تصمیم‌گیری‌های مدیریتی و تیپ‌های شخصیتی سرمایه‌گذاران بورس مطالعه‌ای با شبیه‌سازی عامل‌بنیان
محورهای موضوعی : اداره امور عمومی، حکمرانی، سیاستگذاری و خط مشی گذاری عمومی
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
کلید واژه: شبیه‌سازی‌عامل‌بنیان, بورس‌اوراق‌بهادار, متغیرهای‌کلان‌اقتصادی, مالی رفتاری,
چکیده مقاله :
با عنایت به پیچیدگی‌های حاکم بر اقتصاد و باتوجه‌به نقش تأثیرگذار بازارهای مالی بر اقتصاد، و اهمیت اقتصاد برای کشور و جامعه، روش‌ها و ابزارهایی که بتوانند ارزیابی، پیش‌بینی، کنترل و هدایت بازار و اقتصاد را به‌نحوی اثربخش و کارا در دسترس سیاست‌گذارانی چون وزارت اقتصاد و امور دارایی، سازمان بورس اوراق بهادار، بانک‌مرکزی، شورای‌عالی بورس یا وزارت صمت قرار دهند، از جایگاهی ویژه برخوردار خواهند شد. این اثربخشی و کارایی زمانی حاصل می‌شود که توجه به لایه‌های پنهان روابط سیستم‌ها مانند رفتارهای جمعی انسانی که بر پیچیدگی بازار و اقتصاد می‌افزاید، نادیده گرفته نشود. در پژوهش حاضر با به خدمت گیری ظرفیت‌های شبیه‌سازی‌عامل‌بنیان در پژوهشی ترکیبی، رفتار انسانی را با روش‌های کمی و کیفی ترکیب نموده و از فناوری شبیه‌سازی به‌عنوان سومین روش تحقیق علمی، علاوه بر رویکردهای قیاسی و استقرایی بهره برده‌ایم. پژوهش از نظر هدف توصیفی، و کاربردی بوده و شبیه‌سازی عامل‌های نظیر به نظیر بازیگران بازار واقعی در نرم‌افزار نت‌لوگو و با مدل نمودن بازار، اعتبارسنجی با روش روست و راند و تحلیل‌حساسیت با رویکرد بورگانوف انجام شده است. نتایج حاصل از پژوهش بیانگر وجود ارتباط مستقیم نسبت ریسک‌پذیری سرمایه‌گذاران با بازده بورس و رشد شاخص کل بورس است. باتوجه‌به پیش‌بینی انجام شده در مدل طراحی شده علاوه بر تیپ ریسکی، امکان سنجش و پایش سایر ویژگی‌های رفتاری سرمایه‌گذاران و همچنین با عنایت به تعریف دیگر عامل‌ها به‌ازای سایر بازیگران فعال بورس امکان مطالعه تأثیر رفتار ایشان بر شاخص کل و دیگر شاخص‌های بااهمیت نیز در دسترس قرار گرفته است، لذا در پژوهش حاضر برای نخستین‌بار تأثیر رفتارهای متغیرهای کلان اقتصادی بر رفتار کلیه بازیگران حاضر در بورس مدل و با ظرفیت‌های شبیه‌سازی‌عامل‌بنیان مدل‌سازی صورت‌گرفته است.
Personality Types of Stock Market Investors and Their Impact on Managerial Decisions: A Study Using Agent-Based Simulation.
Seyed Farhad Gooran Heydari
PhD student in Information Technology Management, Department of Information Technology Management, Science and Research Unit, Islamic Azad University, Tehran, Iran
Abbas Toloui eshlaghi
Professor, Department of Information Technology Management, Science and Research Unit, Islamic Azad University of Tehran, Iran
Ahmad Ebrahimi
Assistant Professor, Department of Industrial management and technology, Science and Research Unit, Islamic Azad University of Tehran, Iran
Mohammad Reza Motadel
Assistant Professor, Department of Management, Central Tehran Branch of Islamic Azad University,
Received: 17 July 2024 | Revised: 21 June 2024 | Accepted: 24 June 2024
Extended Abstract
Given the complexities of the economy and considering the influential role of financial markets on the economy, as well as the importance of the economy for the country and society, methods and tools that can effectively and efficiently assess, predict, control, and guide the market and economy in a manner accessible to policymakers such as the Ministry of Economy and Finance, Securities and Exchange Organization, Central Bank, High Council of Stock Exchange, or Ministry of Industry, will be in a special position. This effectiveness and efficiency are achieved when attention to hidden layers of system relationships such as collective human behavior, which adds to the complexity of the market and economy, is not overlooked. In the present study, by employing the capacities of agent-based simulation in a mixed-method research, human behavior is combined using quantitative and qualitative methods and simulation technology as the third method of scientific research, in addition to comparative and inductive approaches.
