The Agent-based modeling of stockholders’ behavior in Iranian capital market

Document Type : Research Paper

Authors

1 Prof, Department of Industrial Management, School of Management & Economics, University of TarbiatModares, Tehran, Iran

2 Assistant Prof, Department of Industrial Management and Finance, Faculty of Management and Accounting, Farabi Campus, University of Tehran, Qom, Iran

3 Ph.D. Candidate in Industrial Management, Department of Industrial Management and Finance, Faculty of Management and Accounting, Farabi Campus, University of Tehran, Qom, Iran

4 Associate Prof, Department of Industrial Management, School of Management & Economics, University of TarbiatModares, Tehran, Iran

Abstract

Objective: One of the main concerns of the market regulators is the prediction of the effects of these new strategies on the market due to the heterogeneity of the agents, rational boundary and behavioral factors in the investors’decision making. The Iranian stock market has always been fluctuating; therefore,awareness of the effects of strategies before they are implementedwill help regulators to market more effectively. The main objective of this research is to create an artificial market according to the Iranian stock market so that different scenarios can be simulated.
Methods:One of these emerging areas, which emphasizes the impact of social sciences, cognitive sciences and behavioral sciences on operational research, is "Behavioral Operations Research" that helps us solve real-world problems.In this research, considering modeling based onagent-based capabilities, shareholders’ capabilities, bonds including different types of stocks and risk-free papers, and trading rules.
Results:In this artificial market in each trading period, in accordance with the trading strategy and learning procedures, the agents intendto buy and sell. Eventually they worked as the market makers, in accordance with the auction mechanism, and began to execute orders and perform clearing and settlement operations. In order to examine the validity of the model, the statistical output of this market wasadapted to the statistical characteristics of the financial markets and, after validating the model with the scenario, simulation of the research questions were done. In this research, the scenarios for eliminating the range of price fluctuations and elimination of the informed stakeholders and their effects on stock prices were reviewed.
Conclusion: According to the simulated scenarios of the Iranian stock market, due to its immature nature, eliminating controlling mechanisms such as the range of price fluctuations, in the short term the Market willbe highly instable, but in the long run the market tends to be more efficient.

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References

Adriaens H. (2002). Simulating Financial Markets With Heterogeneous Agents: A Study in an Agent Based Computational Economics Framework. master’s thesis, University of Tilburg, www.stuw.uvt.nl/~hendri/University/education.html.
Agliari, A., Naimzada, A., & Pecora, N. (2018). Boom-bust dynamics in a stock market participation model with heterogeneous traders. Journal of Economic Dynamics and Control, 91, 458-468. doi.org/10.1016/j.jedc.2018.04.007.
Arthur W. B. (2004). Inductive Reasoning and Bounded Rationality., The American Economic Review, 84 (2), 406-411.
Bak, P., Paczuski, M., & Shubik, M. (1997). Price variations in a stock market with many agents. Physica A: Statistical Mechanics and its Applications, 246 (3-4), 430-453.
Bianchi, C., Cirillo, P., Gallegati, M., & Vagliasindi, P. A. (2007). Validating and calibrating agent-based models: a case study. Computational Economics, 30 (3), 245-264.
Caldarelli, G., Marsili, M. & Zhang, Y. C. (1997).A prototype model of stock exchange. EPL (Europhysics Letters), 40 (5), 479-484.
Chen S., Chen H. & Yeh C. (2001). Evolving Traders and the Business School With Genetic Programming: A New Architecture of the Agent-Based Artificial Stock Market, Journal of Economic Dynamics and Control, 25, 363-393.
De la Maza M. & Yuret D (1995) .A Model of Stock Participants In Birthahn J. and Nissen V., eds., Evolutionary Algorithms in Management Applications, Springer Verlag, Heidelberg, 290-304.
DeLong J.B., Schleifer, A., Summers, L. H. & Waldmann, R. (1991). The Survival of Noise Traders in Financial Markets, Journal of Business, 64, 1-19.
Fagiolo, G., Windrum, P., & Moneta, A. (2006). Empirical validation of agent-based models: A critical survey (No. 2006/14). LEM Working Paper Series.
Farmer, J. D., & Joshi, S. (2002). The price dynamics of common trading strategies. Journal of Economic Behavior & Organization, 49 (2), 149-171.
Franses, P. H., & Van Dijk, D. (2000). Non-linear time series models in empirical finance. Cambridge University Press.
Gilbert N., and Troitzsch K. (2008). Simulation For The Social Scientist, New York: Open University Press.
Krichene, H., & El-Aroui, M. A. (2017). Artificial stock markets with different maturity levels: simulation of information asymmetry and herd behavior using agent-based and network models. Journal of Economic Interaction and Coordination, 1-25
Hommes, C. H. (2006). Heterogeneous agent models in economics and finance. Handbook of computational economics, 2, 1109-1186.
Joshi, S., & Bedau, M. A. (1998).An explanation of generic behavior in an evolving financial market.Complex Systems, 98, 326-332.
Keles, D., Bublitz, A., Zimmermann, F., Genoese, M., & Fichtner, W. (2016). Analysis of design options for the electricity market: The German case. Applied energy, 183, 884-901.
LeBaron, B. (2006). Agent-based computational finance. Handbook of computational economics, 2, 1187-1233.
Lux, T. (1998). The Socio-economic Dynamics of Speculative Markets: Interacting Agents, chaos, and the Fat Tails of Return Distributions, Journal ofEconomic Behavior and organization, 33, 143-165.
Lux, T., & Marchesi, M. (2000). Volatility clustering in financial markets: a microsimulation of interacting agents. International journal of theoretical and applied finance, 3 (04), 675-702.
Macal, C. M., & North, M. J. (2005).Tutorial on agent-based modeling and simulation.In Simulation conference, 2005 proceedings of the winter (pp. 14-pp).IEEE.
Ponta, L., Pastore, S., & Cincotti, S. (2018). Static and dynamic factors in an information-based multi-asset artificial stock market. Physica A: Statistical Mechanics and its Applications, 492, 814-823.
Rastegar, M., Saedi Far, K. (2017). Optimal Execution Strategy: An Agent-based Approach. FinancialResearchJournal, 9 (2), 262-239.(in persian)
Roberto, M., Cincotti, S., Focardi, S. M., & Marchesi, M. (2001).Traders' long-run wealth in an artificial financial market. Computational Economics, 22 (2-3), 255-272.
Roozmand O., and Webster D. (2014) “Consumer Choice and aggregate demand: AnABM approach to understanding the impacts of satisficing behavior ”, International Journal of Agent Technologies and Systems (IJATS), 6 (4), 1-18.
Sa'idi, A. & Farhanian, S. M. J. (2015). Basics of Behavioral Economics and Finance. Tehran: Exchange. (in Persian)
Shatner, M., Muchnik, L., Leshno, M., & Solomon, S. (2000). A continuous time asynchronous model of the stock market; beyond the lls model. arXiv preprint cond-mat/0005430.
Youssefmir, M., Huberman, B. A., & Hogg, T. (1998). Bubbles and market crashes. Computational Economics, 12 (2), 97-114.
 
 
Financial Research Journal
Volume 20, Issue 2
2018
Pages 130-150

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