An Artificial Stock Market Based on Soft Control Theory
International Journal of Economics, Finance and Management Sciences
Volume 6, Issue 2, April 2018, Pages: 54-59
Received: Apr. 26, 2018; Published: Apr. 27, 2018
Views 564      Downloads 46
Pan Fuchen, College of Science, Dalian Ocean University, Dalian, China; College of Basic Education, Dalian University of Finance and Economics, Dalian, China
Li Lin, Department of Technology, Dalian Radio and TV University, Dalian, China
Article Tools
Follow on us
In this study, we consider the simple but typical artificial stock market model proposed by LeBaron, B. et al.: each trader makes decision by maximizing the same utility function. We constructed a multi-agents artificial market model and investigated the effect of control traders among traders on price shock transfer from one asset to the whole market. The model is composed of two sorts of asset: price stocks and its underlying stocks. Our simulation featured two types of agent: control trader and ordinary traders. Control trader, who owns enough wealth, can intervene in the trading behavior of the group by applying the trading rule: trade when the stock price deviates from preset value. The traders in the artificial stock market reproduce their stylized facts related mainly to information asymmetry and herd behavior, which reduces the volatility of the stock market. The implications for market rules are discussed. From simulations of various trading strategies of control traders, we found the stock price can be controlled by control traders with certain strategies. The simulation results demonstrate the effectiveness of the method.
Soft Control, Artificial Stock Market, Control Trader, Stock Returns
To cite this article
Pan Fuchen, Li Lin, An Artificial Stock Market Based on Soft Control Theory, International Journal of Economics, Finance and Management Sciences. Vol. 6, No. 2, 2018, pp. 54-59. doi: 10.11648/j.ijefm.20180602.13
Leigh Tesfatsion. Introduction to the special issue on agent-based computational economics. Journal of Economic Dynamics and Control, 2001, (25), pp:281-293.
Derveeuw, J. Beaufils, B. Brandouy, O. and Mathieu, P., Testing double auction as a component within a generic market model architecture, In Artificial Economics (AE07). Springer (2007).
LeBaron, B. Agent based computational finance: suggested readings and early research. Journal of Economic Dynamics and Control, 2000, 24, pp: 679–702.
LeBaron, B. A builder’s guide to agent-based financial markets. Quantitative Finance, 2001, 1, pp: 254–261.
Westerhoff, F.. The use of agent-based financial market models to test the effectiveness of regulatory policies. Journal of Economics and Statistics, 2008, 228, pp: 195–227.
Galimberti, J. K., & Moura, M. L. Improving the reliability of real-time output gap estimates using survey forecasts. International Journal of Forecasting, 2016, 32 (2), pp: 358–373.
Kovaleva, P., & Iori, G.. The impact of reduced pre-trade transparency regimes on market quality. Journal of Economic Dynamics and Control, 2015, 57, pp: 145–162.
Jaqueson K. Galimberti, Nicolas Suhadolnik, Sergio Da Silva. Cowboying stock market herds with robot traders. Computational economics, 2017, 50, pp: 393–423.
Zhou Qingyuan, Luo Juan. The service quality evaluation of ecologic economy systems using simulation computing. Computer systems science and engineering, 2016, 31 (6), pp: 453–460.
Assenza T, Delli Gatti D, Grazzini J, 2015. Emergent dynamics of a macroeconomic agent based model with capital and credit. Journal of economic dynamics and control. 50, pp: 5–28.
Xu HC, Zhang W, Xiong X, Zhou WX. An agent-based computational model for china’s stock market and stock index futures market. Mathematical problems in engineering, 2014 Article ID: 563912.
Takuma Torii, Kiyoshi Izumi, Kenta Yamada. Shock transfer by arbitrage trading: analysis using multi-asset artificial market. Evolut Inst Econ Rev, 2015, 12, pp: 395–412.
[13] Wang, J., Wang, J. Forecasting stock market indexes using principle component analysis and stochastic time effective neural networks. Neurocomputing, 2015, 156, pp: 68–78.
de Fortuny, E. J., De Smedt, T., Martens, D., Daelemans, W.. Evaluating and understanding textbased stock price prediction models. Inf. Process. Manag, 2014, 50, pp: 426–441.
Jing Han, Ming Li and Lei Guo. Soft control on collective behavior of a group of autonomous agents by a shill agent. Journal of systems science and complexity, 2006, (19), pp: 54-62.
Guillaume Sartoretti, Leader‑based versus soft control of multi‑agent swarms, Artificial Life Robotics, 2016, 21, pp: 302–307.
Derveeuw, J. Beaufils, B. Brandouy, O. and Mathieu, P. Testing double auction as a component within a generic market model architecture, In Artificial Economics (AE07), 2007, Springer.
Palmer, r., w. Arthur, j. Holland, b. Lebaron, and p. Tayler. Artificial Economic Life: A Simple Model of a Stockmarket Physica D, 1994, 75, pp: 264-274.
Boer, K. Agent-Based Simulation of Financial Markets A Modular, Continuous-Time Approach. PhD thesis, Erasmus University Rotterdam, UK (2008).
Arthur W. B., Holland J. H., LeBaron B., Palmer R., Tayler P.. Asset Pricing Under Endogenous Expectations in an Artificial Stock Market, The Economy as an Evolving Complex System, Addison-Wesley, 1997, pp:15-44.
Gordillo, J. L., Stephens, C. R. Analysis of financial markets with the artificial agent-based model — NNCP. Proceedings of ENC01. INEGI, Mexico, 2001, pp: 251–263.
Shu-Heng Chen, Chia-Hsuan Yeh. Evolving traders and the business school with genetic programming: A new architecture of the agent-based artificial stock market. Journal of Economic Dynamics & Control, 2001, 25, pp. 363-393.
Westerhoff, F. Speculative markets and the effectiveness of price limits. Journal of Economic Dynamics and Control 28, 493–508. G. Eason, B. Noble, and I. N. Sneddon, “On certain integrals of Lipschitz-Hankel type involving products of Bessel functions,” Phil. Trans. Roy. Soc. London, 2003, vol. A247, pp: 529–551.
Science Publishing Group
NEW YORK, NY 10018
Tel: (001)347-688-8931