مدل‏سازی عامل‏گرای رفتار سهام‏داران در بازار سرمایه ایران

نوع مقاله : مقاله علمی پژوهشی

نویسندگان

1 استاد گروه مدیریت صنعتی، دانشکده مدیریت و اقتصاد، دانشگاه تربیت مدرس، تهران، ایران

2 استادیار گروه مدیریت صنعتی و مالی، دانشکده مدیریت و حسابداری، پردیس فارابی، دانشگاه تهران، قم، ایران

3 دانشجوی دکتری مدیریت صنعتی، گروه مدیریت صنعتی و مالی، دانشکده مدیریت و حسابداری، پردیس فارابی، دانشگاه تهران، قم، ایران

4 دانشیار گروه مدیریت صنعتی، دانشکده مدیریت و اقتصاد، دانشگاه تربیت مدرس، تهران، ایران

چکیده

هدف:یکی از دغدغه‏های متولیان بازار پیش‏بینی تأثیرات استراتژی‏های جدید باتوجه به ناهمگن بودن، عقلانیت محدود و عوامل رفتاری در تصمیم‏گیری سهامداران است. بازار سهام ایران همواره با نواسانات شدیدی روبرو بوده، آگاهی از تأثیرات استراتژی‏ها قبل از اجرا به متولیان در جهت کاراتر نمودن بازار کمک می‏نماید. هدف اصلی این تحقیق ایجاد یک بازار مصنوعی مطابق با بازار سهام ایران بوده به نحوی که بتوان سناریوهای مختلف را شبیه‏سازی نمود.
روش:یکی از حوزه‏های نوظهور در تحقیق در عملیات «تحقیق در عملیات رفتاری» است که با ابزار مدلسازی مبتنی برعامل، ما را در حل این مسئله یاری می‏رساند. در این پژوهش با تمرکز بر قابلیت‏های مدلسازی مبتنی بر عامل، سهامداران، اوراق قابل معامله شامل انواع سهام و اوراق بدون ریسک و قوانین معاملاتی مدلسازی می‏شوند.
یافته‏ها:عامل‏ها در این بازار مصنوعی در هر دوره معاملاتی مطابق با استراتژی معاملاتی و یادگیری‏های صورت پذیرفته اقدام به پیشنهاد خرید، فروش و در نهایت بازارساز مطابق با مکانیزم حراج، شروع به تطبیق سفارشات و انجام عملیات تسویه و پایاپای می‏نمایند. جهت بررسی اعتبار مدل، خروجی آماری این بازار را با مشخصه‏های آماری بازارهای مالی تطبیق داده و پس از تأیید اعتبار مدل، سناریو حذف دامنه نوسان قیمت و حذف سهامداران آگاه و تأثیرات آن بر روی قیمت سهام بررسی شدند.
نتیجه گیری: مطابق با سناریوهای‏های شبیه‏سازی شده بازار سهام ایران با توجه به نابالغ بودن با حذف مکانیزم‏های کنترلی مثل دامنه نوسان قیمت در کوتاه مدت به شدت پر نوسان بوده اما در بلند مدت بازار به سمت کارایی هر بیشتر متمایل می‏شود.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

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

نویسندگان [English]

  • Adel Azar 1
  • Alireza Saranj 2
  • Ali asghar Sadeghi Moghadam 3
  • Ali Rajabzadeh 4
  • Hashem Moazzez 2
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
چکیده [English]

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.

کلیدواژه‌ها [English]

  • Agent-based modeling
  • Artificial market
  • Behavioral Operations Research
  • simulation
  • Stock market

مراجع

 
 
سعیدی، علی؛ و فرهانیان، سید محمد جواد (1394). مبانی اقتصاد و مالی رفتاری. تهران: انتشارات بورس (وابسته به شرکت اطلاع رسانی و خدمات بورس).
رستگار، محمدعلی؛ و ساعدی فر، خاطره (1396). استراتژی بهینه اجرای معاملات بزرگ با رویکرد شبیه‌سازی عامل‎گرا. تحقیقات مالی، (2)19، 239-262.
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.
 
 
 
 
 
 
سعیدی، علی؛ و فرهانیان، سید محمد جواد (1394). مبانی اقتصاد و مالی رفتاری. تهران: انتشارات بورس (وابسته به شرکت اطلاع رسانی و خدمات بورس).
رستگار، محمدعلی؛ و ساعدی فر، خاطره (1396). استراتژی بهینه اجرای معاملات بزرگ با رویکرد شبیه‌سازی عامل‎گرا. تحقیقات مالی، (2)19، 239-262.
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.
 
 
 
 
 
تحقیقات مالی
دوره 20، شماره 2
1397
صفحه 130-150

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