The research is descriptive and applied, and agent-to-agent simulations of real market players in NetLogo software with modeling the market, validation using Rust and Rand tests, and sensitivity analysis using the Borgonovo approach have been conducted.
The results of the study indicate a direct relationship between investors' risk tolerance and stock market returns and the overall stock market index growth. With the prediction made in the designed model, in addition to risk type, the possibility of assessing and monitoring other behavioral characteristics of investors, as well as with consideration of the definition of other factors for other active market players, the study of their behavior's impact on the overall index and other important indicators is also available.
In this study, for the first time, the influence of the behaviors of macroeconomic variables on the behavior of all players present in the stock market was modeled and simulated using agent-based simulation capacities.
Therefore, the collective behavior of unskilled and untrained individuals in the capital market, on the one hand, has caused them to surrender to the waves resulting from these decisions, and on the other hand, due to the uncertainty of the weight and role of market players in the resulting fluctuations, unnatural and unpredictable ups and downs, and the ability to control policymakers, it limits and in some cases also throws the flow out of control, which has caused investors and dense masses of people to distrust the stock market, and despite creating incentives or stability and control in parts of the market, we continue to witness investors' lack of interest in this market and the continuous outflow of small and medium-sized capital from the market.
This issue and the country's recent experiences correctly indicate that despite the fact that numerous studies have been conducted in the field of the capital market, traditional analyses or statistical and mathematical analyses do not have the necessary capabilities at times when psychological, social and sociological parameters need to be included in studies.
In fact, the dynamics and complexity of the capital market, which is due to the events behind the scenes of the market mechanism in the formation of prices and the non-commercial motives of the players present in the market, the existing approaches in market study that try to discover the relationships between variables by analyzing historical data and matching the results, have not been successful in solving the problem posed so far; Therefore, it seems that using the results of a simulated model of the country's stock exchange, as an artificial capital market by defining similar factors of influential players in the market and predicting the results of changing their behavior by controlling the behavior of other simulated factors, can prevent the repetition of similar trends in recent years.
This approach can replace traditional statistical and mathematical analyses that have ignored the role of psychology, behavioral, social and sociological factors. In short; in the present study, we have addressed this issue and designed an artificial market with factor-based simulation, defining the ratio of similar factors to similar ones of stock market players, and analyzing changes in their behavior due to changes in the outcome of macroeconomic variables.
The results of the sensitivity analysis indicated a direct relationship between the type of investors and their decisions, and consequently changes in the market trend and the overall stock market index. The most important feature of the designed market is its flexibility and expandability, which allows changes in input data, input variables, output variables, factors, and the way they interact with each other. This important feature, which stems from the important ability of factor-based simulation, allows policymakers and economic administrators to study, analyze, review, and monitor the results of changes in the artificial market and generalize the results to the real market with minimal time, energy, and cost, without making changes to the real market.
Given that the country's economic and market managers have access to confidential, historical, and personal data of the players present in the capital market, their use of the aforementioned data will provide them with much more practical results. Therefore, it is suggested that by utilizing the aforementioned data, the results of the present study, the capacities of the designed model, and the flexible, agile, and inexpensive development capabilities available in this model, which will be available with minimal time, energy, and cost, they should monitor, supervise, and take preventive controls against exceptional ups and downs that reduce the trustworthiness of the stock exchange.
Keywords: Agent-Based Simulation, Stock Exchange, Macroeconomic Variables, Behavioral Finance
Abasisir, Salman, Hashemi Gohar, Mohsen, and Feyzi, Ammar. (1401). Factor-based modeling of shareholder behavior in the Tehran stock market (case study: Mobarakeh Steel Company of Isfahan). Modern Research in Decision Making, 7(1), 88-114. SID. https://sid.ir/paper/1045884/fa #
Agliari, A., Naimzada, A., & Pecora, N. (2018). Boom-bust dynamics in a stock market participation model with heterogeneous traders. Journal of Economic Dynamics.
