Abstract
We investigate the properties of a calibrated network structure of an agent-based model for a simulated financial market. A meta-model of a network of networks is introduced to capture the simulated market structure. The agent-based model consists of heterogeneous agents characterized by two-dimensional attributes that are investment behavior and investment strategy. The resulting groups of agents are viewed as subnetworks giving rise to a network of networks (NoN). The aggregation of activities of agents in a subnetwork trickles up to shape the aggregate activities of the NoN. The objective of introducing the NoN is to provide a testbed for complex models of simulated markets. Furthermore, we investigate the emergence of the market patterns in terms of prices, moments of returns, market capital, and wealth distributions. The investigation was performed for fully connected homogeneous agents. The results show a significant difference in the market emergence behaviors in terms of prices and returns, however, the market capitalization stays close to the calibrated financial market. Also, the deviation of wealth distributions was less than those in the heterogeneous market.
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References
Alsulaiman, T., Khashanah, K.: Bounded rational heterogeneous agents in artificial stock markets: Literature review and research direction. International Journal of Social, Behavioral, Educational, Economic and Management Engineering 9, 2038–2057 (2015)
Arthur, W.B., Holland, J.H., LeBaron, B., Palmer, R.G., Tayler, P.: Asset pricing under endogenous expectations in an artificial stock market. Available at SSRN 2252 (1996)
Barabási, B.A.L., Bonabeau, E.: Scale-free. Scientific American (2003)
Bertella, M.A., Pires, F.R., Feng, L., Stanley, H.E.: Confidence and the stock market: An agent-based approach. PloS one 9(1), e83,488 (2014)
Brock, W.A., Hommes, C.H.: A rational route to randomness. Econometrica: Journal of the Econometric Society pp. 1059–1095 (1997)
Brock, W.A., Hommes, C.H.: Heterogeneous beliefs and routes to chaos in a simple asset pricing model. Journal of Economic dynamics and Control 22(8), 1235–1274 (1998)
Chan, N.T., LeBaron, B., Lo, A.W., Poggio, T., Yy, A.W.L., Zz, T.P.: Agent-based models of financial markets: A comparison with experimental markets. Citeseer (1999)
Derveeuw, J.: Market dynamics and agents behaviors: a computational approach. In: Artificial Economics, pp. 15–26. Springer (2006)
Donges, J.F., Schultz, H.C., Marwan, N., Zou, Y., Kurths, J.: Investigating the topology of interacting networks. The European Physical Journal B 84(4), 635–651 (2011)
Eppstein, D., Löffler, M., Strash, D.: Listing all maximal cliques in sparse graphs in nearoptimal time. In: International Symposium on Algorithms and Computation, pp. 403–414. Springer (2010)
Frankel, J.A., Froot, K.A.: Explaining the demand for dollars: International rates of return and the expectations of chartists and fundamentalists. Department of Economics, UCB (1986)
Freeman, L.C.: Centrality in social networks conceptual clarification. Social networks 1(3), 215–239 (1978)
Gao, J., Buldyrev, S.V., Havlin, S., Stanley, H.E.: Robustness of a network of networks. Physical Review Letters 107(19), 195,701 (2011)
Gode, D.K., Sunder, S.: Allocative efficiency of markets with zero-intelligence traders: Market as a partial substitute for individual rationality. Journal of political economy pp. 119–137 (1993)
Hommes, C.H.: Heterogeneous agent models in economics and finance. Handbook of computational economics 2, 1109–1186 (2006)
Kahneman, D., Tversky, A.: Prospect theory: An analysis of decision under risk. Econometrica: Journal of the econometric society pp. 263–291 (1979)
Khashanah, K., Alsulaiman, T.: Network theory and behavioral finance in a heterogeneous market environment. Complexity (2016)
Kim, G.r., Markowitz, H.M.: Investment rules, margin, and market volatility. The Journal of Portfolio Management 16(1), 45–52 (1989)
Kivelä, M., Arenas, A., Barthelemy, M., Gleeson, J.P., Moreno, Y., Porter, M.A.: Multilayer networks. Journal of complex networks 2(3), 203–271 (2014)
Laguna, M., Marti, R.: The optquest callable library. In: Optimization Software Class Libraries, pp. 193–218. Springer (2003)
Laguna, M., Marti, R.: Scatter search: methodology and implementations in C, vol. 24. Springer Science & Business Media (2012)
LeBaron, B.: Agent-based computational finance. Handbook of computational economics 2, 1187–1233 (2006)
Martinez-Jaramillo, S., Tsang, E.P.: An heterogeneous, endogenous and coevolutionary gp-based financial market. IEEE Transactions on Evolutionary Computation 13(1), 33–55 (2009)
Newman, M.E.: Assortative mixing in networks. Physical review letters 89(20), 208,701 (2002)
Newman, M.E.: Mixing patterns in networks. Physical Review E 67(2), 026,126 (2003)
Palmer, R.G., Arthur, W.B., Holland, J.H., LeBaron, B., Tayler, P.: Artificial economic life: a simple model of a stockmarket. Physica D: Nonlinear Phenomena 75(1), 264–274 (1994)
Panchenko, V., Gerasymchuk, S., Pavlov, O.V.: Asset price dynamics with heterogeneous beliefs and local network interactions. Journal of Economic Dynamics and Control 37(12), 2623–2642 (2013)
Takahashi, H., Terano, T.: Agent-based approach to investors’ behavior and asset price fluctuation in financial markets. Journal of artificial societies and social simulation 6(3) (2003)
Wang, Y., Xiao, G.: Effects of interconnections on epidemics in network of networks. In:Wireless Communications, Networking and Mobile Computing (WiCOM), 2011 7th International Conference on, pp. 1–4. IEEE (2011)
Wasserman, S., Faust, K.: Social network analysis: Methods and applications, vol. 8. Cambridge university press (1994)
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Alsulaiman, T., Khashanah, K. (2017). Network of Networks: A Meta-model for Simulated Financial Markets. In: Cherifi, H., Gaito, S., Quattrociocchi, W., Sala, A. (eds) Complex Networks & Their Applications V. COMPLEX NETWORKS 2016 2016. Studies in Computational Intelligence, vol 693. Springer, Cham. https://doi.org/10.1007/978-3-319-50901-3_53
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DOI: https://doi.org/10.1007/978-3-319-50901-3_53
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