, please refer to comprehensive surveys (Biais et al. 2005; Madhavan 2000) and an empirical study for Tokyo Stock Exchange (Ahn et al. 2002).

  14. 14.

    The agglomeration method was “ward.D2” in R.

  15. 15.

    Before obtaining the dendrogram, the author extracted several trading agents as outliers because the distance is quite large. They are as follows: 1–6, 1–30, 1–50, 1–51, 1–76, and 1–80 in Round 1 and 2–6, 2–30, and 2–80 in Round 2.

  16. 16.

    In computer simulations, the agents of Cluster 1 submitted buy orders more often than those of Clusters 2 to 5, whereas those of Clusters 2 and 3 would not submit sell orders compared to those of Clusters 1, 4, and 5.

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Yamada, T. (2022). Programmed Trading Agents and Market Microstructure in an Artificial Futures Market. In: Aruka, Y. (eds) Digital Designs for Money, Markets, and Social Dilemmas. Evolutionary Economics and Social Complexity Science, vol 28. Springer, Singapore. https://doi.org/10.1007/978-981-19-0937-5_12

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