Forecasting Weekly Returns of the Tehran Stock Exchange Total Index Using the MIDAS Model

Raha  Komaee; Farhad Ghaffari; Farzin Arbabi

Authors

Raha Komaee Ph.D. Student, Department of Economics, SR.C., Islamic Azad University, Tehran, Iran Author https://orcid.org/0009-0003-7484-9509
Farhad Ghaffari Professor, Department of Economics, SR.C., Islamic Azad University, Tehran, Iran https://orcid.org/0000-0002-9945-5720
Farzin Arbabi Assistant Professor, Department of Economics, CT.C., Islamic Azad University, Tehran, Iran Author https://orcid.org/0000-0002-1444-8362

Keywords:

MIDAS model, Tehran Stock Exchange Total Index return, exchange rate, global gold price, global oil price, GARCH, capital market return forecasting

Abstract

Forecasting capital market returns plays a pivotal role in the decision-making processes of investors, portfolio managers, and economic policymakers. Because economic variables are observed at different temporal frequencies, conventional temporal aggregation of data may result in the loss of valuable within-period information. The primary objective of this study is to evaluate the out-of-sample forecasting performance for weekly returns of the Tehran Stock Exchange (TSE) Total Index using a Mixed Data Sampling (MIDAS) regression model with a Beta polynomial weighting function. For this purpose, daily data on changes in the exchange rate, changes in the global gold price, changes in oil prices, daily index returns, and daily conditional volatilities extracted from a GARCH(1,1) model over the 2016–2025 period were employed. Model performance was evaluated using an out-of-sample forecasting-window approach by comparing forecast error measures, including the root mean squared error (RMSE) and mean absolute error (MAE), together with the Diebold–Mariano test of predictive accuracy. The empirical results indicate that changes in the exchange rate, the conditional volatilities of the exchange rate and gold price, and daily index returns are positively associated with weekly stock market returns, whereas the empirical evidence indicates a negative relationship between changes in the gold price and stock returns. Furthermore, incorporating high-frequency daily information and short-term volatility into the MIDAS specification resulted in a statistically significant reduction in out-of-sample forecast errors, as confirmed by the Diebold–Mariano test. The findings demonstrate the effectiveness of mixed-frequency models in extracting latent information from daily data to improve forecasting accuracy and risk management in the capital market.

References

[1] F. Alshubiri, "The Stock Market Capitalisation and Financial Growth Nexus: An Empirical Study of Western European Countries," Future Business Journal, vol. 7, no. 1, p. 46, 2021, doi: 10.1186/s43093-021-00092-7.

[2] C. Chikwira and J. I. Mohammed, "The Impact of the Stock Market on Liquidity and Economic Growth: Evidence of Volatile Market," Economies, vol. 11, no. 6, p. 155, 2023, doi: 10.3390/economies11060155.

[3] M. M. Rutendo and M. Godfrey, "Stock Returns and Income Inequality," Journal of Risk and Financial Management, vol. 19, no. 1, p. 83, 2026, doi: 10.3390/jrfm19010083.

[4] H. Setiawan, A. Setyanto, E. Utami, and Kusrini, "Advancements and Challenges in Financial Forecasting Models: A Systematic Literature Review," Sage, 2025, pp. 690-697.

[5] S. H. Lee, "Directional Forecasting of Weekly Stock Returns: Revisiting Market Efficiency with Deep Neural Networks," SSRN Electronic Journal, 2025, doi: 10.2139/ssrn.5270055.

[6] H. Oukhouya et al., "Forecasting Stock Markets in the MENA Region: ARIMAX and Ensemble Machine Learning Models with SHAP Interpretability," Quality & Quantity, vol. 60, no. 2, pp. 3387-3418, 2026, doi: 10.1007/s11135-025-02395-1.

[7] A. Karimi Dastgerdi and F. Zamani Boroujeni, "A Review of Deep Learning Methods for Financial Market Prediction," Transactions on Data Analysis in Social Science, vol. 2, no. 3, pp. 164-172, 2020, doi: 10.47176/TDASS.2020.164.

[8] A. Aminimehr, H. Bajelan, and Hanieh, "A Study of Tehran Stock Exchange Index Return Data Behavior and a Deep Neural Network-Based Method for Predicting Regime Changes," Financial Management Perspective, vol. 11, no. 34, pp. 145-171, 2021.

[9] S. Shaghaghi Shahri and O. Rahmani Seryasat, "Evaluation and Comparison of Classification Model Performance in Predicting Corporate Credit Ratings Using Artificial Intelligence: A Case Study of the Tehran Stock Exchange," Transactions on Data Analysis in Social Science, vol. 6, no. 2, pp. 109-118, 2024, doi: 10.47176/TDASS.2024.109.

