Modeling Investors’ Financial Behavior During Capital Market Volatility and Forecasting Future Market Trends Using Genetic Algorithm Simulation
Keywords:
financial behavior, genetic algorithm, market volatility, capital market forecasting, support vector machine, behavioral financeAbstract
This study was conducted with the aim of modeling investors’ financial behavior during capital market volatility and forecasting future market trends using genetic algorithm simulation. The principal objective was to develop an integrated framework for the dynamic simulation of learning, selection, and evolution of investment strategies under conditions of uncertainty and to establish a bridge between behavioral finance and quantitative market forecasting. The present research adopted a mixed-methods approach (qualitative–quantitative). In the qualitative phase, through in-depth interviews with 20 experts and by applying grounded theory methodology and interpretive structural modeling, the factors and components of financial behavior were identified. In the quantitative phase, a researcher-developed questionnaire consisting of 30 items across four dimensions (risk tolerance, response to market volatility, buying and selling strategies, and liquidity and trading volume) was distributed among 350 active investors of the Tehran Stock Exchange. Financial behavior was ranked using the TOPSIS method, and the model was tested through structural equation modeling. Subsequently, future market trends were forecast using support vector machines and genetic algorithms implemented in Python software. The results indicated that large investors exhibited the most optimal financial behavior, with a closeness coefficient of 0.78. The model fit indices (GFI = 0.97, NFI = 0.93, RMSEA = 0.031) were satisfactory. The genetic algorithm achieved a coefficient of determination of 0.96, demonstrating very high accuracy in forecasting market trends. Scenario analysis revealed that improvements in liquidity and trading volume exerted the greatest impact on future market trends (21.55–23.69 percent), while risk tolerance constituted the second most influential factor (18.74–19.51 percent). Accordingly, genetic algorithms represent an efficient tool for modeling investors’ financial behavior and forecasting market trends. Enhancing the financial literacy of retail investors, particularly in the areas of liquidity management and rational risk tolerance, can contribute significantly to the stability of the capital market.
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References
1. Baker M, Wurgler J. Investor sentiment in the stock market. Journal of Economic Perspectives. 2007;21(2):129-51. doi: 10.1257/jep.21.2.129.
2. Shiller RJ. Irrational exuberance: Princeton University Press; 2013.
3. Zhou Z. The impact of investor sentiment fluctuations on stock market liquidity and volatility: Implications for market returns. Finance Research Letters. 2025.
4. Huang W, Nakamori Y, Wang SY. Forecasting stock market movement. Expert Systems with Applications. 2005;28(2):231-8. doi: 10.1016/j.eswa.2004.10.004.
5. Zamani M, Afser A, Saghaqi Nejad S, Bayat A. Expert system for forecasting stock prices and optimizing investment portfolios using fuzzy neural networks, fuzzy modeling, and genetic algorithms. Financial Engineering and Securities Management (Portfolio Management). 2014;5(21):107-30. doi: 10.1016/j.fesys.2014.01.001.
6. Atsalakis GS, Valavanis KP. Surveying stock market forecasting techniques. Expert Systems with Applications. 2009;36(3):5932-41. doi: 10.1016/j.eswa.2008.07.006.
7. Krauss C, Do XA, Huck N. Deep neural networks in stock market prediction. European Journal of Operational Research. 2017;258(2):689-97. doi: 10.1016/j.ejor.2016.10.031.
8. Oyewole AT, Adeoye OB, Addy WA, Okoye CC, Ofodile OC, Ugochukwu CE. Predicting stock market movements using neural networks: A review and application study. Computer Science & IT Research Journal. 2024;5(3):651-70. doi: 10.51594/csitrj.v5i3.912.
9. Kadiri H, Oukhouya H, Belkhoutout K. A comparative study of hybrid and individual models for predicting the Moroccan MASI index: Integrating machine learning and deep learning approaches. Scientific African. 2025;28:e02671. doi: 10.1016/j.sciaf.2025.e02671.
10. Chou JS, Chen KE. Optimizing investment portfolios with a sequential ensemble of decision tree-based models and the FBI algorithm for efficient financial analysis. Applied Soft Computing. 2024;158:111550. doi: 10.1016/j.asoc.2024.111550.
11. Rezaeian S, Taleghani M, Sharjesharifi A. Development of a comprehensive model for predicting stock prices in the Tehran Stock Exchange using interpretive structural modeling. Asset Management and Financial Supply. 2024;12(2):39-58. doi: 10.22108/amf.2024.138983.1821.
12. Hommes C. Heterogeneous agent models in economics and finance. Handbook of Computational Economics. 2006;2:1109-86. doi: 10.1016/S1574-0021(05)02023-X.
13. LeBaron B. Agent-based computational finance. Handbook of Computational Economics. 2006;2:1187-233. doi: 10.1016/S1574-0021(05)02024-1.
14. Chen SH, Chang CL, Du YR. Agent-based economic models and econometrics. The Knowledge Engineering Review. 2010;25(2):187-219. doi: 10.1017/S0269888912000136.
15. Hommes C, Wagener F. Complex evolutionary systems in behavioral finance. Handbook of Financial Markets. 2009:217-76. doi: 10.1016/B978-012374258-2.50008-7.
16. Bouchaud JP, Farmer JD, Lillo F. How markets slowly digest changes in supply and demand. Quantitative Finance. 2018;9(7):737-50. doi: 10.2139/ssrn.1266681.
17. Dadgar Y, Varmazyari B. Empirical testing of stability, predictability, and volatility in the capital markets of the Persian Gulf countries. Economic Modeling. 2011;5(16):1-20. doi: 10.1016/j.econmod.2011.01.002.
