Designing a Model for Analyzing Financial Behavioral Change in Stock Market Actors in Response to Macroeconomic Variables: A Study with Agent-Based Simulation Approach
الموضوعات : International Journal of Finance, Accounting and Economics Studies
سید فرهاد گوران حیدری
1
,
عباس طلوعی اشلقی
2
,
احمد ابراهیمی
3
,
محمدرضا معتدل
4
1 - دانشجوی دکتری مدیریت فناوری اطلاعات علوم و تحقیقات تهران
2 - استاد گروه مدیریت فناوری اطلاعات ، دانشکده مدیریت و اقتصاد، ، واحد علوم و تحقیقات، دانشگاه آزاد اسلامی تهران، ایران
3 - استاد یار گروه مدیریت صنعتی و تکنولوژی، دانشکده مدیریت و اقتصاد، ، واحد علوم و تحقیقات، دانشگاه آزاد اسلامی تهران
4 - عضو هیأت علمی
الکلمات المفتاحية: Agent-based simulation, stock exchange, macroeconomic variables, behavioral finance,
ملخص المقالة :
The dynamics of the capital market is recognized as a key factor in the economic growth of countries. The reactions of stock market participants to changes in macroeconomic variables can have both positive and negative effects on the market. Identifying existing threats and transforming them into opportunities is of particular importance.
Purpose:Given the complexities of financial market structures and human behaviors, designing a simulation model to manage these complexities appears to be essential. This research focuses on the development of a model for the financial analysis of the country's stock market.
Design and methodology: After examining the market structure and price microstructures, broader characteristics have been predicted using a qualitative and inductive approach, resulting in a conceptual model design. The analysis and comparison of artificial markets, along with a comparative study and the use of a mixed-method approach to integrate human behaviors with quantitative and qualitative research techniques, constitute the next steps of this research. Ultimately, simulation technology has been utilized as a third method in scientific research approaches. The research is considered descriptive and practical in its objectives. For simulation purposes, the effective factors and their interactions have been identified and implemented as programming objects in NetLogo. The model validation has been conducted based on the proposed methods of Ronald Rust and William Rand, and sensitivity analysis has been performed according to Borgonov's systematic approach.
Findings: The findings indicate the impact of macroeconomic variables on the decisions of marketmakers, portfolio managers and investment funds concerning the growth of the overall stock index.
1. Agliari, A., Naimzada, A., & Pecora, N. (2018). Boom-bust dynamics in a stock market participation model with heterogeneous traders. Journal of Economic Dynamics.
2. Ahn, K., Cong, L., Jang, H., et al. (2024). Business cycle and herding behavior in stock returns: theory and evidence. Financial Innovation, 10(6). https://doi.org/10.1186/s40854-023-00540-z
3. Axelrod, R. (2003). Advancing the Art of Simulation in the Social Sciences. Japanese Journal for Management Information System, Special Issue on Agent-Based Modeling.
4. Azar, A., Saranj, A., Sadeghi-Moghaddam, A. A., Rajabzadeh, A., Moazzez, H. (2018 [1397 SH]). Agent-Based Modeling of Shareholders’ Behavior in the Iranian Stock Market. Financial Research Quarterly, 20(2).
5. Borgonovo, E., Pangallo, M., Rivkin, J., Rizzo, L., & Siggelkow, N. (2022). Sensitivity analysis of agent-based models: a new protocol. Computational and Mathematical Organization Theory, 28, 52–94.
6. Ebrahimi, M. (2019 [1398 SH]). Investigating the Impact of Macroeconomic Variables on Iran’s Stock Market with Data Mining Algorithms. Financial Economics, 49.
7. Edwin Achorn (2004). Integrating Agent-Based Models with Quantitative and Qualitative Research Methods. Faculty of Education, Monash University.
8. Fakhari, H., Nasiri, M. (2020 [1399 SH]). The Effect of Firm Performance on Future Stock Price Crash Risk. Financial Management Strategy, 8(3), 43.
9. Fouad Ben Abdelaziz, Fatma Mrad (2021). Multiagent systems for modeling the information game in a financial market. International Transactions in Operational Research.
10. Gao, Kang, Vytelingum, P., Weston, S., Luk, W., & Guo, C. (2024). High-Frequency Financial Market Simulation and Flash Crash Scenarios Analysis: An Agent-Based Modelling Approach. Journal of Artificial Societies and Social Simulation. DOI:10.18564/jasss.5403
11. Ghorbani, N., Babaei, E. (2015 [1394 SH]). Evaluating the Efficiency of the EMA Algorithm in Solving Optimization Problems. (Conference Paper)
12. Gilbert, N., & Troitzsch, K. (2008). Simulation For The Social Scientist. Open University Press.
13. Habibi, M., Boromandnia, A., Haroonabadi, A. (2021 [1400 SH]). Proposing a New Method to Counter DDoS Attacks in Named Data Networks with Agent-Based Simulation. ICT Police Quarterly, 2(2).
