Abstract
As a complex giant system, financial market has complexity characteristics with distinctive forms, which shakes theoretical basis of effective market hypothesis to challenge traditional paradigm for financial research. Based on agent-based computational economics, the work established artificial stock market by computer’s high-speed information processing ability and bottom-up modeling method. The real financial markets were highly simulated to guide financial market regulation and policy development. The artificial stock market model in the work realized the simulation of real stock market to a certain extent. This showed that it was effective to improve artificial stock market modeling method based on agent and scale-free network. Therefore, the application value analysis of model has important practical significance.
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References
Fama, E.F.: Efficient capital market: a review of theory and empirical work. J. Financ. 25(2), 383–417 (1970)
Odean, T.: Are investors reluctant to realize their losses? J. Financ. 53, 1775–1798 (1998)
Shleifer, A.: Inefficient Markets: An Introduction to Behavioral Finance, pp. 1–35. Oxford University Press, Oxford (2000)
Schleifer, A., Vishny, R.W.: The limits of arbitrage. J. Financ. 52, 35–55 (1997)
Thaler, R.H.: The end of behavioral finance. Financ. Anal. J. 12, 12–18 (1999)
Jegadeesh, N., Titman, S.: Returns to buying winners and selling losers: implications for stock market efficiency. J. Financ. 48(1), 65–91 (1993)
Kim, G.S., Eom, C.: Long-term memory and volatility clustering in high-frequency price changes. Phys. A 87, 1247–1254 (2008)
Weil, P.: The equity premium puzzle and the risk-free rate puzzle. J. Monet. Econ. 24, 401–421 (1989)
Epstein, L., Stanley, Z.: Substitution, risk aversion, and the temporal behavior of consumption and asset returns: an empirical investigation. J. Polit. Econ. 99, 263–286 (1991)
Lux, T., Marches, M.: Scaling and criticality in a stochastic multi-agent model of a financial market. Nature 397(11), 498–500 (1999)
Stanley, H.E., et al.: Econophysics: can physicists contribute to the science of economics. Phys. A 269(1), 156–169 (1999)
Keim, D.: Size-related anomalies and stock return seasonality: further empirical evidence. J. Financ. Econ. 12, 13–32 (1983)
John, R., Chen, C.H.: The wandering weekday effect in major stock markets. J. Bank. Financ. 33(8), 1388–1399 (2009)
Cheng, J.C., Guo, J.D., Shi, W.Z.: Uncertainty of Remote Sensing Data. Science Press, New York (2004)
Milan, S., Vaclav, H., Roger, B.: Image Processing, Analysis and Machine Vision \(^{{\rm [M]}}\). Springer, New York (2014)
Blaschke, H.: Object-oriented image analysis and scale-space: theory and methods for modeling and evaluating multi-scale landscape structure. Int. Arch. Photogr. Remote Sens. 34, 22–29 (2001)
Ursula, C.B., Peter, H., Gregor, W., Iris, L., Markus, H.: Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information. ISPRS J. Photogr. Remote Sens. 58(3–4), 239–258 (2004)
Baatz, M., Schape, A.: Multi-resolution Segmentation: An Optimization Approach for Quality Multi-scale Segmentation (2000). http://www.agit.at/papers/2000/baatz_FP_12.PDF
Haris, K., Efsteatiadis, S.: Hybrid image segmentation using watersheds and fast region merging. IEEE Trans. Image Process. 7(12), 1684–1698 (1998)
Gong, J.: Hierarchical image segmentation based on watershed filtering and fuzzy cluster. J. First Mil. Med. Univ. 24(3), 329–331 (2004)
Zhang, L.Z.: Image Segmentation. Science Press, New York (2001)
Moghaddamzadeh, A., Bourbakis, N.: A fuzzy region growing approach for segmentation of color images. Pattern Recogn. 30(6), 867–881 (1997)
Zhou, Qingyuan, Luo, Jianjian: The risk management using limit theory of statistics on extremes on the big data era. J. Comput. Theor. Nanosci. 12, 6237–6243 (2015). doi:10.1166/jctn.2015.4661
Zhou, Qingyuan, Luo, Jianjian: Artificial neural network based grid computing of E-government scheduling for emergency management. Comput. Syst. Sci. Eng. 30(5), 327–335 (2015)
Zhou, Qingyuan, Luo, Juan: The service quality evaluation of ecologic economy systems using simulation computing. Comput. Syst. Sci. Eng. 31(6), 453–460 (2016)
Zhou, Qingyuan, Luo, Jianjian: The study on evaluation method of urban network security in the big data era. Intell. Autom. Soft Comput. (2017). doi:10.1080/10798587.2016.1267444
Hongnan, S.: Study on the improved model of artificial stock market based on agent and scale-free network [D]. Harbin Institute of Technology, 2011
Jiang, Jijiao, Yang, Naiding: Artificial stock market model based on bounded rational agent. Comput. Eng. Appl. 35, 4–6 (2005)
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Yang, J. The artificial stock market model based on agent and scale-free network. Cluster Comput 21, 977–984 (2018). https://doi.org/10.1007/s10586-017-0991-4
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DOI: https://doi.org/10.1007/s10586-017-0991-4