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Review of Quantitative Finance and Accounting

, Volume 50, Issue 1, pp 301–352 | Cite as

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

  • Junhuan Zhang
  • Peter McBurney
  • Katarzyna Musial
Original Research

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.

Keywords

Market microstructure Agent-based modeling Social networks Investment decisions Automated trading Continuous double auctions Reinforcement learning 

JEL Classification

C73 D44 D47 G02 G11 

Notes

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.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

References

  1. Acemoglu D, Dahleh MA, Lobel I, Ozdaglar A (2011) Bayesian learning in social networks. Rev Econ Stud 78(4):1201–1236CrossRefGoogle Scholar
  2. Albert R, Barabási A (2002) Statistical mechanics of complex networks. Rev Mod Phys 74(1):47CrossRefGoogle Scholar
  3. Alfarano A, Milaković M, Raddant M (2013) A note on institutional hierarchy and volatility in financial markets. Eur J Financ 19(6):449–465CrossRefGoogle Scholar
  4. Arthur WB (2013) Complexity economics: a different framework for economic thought. Working Paper, Santa Fe InstituteGoogle Scholar
  5. Battiston S, Farmer JD, Flache A, Garlaschelli D, Haldane AG, Heesterbeek H, Hommes C, Jaeger C, May R, Scheffer M (2016) Complexity theory and financial regulation. Science 351(6275):818–819CrossRefGoogle Scholar
  6. Brock W, Lakonishok J, LeBaron B (1992) Simple technical trading rules and the stochastic properties of stock returns. J Financ 47:1731–1764CrossRefGoogle Scholar
  7. Chapman M, Tyson G, Atkinson K, Luck M, and McBurney P (2012) Social networking and information diffusion in automated markets. In AMEC & TADA, AAMAS 2012, Valencia, SpainGoogle Scholar
  8. Clauset A, Moore C, Newman MEJ (2008) Hierarchical structure and the prediction of missing links in networks. Nature 453:98–101CrossRefGoogle Scholar
  9. Erdős P, Rényi A (1960) On the evolution of random graphs. Publication of the Mathematical Institute of the Hungarian Academy of Sciences 5:17–61Google Scholar
  10. Farmer JD, Patelli P, Zovko II (2005) The predictive power of zero intelligence in financial markets. PNAS 102(11):2254–2259CrossRefGoogle Scholar
  11. Farmer JD, Foley D (2009) The economy needs agent-based modelling. Nature 460:685–686CrossRefGoogle Scholar
  12. Gjerstad S, Dickhaut J (1998) Price formation in double auctions. Game Econ Behav 22:1–29CrossRefGoogle Scholar
  13. Gjerstad S and Shachat JM (2007) Individual rationality and market efficiency. Working Paper, Purdue UniversityGoogle Scholar
  14. Gode DK, Sunder S (1993) Allocative efficiency of markets with zero-intelligence traders: market as a partial substitute for individual rationality. J Polit Econ 101(1):119–137CrossRefGoogle Scholar
  15. Golub B, Jackson MO (2012) How homophily affects the speed of learning and best-response dynamics. Q J Econ 127:1287–1338CrossRefGoogle Scholar
  16. Hardie I, MacKenzie D (2007) Assembling an economic actor: the agencement of a hedge fund. Sociol Rev 55(1):57–80CrossRefGoogle Scholar
  17. Hendershott T and Riordan R (2009) Algorithmic trading and information. Working paper, University of California, BerkeleyGoogle Scholar
  18. Hendershott T, Jones CM, Menkveld AJ (2011) Does algorithmic trading improve liquidity? J Financ 66(1):1–33CrossRefGoogle Scholar
  19. Jackson MO (2015) The past and future of network analysis in economics. The Oxford Handbook on the Economics of NetworksGoogle Scholar
  20. Klemperer P (2004) Why every economist should learn some auction theory. In Auctions: theory and practice, chapter 2. Princeton University PressGoogle Scholar
  21. Koutmos D (2012) An intertemporal capital asset pricing model with heterogeneous expectations. J Int Financ Mark Inst Money 22(5):1176–1187CrossRefGoogle Scholar
  22. Koutmos D (2015) Is there a positive risk-return tradeoff? A forward-looking approach to measuring the equity premium. Eur Financ Manag 21(5):974–1013CrossRefGoogle Scholar
  23. Ladley D (2012) Zero intelligence in economics and finance. Knowl Eng Rev 27(2):273–286CrossRefGoogle Scholar