Azar, Adel, Saranj, Alireza, Sadeghi Moghadam, Aliasghar, Rajabzadeh, Ali, Mozahed, Hashem. (2018). Factor-oriented modeling of shareholder behavior in the Iranian capital market. Financial Research, 20(2) Doi:10.22059/frj2018.259369.1006670 #
Beiranvand Mehdi. (2017). Evaluating the Relationship between Investors' Behavior in the Face of Risk and Performance Indicators. Certified Public Accountant No. 39 https://www.noormags.ir/view/fa/articlepage/1348049 #
Berger a, Dave & H.J. Turtle. (2012). Cross-sectional performance and investor sentiment in a multiple risk factor model, Journal of Banking & Finance
Betshekan, Mohammad Hashem, and Mohseni, Hossein. (2018). Investigating the spillover of oil price fluctuations on stock market returns. Investment Knowledge, 7(25), 267-284. SID. https://sid.ir/paper/187974/fa #
DOI: 10.1007/s00354-021-00133-3 #
DOI: 10.1016/j.ijresmar.2011.04.002 #
DOI: 10.1016/j.jbankfin.2011.11.001 #
DOI: 10.1016/j.jebo.2020.01.004#
DOI: 10.1016/j.jedc.2018.04.007 #
DOI: 10.1111/itor.12944 #
DOI: 10.2139/ssrn.2710495 #
Ebrahimi, Mehrzad. (2019). Investigating the impact of macroeconomic variables on the Iranian stock market using data mining algorithms. Financial Economics 13(49), 283-309SID.https://sid.ir/paper/229287/fa #
Emanuele Borgonovo،· Marco Pangallo، Jan Rivkin، Leonardo Rizzo، Nicolaj Siggelkow. (2022).Sensitivity analysis of agent‑based models: a new protocol. Computational and Mathematical Organization Theory DOI: 10.1007/s10588-021-09358-5 #
Fakhari, Hossein, Nasiri, Mehrab. (2019). The effect of company performance on the risk of future stock price collapse. Financial Management Strategy, 8(3), 43 Doi: 10.22051/JFM.2019.25489.2037 #
Fouad Ben Abdelaziz ،Fatma Mrad. (2021). Multiagent systems for modeling the information game in a financial market. International Transactions in Operational Research.
Gao, Kang, Vytelingum, Perukrishnen, Weston, Stephen, Luk, Wayne and Guo, Ce (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 #
Ghorbani Naser, Babaei Ebrahim. (2015). Investigating the efficiency of EMA algorithm in solving optimization problems. Kermanshah: National Conference on Technology and Data with a Computer Engineering Approach https://www.esearchgate.net/publication/281297927 #
Gilbert, N., and K. Troitzsch. (2007). Simulation for the Social Scientist. George Mason University: McGraw-Hill. 2nd ed. GMU. DOI: 10.5565/rev/papers/v80n0.1837 #
Hadipour Hassan, Paytakhti Oskouei Seyed Ali, Alavi Matin Yaghoub, Rahmani Kamal-eddin. (1400). Factors affecting the volatility index in Tehran Stock Exchange (Case study: Basic Metals Industry). Industrial Management Studies Doi: 10.22054/jims.2021.57264.2581 #
Khoshnoud, Mehdi, Rahnamae Roudposhti, Fereydoun, and Nikomaram, Hashem. (2019). Optimization of Investment Pattern in Major Downturns of Tehran Stock Exchange in the Framework of Heterogeneous Factors Approach and Basis Factor Modeling Using Genetic Algorithm. Financial Engineering and Securities Management (Portfolio Management), 11(42), 248-271. SID. https://sid.ir/paper/367632/fa #
Lovric, M. (2011, March 25). Behavioral Finance and Agent-Based Artificial Markets (No. EPS-2011--F&A).ERIM Ph.D. Research in Management. Retrieved from http://hdl.handle.net/1765/22814 #
Macal Charles; North Michael. (2014). Introductory tutorial: Agent-based modeling and simulation . Savannah, GA, USA: Proceedings of the Winter Simulation Conference. DOI:10.1109/WSC.2014.7019874 #
Mishra, R. (2018). Financial Literacy, Risk Tolerance and Stock Market Participation. Asian Economic and Financial Review. DOI:10.18488/journal. aefr.2018.812.1457.1471 #
Mizuta Takanobu. (2021).An Agent-Based Model for Designing a Financial Market That Works Well. IEEE Symposium Series on Computational Intelligence. DOI:10.1109/SSCI47803.2020.9308376 #
Mizuta Takanobu. (2022). A Brief Review of Recent Artificial Market Simulation (Agent-Based Model) Studies for Financial Market Regulations and Rules.
