Simulation Modeling of Information Transfer Between Stock Exchanges Based on a Multi-Agent Model

Authors

  • R. V. Faizullin Department of Information Technologies in Public Administration, MIREA - Russian Technological University
  • P. P. Lukyanchenko National Research University Higher School of Economics

DOI:

https://doi.org/10.22213/2410-9304-2023-4-88-94

Keywords:

information shock, market shock, market simulator, agent-based modeling, stock market, stock exchange, simulation modeling, multi-agent modeling

Abstract

The article substantiates the relevance of solving the problem of stock exchange modeling as an information system accumulating the orders of market participants and executing them. The object of the study is the process of information transfer between stock exchanges. The subject of the study is the use of simulation multi-agent model to study the process of information transfer between stock exchanges. The paper analyzes the possibilities of building a simulation model to analyze rare phenomena in the financial market using a multiagent approach. It is substantiated that this approach allows taking into account those features of complex dynamic systems, for which either no effective analytical approaches have been developed or it is impossible to conduct the necessary numerical experiments. The proposed logic of the market simulator is based on the allocation of certain trader classes, that may differ in the logic of making their decisions on the stock exchange, for this purpose the scheme of agent interaction in the model of the stock exchange is described. On the example of modeling the information transmission and market shocks between markets the possibilities of the proposed simulator are studied. The use of the proposed model in practice allows us to analyze rare phenomena occurring on the stock exchange, which cannot be described analytically, but their recreation by means of the simulation model will allow us to study them. The study of rare phenomena on the stock exchange with the help of simulation modeling allows to obtain different variants of the results of the phenomena at different sets of parameters of the model (agents), i.e. to carry out parameter.

Author Biographies

R. V. Faizullin, Department of Information Technologies in Public Administration, MIREA - Russian Technological University

PhD in Economics, Associate Professor

P. P. Lukyanchenko, National Research University Higher School of Economics

Senior Lecturer, Faculty of Computer Science

References

Peress J., Schmidt D. Glued to the TV: Distracted noise traders and stock market liquidity // The Journal of Finance. 2020. 75 (2). Pp. 1083-1133.

Zhou W., Zhong G. Y., Li J. C. Stability of financial market driven by information delay and liquidity in delay agent-based model // Physica A: Statistical Mechanics and Its Applications. 2022. 600. Pp. 127-526.

Gao K. et al. High-frequency financial market simulation and flash crash scenarios analysis: an agent-based modelling approach // arXiv preprint arXiv:2208.13654. 2022.

King M. A. and Wadhwani S. Transmission of volatility between stock markets. Review of Financial Studies, vol. 3, no. 1, pp. 5-33, 1990.

Омран Ш. Оценка волатильности на российском рынке акций: эмпирический анализ // Банковские услуги. 2020. № 6. С. 21-26.

Rand W., Stummer C. Agent-based modeling of new product market diffusion: an overview of strengths and criticisms // Annals of Operations Research. 2021. 305 (1-2). Pp. 425-447.

Aloud M. et al. Modeling the High-Frequency FX Market: An Agent-Based Approach //Computational Intelligence. 2017. 33 (4). Pp. 771-825.

Clack C. D., Court E., Zaparanuks D. Dynamic Coupling and Market Instability //arXiv preprint arXiv:2005.13621. 2020.

Walter C. 2006. Les martingales sur les marchés financiers. Revue de Synthèse, vol. 127, no 2, p. 379.

Oriol N., Veryzhenko I. Market structure or traders' behavior? A multi agent model to assess flash crash phenomena and their regulation // Quantitative Finance. 2019. 19 (7). Pp. 1075-1092.

Giglio S. et al. Inside the mind of a stock market crash. National Bureau of Economic Research, 2020. № w27272.

Mandel A., Veetil V. The economic cost of COVID lockdowns: an out-of-equilibrium analysis // Economics of Disasters and Climate Change. 2020. 4. Pp. 431-451.

McGroarty F. et al. High frequency trading strategies, market fragility and price spikes: an agent based model perspective // Annals of Operations Research. 2019. Т. 282. С. 217-244.

Court E. The instability of market-making algorithm - an agent based simulation in Miranda" MEng dissertation, Dept.ComputerScience, UCL, 2013. 97 p.

Чернышев С. А. Классификация общих шаблонов проектирования мультиагентных систем // Программные продукты и системы. 2022. Т. 35, № 4. С. 670-679.

Герасимова Е., Яворский Р. Моделирование тестовых сценариев поведения участников биржевой торговли // Инструменты и методы анализа программ: Международная научно-практическая конференция. Кострома, 2014.

Published

09.01.2024

How to Cite

Faizullin Р. В., & Lukyanchenko П. П. (2024). Simulation Modeling of Information Transfer Between Stock Exchanges Based on a Multi-Agent Model. Intellekt. Sist. Proizv., 21(4), 88–94. https://doi.org/10.22213/2410-9304-2023-4-88-94

Issue

Section

Articles