Designing a Composite Comprehensive Stress Index for the Tehran Stock Exchange Using a Machine Learning Approach
Keywords:
Systemic risk, financial contagion, comprehensive stress index, DCC-MGARCH, random forest algorithmAbstract
This study aimed to design and validate a comprehensive index to monitor systemic risk and market-wide stress in the Tehran Stock Exchange using advanced econometric models and machine learning algorithms. Daily time-series data of selected Tehran Stock Exchange indices from 2014 to 2024 were analyzed. Logarithmic returns were calculated, and the DCC-MGARCH model was applied to estimate the dynamic conditional correlation matrix and systemic risk metrics such as ΔCoVaR. To determine feature importance and optimal weighting of indices, three supervised learning algorithms (support vector regression, artificial neural networks, and random forest) were compared, with random forest selected due to superior predictive accuracy. The composite stress index was then constructed and validated using time stability analysis, stress (shock) testing, and logistic regression forecasting. The results revealed that automobile, real estate, paper products, and metal products sectors carried the highest systemic risk, while computer, coal, and textiles showed the lowest. The comprehensive stress index provided reliable early warning signals during market turbulence and achieved strong predictive performance, with an AUC of 0.801 and an accuracy of 89.9% in logistic regression analysis for shock detection. The developed composite stress index is a robust and dynamic tool for identifying vulnerability points and forecasting systemic crises in the Tehran Stock Exchange. It offers significant practical value for policymakers, market analysts, and regulatory authorities to strengthen market resilience and implement proactive risk management strategies. Incorporating macroeconomic variables and extending the historical dataset could further enhance its accuracy and generalizability.
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References
Akhtaruzzaman, M., Boubaker, S., & Sensoy, A. (2021). Financial contagion during COVID-19 crisis. Finance Research Letters, 38, 101604. https://doi.org/10.1016/j.frl.2020.101604
Bhaduri, A., Gupta, A., & Graham-Brady, L. (2022). Stress field prediction in fiber-reinforced composite materials using a deep learning approach. Composites Part B: Engineering, 238, 109879. https://doi.org/10.1016/j.compositesb.2022.109879
Boshkoska, M., Odzaklieska, D., & Spaseska, T. (2024). Understanding Financial Contagion: Insights Into Transmission Mechanisms and Their Contribution in Shock Intensification. Balkan and Near Eastern Journal of Social Sciences, 10(01), 119-125.
Chen, Y., Wang, G. J., Zhu, Y., Xie, C., & Salah Uddin, G. (2024). Identifying systemic risk drivers of FinTech and traditional financial institutions: machine learning-based prediction and interpretation. The European Journal of Finance, 30(18), 2157-2190. https://doi.org/10.1080/1351847X.2024.2358940
Covi, G., Montagna, M., & Torri, G. (2019). Economic shocks and contagion in the euro area banking sector: a new micro-structural approach. Financial stability review, 1, 1.
Dastkhan, H. (2020). Principles of Systemic Risk in Financial Markets: A Special Focus on the Application of Financial Networks.
Diaz-Ramos, R. E., Gomez-Cravioto, D. A., Trejo, L. A., López, C. F., & Medina-Pérez, M. A. (2021). Towards a resilience to stress index based on physiological response: a machine learning approach. Sensors, 21(24), 8293. https://doi.org/10.3390/s21248293
Ding, C., Zhang, Y., & Ding, T. (2023). A systematic hybrid machine learning approach for stress prediction. Peerj Computer Science, 9, e1154. https://doi.org/10.7717/peerj-cs.1154
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
Flavin, T. J., & Lagoa-Varela, D. (2020). Financial Contagion and Shock Transmission During the Global Financial Crisis: A Review of the Literature. In Emerging Tools and Strategies for Financial Management (pp. 173-197). https://doi.org/10.4018/978-1-7998-2440-4.ch008
Gai, P., & Kapadia, S. (2019). Networks and systemic risk in the financial system. Oxford Review of Economic Policy, 35(4), 586-613. https://doi.org/10.1093/oxrep/grz023
Gao, Y., Rojasavachai, R., Rouxelin, F., & Yang, L. (2025). Financial Illiteracy as a Systemic Risk: Implications for Household Resilience and Portfolio Vulnerability.
Liu, J., Song, Q., Qi, Y., Rahman, S., & Sriboonchitta, S. (2020). Measurement of systemic risk in global financial markets and its application in forecasting trading decisions. Sustainability, 12(10), 4000. https://doi.org/10.3390/su12104000
Martins, N. M. (2020). Risco sistêmico, fragilidade financeira e crise: uma análise pós-keynesiana a partir da contribuição de Fernando Cardim de Carvalho. Revista de Economia Contemporanea, 24, e202428. https://doi.org/10.1590/198055272428
Pourmansouri, R., Fallahshams, M., & Afshani, R. G. G. (2024). Designing a financial stress index based on the GHARCH-DCC approach and machine learning models. Journal of the Knowledge Economy, 1-30. https://doi.org/10.1007/s13132-024-02075-9
Shokrollahi, Y., Nikahd, M. M., Gholami, K., & Azamirad, G. (2023). Deep learning techniques for predicting stress fields in composite materials: A superior alternative to finite element analysis. Journal of Composites Science, 7(8), 311. https://doi.org/10.3390/jcs7080311
So, M. K., Chan, T. W., & Chu, A. M. (2024). Bayesian Systemic Risk Analysis using Latent Space Network Models. Data Science in Science, 3(1), 2381724. https://doi.org/10.1080/26941899.2024.2381724
Tedeschi, G., Caccioli, F., & Recchioni, M. C. (2020). Taming financial systemic risk: models, instruments and early warning indicators. Journal of Economic Interaction and Coordination, 15, 1-7. https://doi.org/10.1007/s11403-019-00278-x
Yarovaya, L., Brzeszczyński, J., Goodell, J. W., Lucey, B., & Lau, C. K. M. (2022). Rethinking financial contagion: Information transmission mechanism during the COVID-19 pandemic. Journal of International Financial Markets, Institutions and Money, 79, 101589. https://doi.org/10.1016/j.intfin.2022.101589
Zhang, X., & Hu, Q. (2024). Counterparty risk and contagion in financial networks. Applied Economics Letters, 1-14. https://doi.org/10.1080/00036846.2024.2405202
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Copyright (c) 2025 Elham Babaheidarian, Farhad Hanifi, Mirfiz Fallah Shams (Author)
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