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Convergence of trading strategies in continuous double auction markets with boundedly-rational networked traders

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Abstract

This paper considers the convergence of trading strategies among artificial traders connected to one another in a social network and trading in a continuous double auction financial marketplace. Convergence is studied by means of an agent-based simulation model called the Social Network Artificial stoCk marKet model. Six different canonical network topologies (including no-network) are used to represent the possible connections between artificial traders. Traders learn from the trading experiences of their connected neighbours by means of reinforcement learning. The results show that the proportions of traders using particular trading strategies are eventually stable. Which strategies dominate in these stable states depends to some extent on the particular network topology of trader connections and the types of traders.

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Notes

  1. See: SEC runs eye over high-speed trading, Financial Times, July 29, 2009.

  2. AMH is an atempt to reconcile economic theories based on the efficient market hypothesis with behavioral economics and finance. It is derived from evolutionary principles applied to financial interactions: competition, adaptation, and natural selection (Lo 2004).

  3. Community is a set of investors who are heavily connected among themselves, but sparsely connected with other investors (Ozsoylev et al. 2014). There are communities in small-world networks. That is why we chose small-world networks in our experiments and model.

  4. All traders are identical and have the same budget.

  5. In the experiments, L is set to be 1. It means that the experiments only consider one unit time’s experiences of traders.

  6. \(TS_{\omega _5,1}\) is defined in Eq. 17, and \(P_{5,t}^1\) is defined in Eq. 3.

  7. The data used for the scale-free network in Fig. 4 (d) is from (Paparo et al. 2013).

  8. See: Pages 231–314 in Appendix H (Zhang 2015).

  9. See: Pages 231–314 in Appendix H (Zhang 2015).

  10. We only comment on the results for the standard deviations because these values are not visible in Fig. 9.

  11. Again, we only comment on the results for the standard deviations because these values are not visible in Fig. 11.

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Acknowledgements

We thank Ramo Gençay, Erik Theissen, Todd P. White, Dimitrios Koutmos, and other participants at the 2nd International Workshop on “Financial Markets and Nonlinear Dynamics” (FMND) coorganized by Fredj Jawadi in Paris on 4 and 5 June 2015, and the 24th Annual Conference on Pacific Basin Finance, Economics, Accounting, and Management organized by Cheng-Few Lee in Taiwan on 11 and 12 June 2016, and two anonymous referees for their comments and suggestions.

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Authors and Affiliations

  1. School of Economics and Management, Beihang University, Beijing, 100191, China

    Junhuan Zhang

  2. Department of Informatics, King’s College London, London, WC2R 2LS, UK

    Peter McBurney

  3. Data Science Institute, Bournemouth University, Bournemouth, BH12 5BB, UK

    Katarzyna Musial

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  1. Junhuan Zhang
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  2. Peter McBurney
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  3. Katarzyna Musial
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Correspondence to Junhuan Zhang.

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Appendix

Appendix

See Tables 4, 5, 6, 7, 8, 9, 10, 11, 12 and 13.

Table 4 Converged proportions of buyers choosing each trading strategy at the same time (Regular network)
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Table 5 Converged proportions of buyers choosing each trading strategy at the same time (Random network)
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Table 6 Converged proportions of buyers choosing each trading strategy at the same time (Small-world network)
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Table 7 Converged proportions of buyers choosing each trading strategy at the same time (Scale-free network)
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Table 8 Converged proportions of buyers choosing each trading strategy at the same time (Hierarchical network)
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Table 9 Converged proportions of sellers choosing each trading strategy at the same time (Regular network)
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Table 10 Converged proportions of sellers choosing each trading strategy at the same time (Random network)
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Table 11 Converged proportions of sellers choosing each trading strategy at the same time (Small-world network)
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Table 12 Converged proportions of sellers choosing each trading strategy at the same time (Scale-free network)
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Table 13 Converged proportions of sellers choosing each trading strategy at the same time (Hierarchical network)
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Zhang, J., McBurney, P. & Musial, K. Convergence of trading strategies in continuous double auction markets with boundedly-rational networked traders. Rev Quant Finan Acc 50, 301–352 (2018). https://doi.org/10.1007/s11156-017-0631-3

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