  24. Leece RD, White TP (2017) The effects of firms’ information environment on analysts’ herding behavior. Rev Quant Financ Acc 48(2):503–525CrossRefGoogle Scholar
  25. Liao Z, Wang J (2010) Forecasting model of global stock index by stochastic time effective neural network. Expert Syst Appl 37(1):834–841CrossRefGoogle Scholar
  26. Lo AW (2004) The adaptive markets hypothesis: market efficiency from an evolutionary perspective. J Portfolio Manage 30:15–29CrossRefGoogle Scholar
  27. Lux T (1995) Herd behaviour, bubbles and crashes. Econ J 105(431):881–896CrossRefGoogle Scholar
  28. Lux T, Marchesi M (1999) Scaling and criticality in a stochastic multi-agent model of a financial market. Nature 397:498–500CrossRefGoogle Scholar
  29. Malliaris AG, Malliaris M (2013) Are oil, gold and the euro inter-related? Time series and neural network analysis. Rev Quant Financ Acc 40(1):1–14CrossRefGoogle Scholar
  30. McCabe KA, Rassenti SJ, Smith VL (1993) Designing a uniform price double auction: an experimental evaluation. In: Friedman D, Rust J (eds) The Double Auction Market: Institutions, Theories and Evidence, pp 307–332Google Scholar
  31. Miller T, Niu J (2012) An assessment of strategies for choosing between competitive marketplaces. Electron Commer Res Appl 11:14–23CrossRefGoogle Scholar
  32. Niu J ( 2011) Automated Auction Mechanism Design with Competing Marketplaces. PhD thesis, The City University of New YorkGoogle Scholar
  33. Ozsoylev HN, Walden J, Yavuz MD, Bildik R (2014) Investor networks in the stock market. Rev Financ Stud 27(5):1323–1366CrossRefGoogle Scholar
  34. Paparo GD, Müller M, Comellas F, Martin-Delgado MA (2013) Quantum google in a complex network. Sci Rep 3:2773CrossRefGoogle Scholar
  35. Phelps S (2008) Evolutionary Mechanism Design. PhD thesis, University of LiverpoolGoogle Scholar
  36. Smith VL (1962) An experimental study of competitive market behavior. J Polit Econ 70(2):111–137CrossRefGoogle Scholar
  37. Sutton RS, Barto AG (1998) Reinforcement learning: an introduction. MIT Press, CambridgeGoogle Scholar
  38. Tedeschi G, Iori G, Gallegati M (2012) Herding effects in order driven markets: the rise and fall of gurus. J Econ Behav Organ 81:82–96CrossRefGoogle Scholar
  39. Watts DJ, Strogatz SH (1998) Collective dynamics of ‘small-world’ networks. Nature 393:440–442CrossRefGoogle Scholar
  40. West D (2000) Neural network credit scoring models. Comput Oper Res 27:1131–1152CrossRefGoogle Scholar
  41. Wurman PR, Walsh WE, Wellman MP (1998) Flexible double auctions for electronic commerce: theory and implementation. Decis Support Syst 24(1):17–27CrossRefGoogle Scholar
  42. Xiong W (2001) Convergence trading with wealth effects: an amplification mechanism in financial markets. J Financ Econ 62(2):247–292CrossRefGoogle Scholar
  43. Yao J, Li Y, Tan CL (2000) Option price forecasting using neural networks. Omega-Int J Manage S 28:455–466CrossRefGoogle Scholar
  44. Zhang G, Patuwo BE, Hu MY (1998) Forecasting with artificial neural networks: the state of the art. Int J Forecasting 14:35–62CrossRefGoogle Scholar
  45. Zhang J (2015) Trader decision-making based on individual and social learning with competing trading strategies. PhD thesis, King’s College LondonGoogle Scholar
  46. Zhang J, Wang J (2010) Modeling and simulation of the market fluctuations by the finite range contact systems. Simul Model Pract Thoery 18:910–925CrossRefGoogle Scholar
  47. Zhang J, Wang J, Shao J (2010) Finite-range contact process on the market return intervals distributions. Adv Complex Syst 13:643–657CrossRefGoogle Scholar
  48. Zhang Y, Wu L (2009) Stock market prediction of S&P 500 via combination of improved BCO approach and BP neural network. Expert Syst Appl 36(5):8849–8854CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  • Junhuan Zhang
    • 1
  • Peter McBurney
    • 2
  • Katarzyna Musial
    • 3
  1. 1.School of Economics and ManagementBeihang UniversityBeijingChina
  2. 2.Department of InformaticsKing’s College LondonLondonUK
  3. 3.Data Science InstituteBournemouth UniversityBournemouthUK

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