Networks and Imitations in an Agent based Asset Market

Souhir Masmoudi

Abstract

We propose an agent-based approach to analyze the influence of network structure and investors’ mimicking behavior on price dynamics and the market share of agents. We consider a model that involves two different forecasting strategies for investors: chartists and fundamentalists. Investors switch between the two strategies by either copying (a) the strategy used by the most profitable agent in her neighborhood (most profitable rule), or (b) the strategy with the highest average profitability in her neighborhood (average rule). Our results show that the most profitable rule exhibits greater volatility in terms of the fraction of agents using each strategy. This volatility is higher when (i) there are more random links given the size of local neighborhood, and (ii) the size of neighborhood is larger. Because the price volatility increases monotonically with an increase in the proportion of chartists, such volatility in the fraction of strategies used by agents leads to unstable prices.

References

  1. Alfarano, S., and Milakovic, M. (2008). Should Network Structure Matter in agent-Based Finance? Econ. Work. Pap. Christ.-Albrechts-Univ. Kiel Departement Econ.
  2. Arthur, W. B. (1994). Inductive reasoning and bounded rationality. Am. Econ. Rev. 84, 406-411.
  3. Brock, W. A., and Hommes, C. H. (1998). Heterogeneous beliefs and routes to chaos in a simple asset pricing model. J. Econ. Dyn. Control 22, 1235-1274.
  4. Choi, Hui, and Bell (2010). Spatiotemporal Analysis of Imitation Behavior Across New Buyers at an Online Grocery Retailer. J. Mark. Res. 47, 75-89.
  5. Cont, R., and Bouchaud, J.P. (2000). Herd behavior and aggregate fluctuations in financial markets. Macroecon. Dyn. 4, 170-196.
  6. Ellison, G., and Fudenberg, D. (1995). Word-of-mouth communication and social learning. Q. J. Econ. 110, 93-125.
  7. Föllmer, H., Horst, U., and Kirman, A. (2005). Equilibria in financial markets with heterogeneous agents: a probabilistic perspective. J. Math. Econ. 41, 123-155.
  8. Giarratana, M. S., and Mariani, M. (2013). The relationship between knowledge sourcing and fear of imitation. Strat. Manag. J.
  9. Goodwin, C., and Heritage, J. (1990). Conversation Analysis. Annu. Rev. Anthr. 19, 283-307.
  10. Hommes, C. H. (2006). Chapter 23 Heterogeneous Agent Models in Economics and Finance. In Handbook of Computational Economics, (Elsevier), pp. 1109-1186.
  11. Kirman, A. (1993). Ants, rationality, and recruitment. Q. J. Econ. 108, 137-156.
  12. Kirman, A. (2010). Complex Economics: Individual and Collective Rationality (London).
  13. Kirman, A., and Teyssiere, G. (2002). Microeconomic models for long memory in the volatility of financial time series. Stud. Nonlinear Dyn. Econ. 5, 281-302.
  14. LeBaron, B. (2006). Chapter 24 Agent-based Computational Finance. In Handbook of Computational Economics, (Elsevier), pp. 1187-1233.
  15. Levine, J. M., Resnick, L. B., and Higgins, E. tor. (1993). Social Foundations of Cognition. Annu. Rev. Psychol. 44, 585-612.
  16. Lux, T. (1995). Herd Behaviour, Bubbles and Crashes. Econ. J. 105, 881-896.
  17. Lux, T., and Marchesi, M. (1999). Scaling and criticality in a stochastic multi-agent model of a financial market. Nature 397, 498-500.
  18. McKinley, A. E. (1901). The transition from Dutch to English Rule in New York: A study in political imitation. Am. Hist. Rev. 6, 693-724.
  19. Palmer, R. G., Brian Arthur, W., Holland, J.H., LeBaron, B., and Tayler, P. (1994). Artificial economic life: a simple model of a stockmarket. Phys. Nonlinear Phenom. 75, 264-274.
  20. Panchenko, V., Gerasymchuk, S., and Pavlov, O.V. (2013). Asset price dynamics with heterogenous beliefs and local network interactions. J. Econ. Dyn. Control http://dx.doi.org/10.1016/j.jedc.2013.06.015.
  21. Posen, H. E., Lee, J., and Yi, S. (2013). The power of imperfect imitation. Strat. Manag. J. 34, 149-164.
  22. Schlag, K. H. (1998). Why imitate, and if so, How? A boundedly rational approach to multi-armed bandits. J. Econ. Theory 78, 130-156.
  23. Selten, R., and Ostmann, A. (2001). Imitation equilibrium. Homo Oeconomicus 43, 111-149.
  24. Shiller, R. J. (1995). Conversation, information, and herd behavior. Am. Econ. Rev. 85, 181-185.
  25. Shiller, R. J., and Pound, J. (1989). Survey evidence on diffusion of interest and information among investors. J. Econ. Behav. Organ. 12, 47-66.
  26. Tedeschi, G., Iori, G., and Gallegati, M. (2010). Herding effects in order driven markets: The rise and fall of gurus (Department of Economics, City University, London).
  27. Topol, R. (1991). Bubbles and volatility of stock prices: effect of mimetic contagion. Econ. J. 101, 786-800.
  28. Trichet, J.-C. (2010). Reflections on the nature of monetary policy non-standard measures and finance theory. Open. Address ECB Cent. Bank. Conf. Frankf. 18 Novemb. 2010 http://www.ecb.europa.eu/ press/key/date/2010/html/sp101118.en.html.
  29. Watts, D. J., and Strogatz, S. H. (1998). Collective dynamics of “small-world” networks. Nature 393, 440-442.
  30. Zhou (2006). Innovation, Imitation, and new product performance: The case of China. Ind. Mark. Manag. 35.
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Paper Citation


in Harvard Style

Masmoudi S. (2014). Networks and Imitations in an Agent based Asset Market . In Doctoral Consortium - DCAART, (ICAART 2014) ISBN Not Available, pages 56-65


in Bibtex Style

@conference{dcaart14,
author={Souhir Masmoudi},
title={Networks and Imitations in an Agent based Asset Market},
booktitle={Doctoral Consortium - DCAART, (ICAART 2014)},
year={2014},
pages={56-65},
publisher={SciTePress},
organization={INSTICC},
doi={},
isbn={Not Available},
}


in EndNote Style

TY - CONF
JO - Doctoral Consortium - DCAART, (ICAART 2014)
TI - Networks and Imitations in an Agent based Asset Market
SN - Not Available
AU - Masmoudi S.
PY - 2014
SP - 56
EP - 65
DO -