Designing a Financial Market Analysis Training Model for Institutional Investors Based on Market Forecasting and Data-Driven Learning

Document Type : Original Research Manuscripts

Authors

1 Ph.D. Student, Department of Accounting and Financial, ST.C., Islamic Azad University, Tehran, Iran.

2 Assistant Professor, Department of Accounting and Financial, ST.C., Islamic Azad University, Tehran, Iran.

10.22034/lss.2026.590426.1075
Abstract
This research aims to design a financial market analysis training model to improve the quality of institutional investors’ investment decisions. By examining the stock trends, gold, foreign exchange, and housing markets, this research has evaluated the future status of these markets as a basis for investment decision-making training. The results show that the stock market has experienced significant growth in different periods, including 2004, 2011, 2014, 2016, and 2019, and the peak of this trend in 2019 was accompanied by the recording of historical records of the Tehran Stock Exchange; However, after that, this market entered a period of stagnation and did not experience significant growth. The foreign exchange market also experienced relative growth in the first half of 2026, but then faced a downward trend. On the other hand, the housing and stock markets are still in a recession in 2026. Accordingly, the proposed model for teaching financial market analysis by emphasizing trend analysis, market forecasting and risk assessment can improve the decision-making ability of institutional investors and provide the basis for optimizing investment portfolios. Based on the forecasting results, a financial market analysis training model was proposed consisting of four main learning modules: market trend analysis, forecasting interpretation, risk assessment and portfolio diversification, and investment decision-making. The model integrates real market data, forecasting techniques, and case-based learning approaches to enhance institutional investors’ analytical competencies.

