Predicting the Comprehensive Stress Index of Iran’s Capital Market Using Hybrid Machine Learning Modeling and Exogenous Variables
Keywords:
systematic risk, Granger causality , ∆CoVaR , Capital market, DCC–MGARCH , machine learning , pervasive stress indexAbstract
Objective: The study aims to develop an efficient predictive model for Iran’s capital market stress index by integrating machine learning algorithms with dynamic econometric modeling.
Methodology: Daily time-series data of 20 industrial indices from the Tehran Stock Exchange over a ten-year period (2014–2024) were analyzed. Log returns were calculated, and normality, stationarity, and ARCH effects were tested. Systemic risk was measured using the DCC–MGARCH framework combined with the ∆CoVaR criterion. Three supervised machine learning models—Random Forest, Support Vector Regression, and Artificial Neural Networks—were compared to determine feature importance and extract index weights. The final stress index was constructed using dynamic conditional correlations and normalized risk weights.
Findings: Most indices exhibited non-normality but were stationary and heteroskedastic. The Engle–Sheppard test confirmed dynamic conditional correlations. Based on ∆CoVaR, automotive, real estate, paper products, and basic metals demonstrated the highest systemic risk. Random Forest achieved the lowest MAE and RMSE, outperforming other models. The constructed stress index successfully identified high-risk periods and issued early warning signals prior to major market downturns. Granger causality tests revealed a unidirectional causal effect from the free-market exchange rate to the stress index, while gold coin returns showed no significant influence.
Conclusion: The hybrid modeling framework—combining DCC–MGARCH with machine learning—provides a reliable, forward-looking tool for monitoring systemic risk and forecasting comprehensive market stress in Iran’s capital market.
Downloads
References
Ahmed, H., Aslam, F., & Ferreira, P. (2024). Navigating Choppy Waters: Interplay between Financial Stress and Commodity Market Indices. Fractal and Fractional, 8(2), 96. https://doi.org/10.3390/fractalfract8020096
Asadi, G. H., Abdoh Tabrizi, H., & Farazmand, S. (2022). Exchange Rates, Gold Coin Prices, and Herding in the Stock Market. Iranian Economic Review, 26(2), 389-406. https://ier.ut.ac.ir/article_88170.html
askar, W. I., laftah, F. M., & habib, H. B. (2022). Investigating the Relationship between Exchange Rate and Stock Price ( Case Study : iran). Muthanna Journal of Administrative and Economic Sciences. https://www.researchgate.net/publication/363307089_Investigating_the_Relationship_between_Exchange_Rate_and_Stock_Price_Case_Study_iran
Behravan, I., & Razavi, S. M. (2020). Stock price prediction using machine learning and swarm intelligence. Journal of electrical and computer engineering innovations (JECEI), 8(1), 31-40. https://jecei.sru.ac.ir/article_1421_6d94cea044bc908eba67a7ad40fe184e.pdf
Choi, S. B., Sauka, K., & Lee, M. (2024). Dynamic capital structure adjustment: an integrated analysis of firm-specific and macroeconomic factors in Korean firms. International Journal of Financial Studies, 12(1), 26. https://doi.org/10.3390/ijfs12010026
Dewi, L. P., Fitria, S. A., Nidia, S. P., & Sitio, V. (2024). Pasar modal dan pengaruhnya terhadap perekonomian di Indonesia. JAKA (JURNAL AKUNTANSI, KEUANGAN, DAN AUDITING) Учредители: Fakultas Ekonomi dan Bisnis Universitas Dian Nuswantoro, 5(1), 196-214. https://doi.org/10.56696/jaka.v5i1.10755
Fallah, M. F., Pourmansouri, R., & Ahmadpour, B. (2024). Presenting a new deep learning-based method with the incorporation of error effects to predict certain cryptocurrencies. International Review of Financial Analysis, 95, 103466. https://doi.org/10.1016/j.irfa.2024.103466
Ghaffari Gol Afshani, R., FallahShams, M. F., Safa, M., & Jahangirnia, H. (2023). Designing a Financial Volatility Index (FVI): approach to machine learning models in uncertainty. Macroeconomics and Finance in Emerging Market Economies, 1-30. https://doi.org/10.1080/17520843.2022.2154480
Gholizadeh, M., & Erfani, A. (2020). Analyzing the Structural Breaks and the Exchange Market Turbulence on Volatility Spillovers Between Exchange Rates and Tehran Stock Exchange. Financial Knowledge of Securities Analysis. https://doi.org/10.2139/ssrn.3730886
Jabil, Y. I., Ibbih, J. M., & Akawu, F. A. (2025). Macroeconomic variables and the performance of Nigerian capital market. Journal of Management Science and Career Development. https://www.researchgate.net/publication/388991179_MACROECONOMIC_VARIABLES_AND_THE_PERFORMANCE_OF_NIGERIAN_CAPITAL_MARKET
Kanthimathinathan, S. (2020). Capital Market Instruments and its Importance Role in Stock Market in India.
Nicolescu, L. (2020). Macroeconomic factors and capital markets. Selected experiences in Central and Eastern Europe. Management Dynamics in the Knowledge Economy, 8(2), 159-174. https://doi.org/10.2478/mdke-2020-0011
Okorontah, C. F., & John, O. O. (2022). The Nigerian Capital Market and Selected Macroeconomic Variables: An Econometric Analysis. https://www.researchgate.net/publication/362652065_The_Nigerian_Capital_Market_and_Selected_Macroeconomic_Variables_An_Econometric_Analysis
Permata, C. P., & Ghoni, M. A. (2019). Peranan pasar modal dalam perekonomian negara Indonesia. Jurnal AkunStie (JAS), 5(2), 50-61.
Pourmansouri, R., Fallahshams, M., & Afshani, R. G. G. (2025). Designing a financial stress index based on the GHARCH-DCC approach and machine learning models. Journal of the Knowledge Economy, 16(1), 2689-2718. https://doi.org/10.1007/s13132-024-02075-9
Shamisavi, M., & Jahanshahi, A. (2022). Forecasting tehran stock exchange trend with time series analysis, fundamental data, and sentiment analysis in news. 2022 30th International Conference on Electrical Engineering (ICEE),
Tavakoli, M., & Doosti, H. (2021). Forecasting the Tehran Stock market by Machine Learning Methods using a New Loss Function. Advances in Mathematical Finance and Applications, 6(2), 194-205. https://journals.iau.ir/article_678748.html
Torki, L., Samadi, S., & Safarpoor, Z. (2021). Analysis of the effect of macroeconomic variables on fluctuation of future gold market in Iran. International Journal of New Political Economy, 2(1), 251-271. https://doi.org/10.29252/jep.2.1.251
Yousfani, K., Iftikhar, H., Rodrigues, P. C., Armas, E. A. T., & López-Gonzales, J. L. (2025). Global Shocks and Local Fragilities: A Financial Stress Index Approach to Pakistan's Monetary and Asset Market Dynamics. Economies, 13(8), 243. https://doi.org/10.3390/economies13080243
Zhao, C. (2024). Key Factors for Financial Market Stability and Policy Response. Journal of World Economy, 3(3), 15-18. https://doi.org/10.56397/JWE.2024.09.02
Downloads
Published
Submitted
Revised
Accepted
Issue
Section
License
Copyright (c) 2025 الهام باباحیدریان , فرهاد حنیفی, میرفیض فلاح شمس (نویسنده)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