Mizuta Takanobu، Kosei Takashima، Isao Yagi .Instability of financial markets by optimizing investment strategies investigated by an agent-based model. (2022) .Computational Intelligence for Financial Engineering and Economics. DOI:10.1109/CIFEr52523.2022.9776207 #
Mohamed Amine Souissi, Khalid Bensaid and Rachid Ellaia (2018). Multi-agent modeling and simulation of a stock market. Investment Management and Financial Innovations. DOI:10.21511/imfi.15(4).2018.10 #
Mohammadi Ali, Mosleh Shirazi Alinaghi, Abbasi Abbas, Akhlaq Poursaeed. (2019). Scenario planning of the effect of changes in factors affecting the market value of Tehran Stock Exchange using a system dynamics approach. Financial Management Perspective DOI:10.52547/jfmp.9.26.33 #
Muhammad Asif Khan, Saima Aziz, Shahid Mehmood and Anita Tangl (2024). Role of behavioral biases in the investment decisions of Pakistan StockExchange investors: Moderating role of investment experience. Investment Management and Financial Innovations. doi:10.21511/imfi.21(1).2024.12 #
Mukhtar Band, Mahmoud, Tehrani, Reza, Al-Abboudeh, Manal. (1403). Estimating the impact of fundamental macroeconomic factors on the capital market (variable frequency composite data approach). Financial Research DoI:10.22059/frj.2024.368065.1007538 #
Rand, W., & Rust, R. T. (2011). Intern . J . of Research in Marketing Agent-based modeling in marketing : Guidelines for rigor. International Journal of Research in Marketing.
Rastegar Sorkheh, Mohammad Ali, Khalaj, Ghoncheh, (2019), The Effect of Algorithmic Market Makers in Tehran Stock Exchange: An Agent-Based Modeling Approach, Tarbiat Modares University, Faculty of Industrial, System and Productivity Engineering
Robert Axelrod , Advancing the Art of Simulation in the Social Sciences. (2003). Japanese Journal for Management Information System, Special Issue on Agent-Based Modeling, Vol. 12. https://public.websites.umich.edu/~axe/research/AdvancingArtSim2003.pdf #
Sadek Benhammada ،Frédéric Amblard. (2021). An Agent-Based Model to Study Informational Cascades in Financial Markets. New Generation Computing.
Saltelli A, Bammer G, Bruno I, Charters E, Di Fiore M, Didier E, Espeland WN, Kay J, Lo Piano S, May D, Pielke RJ, Portaluri T, Porter TM, Puy A, Rafols I, Ravetz JR, Reinert E, Sarewitz D, Start PB, Stirling A, van der Sluijs JP, Vineis P. (2020). Five ways to ensure that models serve society: a manifestohttps. DOI: 10.1038/d41586-020-01812-9 #
Shirazian, Zahra, Nikomaram, Hashem, Rahnemae Roudposhti, Fereydoun, and Torabi, Taghi. (2018). Clustering Volatility in Financial Markets with an Agent-Based Simulation Model. Financial Engineering and Securities Management (Portfolio Management), 9(36), 201-224. SID. https://sid.ir/paper/197529/fa #
SID. http://parseh.modares.ac.ir/thesis.php?id=10003527&sid=1&slc_lang=fa #
Vakili Fard, Hamid Reza, Khoshnoud, Mehdi, Forough Nejad, Heydar, and Osoulian, Mohammad. (2014). Agent-based modeling in financial markets. Investment Knowledge, 3(12), 139-158. SID.https://sid.ir/paper/490488/fa #
Valizadeh, Farzaneh, Mohammadzadeh, Amir, Sayqali, Mohsen, Torabian, Mohsen. (1400). Presenting a model for predicting factors affecting the risk of stock price collapse in Tehran Stock Exchange. Financial Management Perspective Doi: 10.52547/jfmp.11.33.217 #
Westphal, Rebecca and Sornette, Didier, Market Impact and Performance of Arbitrageurs of Financial Bubbles in An Agent-Based Model (2020). Swiss Finance Institute Research Paper