Keywords

Subjects

Ashrafzadeh, M., Taheri, H. M., Gharehgozlou, M., & Zolfani, S. H. (2023). Clustering-based return prediction model for stock pre-selection in portfolio optimization using PSO-CNN+MVF. Journal of King Saud University - Computer and Information Sciences, 35(9), Article 101737. https://doi.org/10.1016/j.jksuci.2023.101737
Azami, S., & Abbasi, E. (2024). The effect of institutional investors’ time horizon on the ratio of debt financed through banks in the Tehran Stock Exchange. In Proceedings of the Tenth International Conference on Management and Accounting Sciences, Tehran, Iran. [In Persian]
Barber, B. M., & Odean, T. (2001). Boys will be boys: Gender, overconfidence, and common stock investment. Quarterly Journal of Economics, 116(1), 261–292. https://doi.org/10.1162/003355301556082
Baur, D. G., & Lucey, B. M. (2010). Is gold a hedge or a safe haven? An analysis of stocks, bonds and gold. Financial Review, 45(2), 217–229. https://doi.org/10.1111/j.1540-6288.2010.00244.x
Bedoui, R., Benkraiem, R., Guesmi, K., & Kedidi, I. (2023). Portfolio optimization through hybrid deep learning and genetic algorithms vine Copula-GARCH-EVT-CVaR model. Technological Forecasting and Social Change, 197, Article 122887. https://doi.org/10.1016/j.techfore.2023.122887
Berouaga, Y., El Msiyah, C., & Madkour, J. (2023). Portfolio optimization using minimum spanning tree model in the Moroccan stock exchange market. International Journal of Financial Studies, 11(2), Article 53. https://doi.org/10.3390/ijfs11020053
Case, K. E., & Shiller, R. J. (2003). Is there a bubble in the housing market? Brookings Papers on Economic Activity, 2003(2), 299–362. https://doi.org/10.1353/eca.2004.0004
Chen, Y. S., Kao, C. L. M., & Liu, P. H. (2024). Extracting stock predictive information in mutual fund managers’ portfolio decisions through machine learning with hypergraph. Computational Economics. Advance online publication. https://doi.org/10.1007/s10614-024-10673-7
Cui, T., Du, N., Yang, X., & Ding, S. (2024). Multi-period portfolio optimization using a deep reinforcement learning hyper-heuristic approach. Technological Forecasting and Social Change, 198, Article 122944. https://doi.org/10.1016/j.techfore.2023.122944
Derakhshan, K., & Sheybani Tadarroji, A. (2024). A study of the relationship between short-term and long-term institutional investors and the agency cost of debt. In Proceedings of the Fifteenth International Conference on Accounting, Management, and Innovation in Business, Tehran, Iran.
Engel, C., Mark, N. C., & West, K. D. (2007). Exchange rate models are not as bad as you think. NBER Macroeconomics Annual, 22, 381–441. https://doi.org/10.1086/589859
Espoukeh, J. (2024). Investigating the effect of political communications on the relationship between institutional investors and the sustainability quality of financial reporting in companies listed on the Tehran Stock Exchange. In Proceedings of the First National Conference on Novel Perspectives in Management and Accounting with an Organizational Transformation Approach, Shiraz, Iran. [In Persian]
Fama, E. F. (1970). Efficient capital markets: A review of theory and empirical work. Journal of Finance, 25(2), 383–417. https://doi.org/10.1111/j.1540-6261.1970.tb00518.x
Fiori, A. M., & Porro, F. (2023). A compositional analysis of systemic risk in European financial institutions. Annals of Finance, 19(1), 1–30. https://doi.org/10.1007/s10436-022-00420-7
Gioia, D. G., Fior, J., & Cagliero, L. (2023). Early portfolio pruning: A scalable approach to hybrid portfolio selection. Knowledge and Information Systems, 65(6), 2485–2508. https://doi.org/10.1007/s10115-023-01832-7
Grinblatt, M., & Keloharju, M. (2001). What makes investors trade? Journal of Finance, 56(2), 589–616. https://doi.org/10.1111/0022-1082.00338
Guarino, A., Santoro, D., Grilli, L., & Lozupone, V. (2024). EvoFolio: A portfolio optimization method based on multi-objective evolutionary algorithms. Neural Computing and Applications, 36(13), 7221–7243. https://doi.org/10.1007/s00521-024-09456-w
Liu, Y., Zhou, Y., & Niu, J. (2023). Portfolio optimization: A multi-period model with dynamic risk preference and minimum lots of transaction. Finance Research Letters, 55, Article 103964. https://doi.org/10.1016/j.frl.2023.103964
Lo, A. W. (2004). The adaptive markets hypothesis: Market efficiency from an evolutionary perspective. Journal of Portfolio Management, 30(5), 15–29. https://doi.org/10.3905/jpm.2004.440269
Lusardi, A., & Mitchell, O. S. (2014). The economic importance of financial literacy: Theory and evidence. Journal of Economic Literature, 52(1), 5–44. https://doi.org/10.1257/jel.52.1.5
Martínez-Barbero, X., Cervelló-Royo, R., & Ribal, J. (2024). Portfolio optimization with prediction-based return using long short-term memory neural networks: Testing on upward and downward European markets. Computational Economics. Advance online publication. https://doi.org/10.1007/s10614-024-10604-6
Mehlawat, M. K., Gupta, P., & Khan, A. Z. (2023). An integrated fuzzy-grey relational analysis approach to portfolio optimization. Applied Intelligence, 53(4), 3804–3835. https://doi.org/10.1007/s10489-022-03499-z
Mestre, R. (2023). Stock profiling using time-frequency varying systematic risk measure. Financial Innovation, 9(1), Article 52. https://doi.org/10.1186/s40854-023-00453-x
Mohaqeq, A., Bayesteh, A., & Mirsoleimani, F. (2024). The relationship between stock price crash risk, institutional investors, and stock returns of companies active in the capital market. In Proceedings of the Thirteenth International Conference on Modern Research in Accounting, Management, and Human Sciences in the Third Millennium, Tehran, Iran. [In Persian]
Papathanasiou, S., Kenourgios, D., Koutsokostas, D., & Grigoriadis, I. (2024). The dynamic connectedness between collateralized loan obligations and major asset classes: A TVP-VAR approach and portfolio hedging strategies for investors. Empirical Economics. Advance online publication. https://doi.org/10.1007/s00181-024-02583-2
Pourmahdian Davarani, A. (2024). Investigating the effect of institutional ownership on stock returns in companies listed on the Tehran Stock Exchange. In Proceedings of the Tenth International Conference on Management and Accounting Sciences, Tehran, Iran. [In Persian]
Ramos, H. P., Righi, M. B., Guedes, P. C., & Müller, F. M. (2023). A comparison of risk measures for portfolio optimization with cardinality constraints. Expert Systems with Applications, 228, Article 120412. https://doi.org/10.1016/j.eswa.2023.120412
Soltanipour Sardou, M. (2024). Investigating the effect of institutional investors and financial statement entropy on stock returns in companies listed on the Tehran Stock Exchange. In Proceedings of the Twentieth National Conference on Economics, Management, and Accounting, Shirvan, Iran. [In Persian]
Vega-Gámez, F., & Alonso-González, P. J. (2024). How likely is it to beat the target at different investment horizons: An approach using compositional data in strategic portfolios. Financial Innovation, 10(1), Article 125. https://doi.org/10.1186/s40854-023-00601-3

  • Receive Date 23 March 2026
  • Revise Date 12 April 2026
  • Accept Date 20 May 2026