ABM with behavioral bias and applications in simulating china stock market

• Autorzy: Wang G. , Zhang S.

Identyfikatory

DOI

10.1515/jaiscr-2015-0034

• Języki publikacji: EN

Abstrakty

EN

One of the most important advantage of ABM (Agent-Based Modeling) used in social and economic calculation simulation is that the critical behavioral characteristics of the micro agents can be deeply depicted by the approach. Why, what and how real behavior(s) should be incorporated into ABM and is it appropriate and effective to use ABM with HSCA collaboration and micro-macro link features for complex economy/finance analysis? Through deepening behavioral analysis and using computational experimental methods incorporating HS (Human Subject) into CA (Computational Agent), which is extended ABM, based on the theory of behavioral finance and complexity science as well, we constructed a micro-macro integrated model with the key behavioral characteristics of investors as an experimental platform to cognize the conduction mechanism of complex capital market and typical phenomena in this paper, and illustrated briefly applied cases including the internal relations between impulsive behavior and the fluctuation of stock’s, the asymmetric cognitive bias and volatility cluster, deflective peak and fat-tail of China stock market.

• Wydawca: University of Social Sciences
• Czasopismo: Journal of Artificial Intelligence and Soft Computing Research
• Rocznik: 2015
• Tom: Vol. 5, No. 4
• Strony: 257--270
• Opis fizyczny: Bibliogr. 22 poz., rys.

Twórcy

autor

Wang G.

• Institute of Quantitative & Technical Economics Chinese Academy of Social Sciences Beijing 100732 China

autor

Zhang S.

• Graduate School of Chinese Academy of Social Sciences Beijing Fangshan 102488 China zhangsg [e-mail]

Bibliografia

• [1] B. Arthur, Economic Agents that Behave like Human Agents, Journal of Evolutionary Economics, Issue 3, 1993, 1-22. Reprinted in The Legacy of Joseph A. Schumpeter, H. Hanusch, Ed., Edward Elgar Publishers, 2000.
• [2] B. LeBaron, Agent-based computational finance, In: Tesfatsion L., Judd K. L. (Eds.), Handbook of Computational Economics. Elsevier, 2006, 1187-1233.
• [3] B. LeBaron, Heterogeneous gain learning and the dynamics of asset prices. Journal of Economic Behavior and Organization. Vol.83, 2012 ,424-445.
• [4] C. F. Camerer, T. Ho and J. K. Chong, A cognitive hierarchy model of games. Quarterly journal of economics, 119(3), 2004, 861—898.
• [5] C. H. Hommes, Heterogeneous agent models in economics and finance. In L. Tesfatsion and K.L. Judd (Eds.), Handbook of computational economics, volume 2, 2006, Agent-based computational economics. Amsterdam, the Netherlands: Holland/Elsevier.
• [6] D. Levin, Is Behavioral Economics Doomed? The Ordinary versus the Extraordinary. UK: openbook publishers, 2012.
• [7] F. Schweitzer et al., Economic Networks: The New Challenges”, Science, 24 July, Vol. 325. No. 5939, 2009, 422 – 425.
• [8] G. Wang, Exploring complex economy to develop quantitative economics from Micro-behavior perspective, Journal of Quantitative Economics, Vol. 2(1), 2011, 102-120.
• [9] G. Wang, The evolvement and beyond of rationalism in modern economics. Social Sciences in China, No. 7, 2012, 66-82.
• [10] G. Wang, Deepening micro-behavioral analysis and exploring the complexity of macro-economy. Jiangsu Social Sciences, No. 3, 2013, 20-28.
• [11] G. Wang, Behavioral Macro-Financial Modeling from Investor’s Bias with Applications — Based on the Experiment of Incorporating HS and CA, Journal of Management Science & Statistical Decision Vol. 11, no.1, 2014, 24–40.
• [12] G. Wang, and Y. Long, Study on Emergence of Capital Market with Cognitive Hierarchy and Extensive Agent-Based Modeling— An Application of e-Science in Social Sciences, e-Science Technology & Application, 5(1), 2014, 83˜92.
• [13] G. Wang, and S.G. Zhang.,Behavioral Compatibility, ACF and the Emergence of Chinese Stock Market, 21st International Conference on Computing in Economics and Finance, Taipei, Taiwan, June 20-22, 2015.
• [14] J. D. Farmer and D. Foley, The economy needs agent-based modeling, Nature, (6th August) 460, 2009, 685-686.
• [15] J. Duffy, Agent-based models and human subject experiments. In: Tesfatsion L., Judd K.L (Eds.),Handbook of Computational Economics, Volume 2, 2006, 949-1011. North-Holland, Amsterdam, the Netherlands: Elsevier.
• [16] J. H. Miller and E. Scott, Complex Adaptive Systems: An introduction to computational models of social life, NJ: Princeton university press, 2007.
• [17] L. Scheffknecht, and F. Geiger, A behavioral macroeconomic model with endogenous boombust cycles and leverage dynamcis, FZID Discussion Papers 37-2011, University of Hohenheim, Center for Research on Innovation and Services, 2011.
• [18] M. Lengnick and H. W. Wohltmann , Agent-based financial markets and New Keynesian macroeconomics: a synthesis, Journal of Economic Interaction and Coordination, Springer, vol. 8(1), 2013, 1-32.
• [19] P. De Grauwe, Top-Down versus Bottom-Up Macroeconomics, CESifo Economic Studies, 56, 2010 , 465-497.
• [20] P. Shao, Bounded rationality, cognitive hierarchy and investment game. The journal of quantitative & technical economics. 10, 2010, 145—155.
• [21] T. Lux, Stochastic Behavioral Asset-Pricing Models and the Stylized Facts, In: Handbook of Financial Markets: Dynamics and Evolution. Edited by Thorsten Hens and Klaus R. Schenk-Hoppe, chapter 3, 2009, 161–215.
• [22] W. Zhang, Y. J. Zhang and X. Xiong, Agent-based Computational Finance, Beijing, Science Press, 2010.