Abstract
We study the relationship between communication network topologies, namely the small-world networks introduced by Watts and Strogatz, and the simulation results of an artificial stock market, here the Frankfurt Artificial Stock Market. Heterogeneous interacting agents communicate their success and trading strategy to their nearest neighbors. A process of information diffusion arises through the adaptive behavior of agents when encountering more successful strategies in their direct neighborhood. We will show that an increasing rewiring probability of the small-world network will lead to higher volatility and distortion within our simulation model. It seems probable that the spatial position of traders within a communication network affects the price building process.
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References
Arthur W, Holland J, LeBaron B, Palmer R, Taylor P (1997) Asset pricing under endogenous expectations in an artificial stock market. In: Arthur W, Durlauf S, Lane D (eds) The economy as an evolving complex system II. Addison-Wesley, Reading
Bak P, Paczuski M and Shubik M (1997). Price variations in a stock market with many agents. Physica A 246: 430–453
Bikhchandani S, Sharma S (2001) Herd behavior in financial markets. IMF Staff Papers, vol 47, no. 3
Cont R (2001). Empirical properties of asset returns: stylized facts and statistical issues. Quant Finance 1: 223–236
Cont R and Bouchaud J-P (2000). Herd behavior and aggregate fluctuations in financial markets. Macroecon Dyn 4: 170–196
Dorogovtsev S and Mendes J (2003). Evolution of networks, from biological nets to the Internet and WWW. Oxford University Press, Oxford
Erdös P and Renyi A (1959). On random graphs. Publ Math Debrecen 6: 290
Hommes C (2006) Heterogeneous agent models in economics and finance. In: Tesfatsion L, Judd K (eds) Handbook of computational economics, vol 2. Elsevier, Amsterdam
Kermack WO and McKendrick AG (1927). A contribution to the mathematical theory of epidemics. Proc R Soc Lond A 115: 700–721
Levy H, Levy M and Solomon S (2000). Microscopic simulations of financial markets. Academic Press, New York
Lux T and Marchesi M (2000). Volatility clustering in financial markets: a microsimulation of interacting agents. Int J Theor Appl Finance 3(4): 675–702
Lux T and Marchesi M (1999). Scaling and criticality in a stochastic multi-agent model of a financial market. Nature 397(11): 498–500
Newman MEJ (2000). Models of the small world. J Stat Phys 101: 819–841
Newman MEJ, Barabasi A-L and Watts DJ (2006). Structure and dynamics of networks. Princeton University Press, Princeton
Strogatz SH (2001). Complex networks. Nature 410: 268–276
Walsh T (1999) Search in a small world, In: Dean T (ed) Proceedings of the 16th International Joint Conference on Artificial Intelligence. Morgan, Kaufmann
Wang FX and Chen G (2003). Networks: small-world, scale-free and beyond. IEEE Circuits Syst Mag 1: 6–20
Watts DJ (2003). Six Degrees: the science of a connected age. W. W. Norton, New York
Watts DJ and Strogatz SH (1998). Collective dynamics of small-world networks. Nature 393: 440–442
Westerhoff F (2003). Heterogeneous traders and the tobin tax. J Evol Econ 13: 53–70
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Hein, O., Schwind, M. & Spiwoks, M. Frankfurt Artificial Stock Market: a microscopic stock market model with heterogeneous interacting agents in small-world communication networks. J Econ Interac Coord 3, 59–71 (2008). https://doi.org/10.1007/s11403-008-0036-4
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DOI: https://doi.org/10.1007/s11403-008-0036-4