14. Hadipour, H., Paytakhti, S. A., Alavi-Matin, Y., Rahmani, K. (2021 [1400 SH]). Factors Affecting Instability in the Basic Metals Sector of the Tehran Stock Exchange. Industrial Management Studies.
15. Horcher, K. A. (2005). Essentials of financial risk management. John Wiley & Sons. ISBN 978-0-471-70616-8
16. Kiaei, A. (2010 [1389 SH]). Mutual Funds: An Instrument for Risk-Averse, Inexperienced Investors. Danesh-e Hesabresi, 10.
17. Kiani, R. (2017 [1396 SH]). Investigating Specialist Market Making in the Stock Exchange. Tehran: Securities and Exchange Organization.
18. Lev Muchnik, Yoram Louzoun, Sorin Solomon (2006). Agent Based Simulation Design Principles – Applications to Stock Market. Practical Fruits of Econophysics.
19. Macal, C. M., & North, M. J. (2007). Agent-based modeling and simulation: Desktop ABMS. Winter Simulation Conference.
20. Matthew Duffin, John Cartlidge (2018). Agent-Based Model Exploration of Latency Arbitrage in Fragmented Financial Markets. IEEE Symposium Series on Computational Intelligence.
21. McNeil, A. J., Frey, R., & Embrechts, P. (2005). Quantitative risk management: concepts, techniques, and tools. Princeton University Press. ISBN 978-0-691-12255-7.
22. Mizuta, Takanobu (2021). An Agent-Based Model for Designing a Financial Market That Works Well. IEEE Symposium Series on Computational Intelligence, Computational Intelligence for Financial Engineering and Economics (CIFEr).
23. Mizuta, Takanobu (2022). A Brief Review of Recent Artificial Market Simulation (Agent-Based Model) Studies for Financial Market Regulations and Rules.
24. Mizuta, Takanobu, Kosei Takashima, Isao Yagi (2022). Instability of financial markets by optimizing investment strategies investigated by an agent-based model. Computational Intelligence for Financial Engineering and Economics.
25. Mohammadpour, A., Sadeghi, R., Rezaei, M. (2010 [1389 SH]). Mixed Methods Research as the Third Methodological Movement. Applied Sociology, 2(2).
26. Mokhtarband, M., Tehrani, R., Al-Abouda, M. (2024 [1403 SH]). Estimation of Fundamental Macroeconomic Factors on the Capital Market (Mixed-Frequency Data Approach). Financial Research.
27. Muhammad Asif Khan, Saima Aziz, Shahid Mehmood, Anita Tangl (2024). Role of behavioral biases in the investment decisions of Pakistan Stock Exchange investors: Moderating role of investment experience. Investment Management and Financial Innovations. doi:10.21511/imfi.21(1).2024.12
28. Muhammad Hanif, Arshad Bhatti (2018). Causality among Stock Market and Macroeconomic Factors: A Comparison of Conventional and Islamic Stocks. Journal of Islamic Business and Management.
29. Ponta, L., Pastore, S., & Cincotti, S. (2018). Static and dynamic factors in an information-based.
30. Rand, W., & Rust, R. T. (2011). Agent-based modeling in marketing: Guidelines for rigor. International Journal of Research in Marketing.
31. Rostami-Jaz, H., Bavvaghar, M., Reisi, L. (2024 [1403 SH]). Explaining the Effect of Personality Traits on Financial Specialists’ Behavioral Biases in Iran. Financial and Behavioral Research in Accounting, 1(4).
32. Sadek Benhammada, Frédéric Amblard (2021). An Agent-Based Model to Study Informational Cascades in Financial Markets. New Generation Computing.
33. Sotudeh, R., Hirad, A., B. Pirnia, B. (2024 [1403 SH]). Explaining Behavioral Decision-Making Patterns for Investors in the Country’s Capital Market. Financial and Behavioral Research, 4(1).
34. Tina Comes & Frances Brazier (2023). A Methodology to Develop Agent-Based Models for Policy Support Via Qualitative Inquiry. Delft University of Technology, Netherlands. Journal of Artificial Societies and Social Simulation, 26(1)10.
35. Tohidlu, M., Baiat, A., Fathi, A., Rostami, V. (2024 [1403 SH]). Investigating the Mediating Role of Financial Statement Comparability in the Relationship between Managers’ Opportunistic Behaviors and Divergence in Investor Opinions. Financial and Behavioral Research in Accounting, 1(4).
36. Vakili-Fard, H. R., Khoshnud, M., Foroughnejad, H., Osoulian, M. (2014 [1393 SH]). Agent-Based Modeling in Financial Markets. Investment Knowledge Quarterly.
37. Valizadeh, F., Mohammadzadeh, A., Seyghali, M., Torabian, M. (2021 [1400 SH]). Presenting a Model for Predicting Factors Affecting Stock Price Crash Risk. Financial Management Vision.