18. Rostami J, Fattahi S, Sohaili K. Modeling and estimating stock returns in the Tehran Stock Exchange using dynamic models. Financial Economics. 2023;17(62):185-216. doi: 10.30495/fed.2023.700132.
19. Taheri S, Abdolbaghi Attaabadi A, Vaziri Sarshak M, Arman M. The effect of short-term gold and foreign exchange volatility cycles on capital market volatility. Financial Economics. 2023;17(63):95-116. doi: 10.30495/fed.2023.702186.
20. Rahavi M, Zamerdian G, Bani Mahd B. Behavioral modeling of stock index volatility with emphasis on market risk and trading volume volatility using structural vector autoregression models. Journal of Accounting, Finance and Computational Intelligence. 2024;2(2):19-37. doi: 10.1016/j.jafci.2024.01.003.
21. Saadat-Zadeh Hessar B, Abdi R, Mohammadzadeh Salteh H, Narimani M. Examining the relationship between investors' cognitive bias and stock price volatility. Financial Economics. 2021;15(56):303-20. doi: 10.30495/fed.2021.687880.
22. Gao J, Fan C, Liu T, Bai X, Li W, Tan H. Embracing market dynamics in the post-COVID era: A data-driven analysis of investor sentiment and behavioral characteristics in stock index futures returns. Omega. 2025;131:103193. doi: 10.1016/j.omega.2024.103193.
23. Akdoğan YE, Anbar A. More than just sentiment: Using social, cognitive, and behavioral information of social media to predict stock markets with artificial intelligence and big data. Borsa Istanbul Review. 2024;24(Supplement 2):61-82. doi: 10.1016/j.bir.2024.12.003.
24. Aghaei M. An empirical analysis of investors' herding behavior in the stock market: Evidence from different economic and social conditions in Iran. Asset Management and Financing. 2025;13(3):29-56. doi: 10.22108/amf.2025.142503.1913.
25. Aghaei M. An empirical analysis of herd behavior among investors in the stock market: Evidence from various economic and social conditions in Iran. Asset Management and Financial Supply. 2025;13(3):29-56. doi: 10.22108/amf.2025.142503.1913.
26. Akin I, Akin M. Behavioral finance impacts on US stock market volatility: an analysis of market anomalies. Behavioural Public Policy. 2024:1-25. doi: 10.1017/bpp.2024.13.
27. Dolou M, Heydari T. Stock index forecasting using a hybrid of artificial neural networks and harmony search and genetic algorithms. Financial Economics. 2017;11(40):1-24. doi: 10.1016/j.fed.2017.01.001.
28. Hashemi SM, Afshar-Kazemi MA, Tolouei Ashlaghi A, Minouei M. Integrating genetic algorithms into portfolio optimization in investors' financial decision-making. Journal of Value Creation in Business Management. 2023;3(4):73-88. doi: 10.22034/jvcbm.2023.412174.1166.
29. Okoh OF, Grace I. Mathematical modeling and machine learning for economic forecasting: A hybrid approach to predicting market trends. Acta Electronica Malaysia. 2022;6(1):07-15. doi: 10.26480/aem.01.2022.07.15.
30. Md Arman H. Artificial Intelligence-Based Financial Analysis Models for Predicting Market Risk and Investment Decisions in US Companies. ASRC Procedia: Global Perspectives in Science and Scholarship. 2025;1(01):1066-95. doi: 10.63125/9csehp36.
31. Abbas Hassan S, Piri P, Chalaki P. Proposing an optimal decision-making model for investors using artificial intelligence with an emphasis on financial reporting transparency. Asset Management and Financial Supply. 2025:1-24. doi: 10.22108/amf.2025.143308.1934.
32. Wang Q, Li Y, Li R. Integrating artificial intelligence in energy transition: A comprehensive review. Energy Strategy Reviews. 2025;57:101600. doi: 10.1016/j.esr.2024.101600.
33. Hanlon M, Yeung K, Zuo L. Behavioral economics of accounting: A review of archival research on individual decision makers. Contemporary Accounting Research. 2021;39(2):1150-214. doi: 10.1111/1911-3846.12739.
34. Taslimpour A, Askarzadeh G, Qalmaq K, Nasiri H. Forecasting the Tehran Stock Exchange index using the NARX neural network model. Asset Management and Financial Supply. 2026;14(1):21-48. doi: 10.22108/amf.2025.143461.1940.
35. Salemi Najafabadi M, Saadatfar N, Karimi F. Forecasting returns on investment opportunities in Iran's financial markets considering market interactions and forming an optimal investment portfolio using artificial intelligence. Asset Management and Financing. 2014;2(4):35-50. doi: 10.22108/amf.2014.19897.
36. Farmer JD, Foley D. The economy needs agent-based modelling. Nature. 2009;460:685-6. doi: 10.1038/460685a.
37. Farmer JD, Hepburn C, Mealy P, Teytelboym A. A third wave in the economics of climate change. Environmental and Resource Economics. 2019;62(2):329-57. doi: 10.1007/s10640-015-9965-2.
38. Sun Y, Jin Q, Cheng Q, Guo K. New tool for stock investment risk management: Trend forecasting based on individual investor behavior. Industrial Management & Data Systems. 2020;120(2):388-405. doi: 10.1108/IMDS-03-2019-0125.
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Copyright (c) 2025 Iman Hedayati (Author); Alireza Ghiyasvand; Farid Sefaty (Author)
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