Journal of Finance and Accounting
Volume 6, Issue 2, March 2018, Pages: 69-75
Received: Apr. 26, 2018;
Published: May 23, 2018
Views 1782 Downloads 213
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
In this paper, we study trading behavior of five different populations with different trading strategies in the framework of an artiﬁcial stock market. Insiders who know accuracy time and quantity of inflow cash enter into market and trade with others, which increase difficulty to get more profit for non-insiders. A new clearing mechanism that matches price in order is mentioned. Simulation results show that trading strategies yield different results. It is noticeable that insider can easily get more profit in short time due to prior information.
Trading Behaviours Analysis in an Artificial Stock Market, Journal of Finance and Accounting.
Vol. 6, No. 2,
2018, pp. 69-75.
John G. Mooney, V. G. a. K. L. K. A Process Oriented Framework for Assessing the Business Value of Information Technology. Forthcoming in the Proceedings of the Sixteenth Annual International Conference on Information Systems. 2001.
Zhou, Qingyuan, Luo, Juan: The service quality evaluation of ecologic economy systems using simulation computing. Comput. Syst. Sci. Eng, 2016, 31 (6), pp: 453–460.
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.
Jian Yang. The artificial stock market model based on agent and scale-free network. Cluster Comput, 2017, DOI 10.1007/s10586-017-0991-4.
Assenza T, Delli Gatti D, Grazzini J. Emergent dynamics of a macroeconomic agent based model with capital and credit. Journal of Economic Dynamics and Control, 2015, 50, pp:5–28.
Cipriani, M., & Guarino, A.. Estimating a structural model of herd behavior in financial markets. American Economic Review, 2014, 104 (1), 224–51.
Hazem Krichene· Mhamed-Ali El-Aroui, Agent-Based Simulation and Microstructure Modeling of Immature Stock Markets, Comput Econ, 2018, 51, pp:493–511, https://doi.org/10.1007/s10614-016-9615-y.
Tedeschi, G., Iori, G., & Gallegati, M.. Herding effects in order driven markets: The rise and fall of gurus. Journal of Economic Behavior and Organization, 2012, 81, pp: 82–96.
Mizuta, T., Izumi, K., Yagi, I., & Yoshimura, S. Investigation of price variation limits, short selling regulation, and uptick rules and their optimal design by artificial market simulations. Electronics and Communications in Japan, 2015, 98 (7), pp: 13–21.
Xuan Zhou· Honggang Li, Buying on Margin and Short Selling in an Artificial, Comput Econ, 2017, DOI 10.1007/s10614-017-9722-4.
Anand, K., Kirman, A., & Marsili, M.. Epidemics of rules, rational negligence and market crashes, The European Journal of Finance, 2013, 19 (5), 438–447.
Arajo, RdA, Oliveira, A. L. I., Meira, S.: A hybrid model for high-frequency stock market forecasting. Expert Systems with. Applications, 2015, 42, pp: 4081–4096.
Hafezi, R., Shahrabib, J., Hadavandi, E.: A bat-neural network multi-agent system (BNNMAS) for stock price prediction: case study of DAX stock price. Appl. Soft Comput. 2015, 29, pp: 196–210.
Kawakubo S, Izumi K, Yoshimura S, Analysis of an option market dynamics based on a heterogeneous agent model. Intell Syst Acc Finance Manag, 2014, 21 (2), pp: 105–128.
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.
Bollerslev T Generalised Autoregressive Conditional Heteroscedasticity. Journal of Econometrics, 1986, 31, pp: 307-327.
Cont R. Empirical properties of asset returns: stylized facts and statistical issues. Quantitative Finance, 2001, 1, pp: 223-236.
Shao Yanhua, LI Jianshi and WANG Jinghong. Model and Simulation of Stock Market Based on Agent. Proceedings of the 2008 IEEE International Conference on Information and Automation June 20-23, 2008, Zhangjiajie, China, 248-252.
Xiumei Zhang, Chi Ma, Xinmiao Yu, A neural network model for financial trend predicting, Cluster Computing, 2018, https://doi.org/10.1007/s10586-018-2196-x.
Wang, X., Wang, H., Wang, W. H.: Artificial neural network theory and applications. Northeastern University Press, Shenyang (2000).