[10] M. Osoulian, A. Nikmaram, and M. Karimi, "Forecasting the Trend of the Overall Index Using Hybrid Neural Networks with a Focus on Multiscale Temporal Feature Extraction in the Tehran Stock Exchange," Financial Research, vol. 27, no. 1, pp. 85-113, 2025.

[11] A. Taslimpour, G. R. Askarzadeh, K. Ghalmagh, and H. Nasiri, "Forecasting the Tehran Stock Exchange Overall Index Using the NARX Neural Network Model," Asset Management and Financing, vol. 14, no. 1, pp. 21-48, 2026.

[12] A. Mitra, S. Das, P. Biswas, and A. K. Jalan, "A Study of Forecasting on Indian Sensex (BSE) Using Prophet Model," Springer, 2026.

[13] N. Hauzenberger, M. Marcellino, M. Pfarrhofer, and A. Stelzer, "Nowcasting with Mixed Frequency Data Using Gaussian Processes," arXiv preprint arXiv:2402.10574, 2024, doi: 10.2139/ssrn.4958306.

[14] Y. Lyu, F. Qin, R. Ke, Y. Wei, and M. Kong, "Does Mixed Frequency Variables Help to Forecast Value at Risk in the Crude Oil Market?," Resources Policy, vol. 88, no. 104426, 2024, doi: 10.1016/j.resourpol.2023.104426.

[15] A. Baranji Tazeh Shahri and S. A. Hosseini Doust, "Comparison of the Accuracy of the ARIMA Model and the Dynamic Nonlinear MIDAS Model in Forecasting the Tehran Stock Exchange Market," 2025.

[16] V. Omidi and A. Jamshidi, "Examining the Effects of Exchange Rate, Oil Price, and Gold Price on the Tehran Stock Exchange in Bear and Bull Markets Using a Behavioral Finance Approach: New Evidence from a THSVAR Model," Interdisciplinary Studies in Economics, vol. 1, no. 2, pp. 83-112, 2025.

[17] O. A. Yahaya, "The Effects of Macroeconomic Variables on the Stock Market Performance in Nigeria," Journal of Accounting, Finance and Management, vol. 21, no. 2, pp. 241-282, 2026.

[18] N. Nautiyal and V. Kandpal, "Nonlinearity between Economic Indicators and Indian Capital Market," FIIB Business Review, vol. 14, no. 1, pp. 43-57, 2025, doi: 10.1177/23197145211035483.

[19] C. L. Chang and Q. Cai, "Frequency and Trend of Abnormal Returns in Chinese Energy Stock Market," Emerging Markets Finance and Trade, pp. 1-16, 2026, doi: 10.1080/1540496X.2025.2612616.

[20] E. Usoro Anthony and E. Akaniynene, "Modeling Nigeria Crude Oil Production and Price Volatility," Studies, vol. 5, pp. 33-54, 2022, doi: 10.52589/AJMSS-L4FI9DW6.

[21] J. Hull, Options, Futures, and Other Derivatives, 11 ed. Pearson, 2022.

[22] J. M. Sampaio and P. A. Morettin, "Stable Randomized Generalized Autoregressive Conditional Heteroskedastic Models," Econometrics and Statistics, vol. 15, no. 67, p. 83, 2020, doi: 10.1016/j.ecosta.2018.11.002.

[23] P. Embrechts, "Quantitative Risk Management," Springer, 2025, pp. 2050-2054.

[24] M. Hassannejad, M. Dolo, and F. Shabani, "Examining the Effect of Changes in the Permissible Price Fluctuation Range on Daily Stock Market Volatility in Iran Using Realized Variance and Fourier Expansion," Asset Management and Financing, vol. 11, no. 2, pp. 19-34, 2023.

[25] Z. Saba, R. Bhuyan, and C. Cetin, "Predicting Short-Term Stock Returns with Weekly Options Indicators," Quarterly Journal of Finance, 2025, doi: 10.1142/S2010139225500041.

[26] N. Kouhestani, A. Dadashi, H. R. Gholamnia Roshan, and K. Azinfar, "Day Characteristics and Stock Market Performance," Financial Management Perspective, vol. 13, no. 44, pp. 33-58, 2023.

[27] G. Ranjbaran and M. Moin, "Feature Extraction and Classification Methods for Stock Market Trend Prediction," Transactions on Data Analysis in Social Science, vol. 1, no. 3, pp. 163-170, 2019, doi: 10.47176/TDASS.2019.163.

[28] A. Nasiri Gheydari and M. Afzali, "Introduction of an Optimal Portfolio Recommendation System Using Quantum Potential," Transactions on Machine Intelligence, vol. 3, no. 2, pp. 111-120, 2020, doi: 10.47176/TMI.2020.111.

[29] M. Shokrkhah, V. Amiri, and V. Veisizadeh, "An Expected Drawdown Model Based on Wavelet Transform across Different Investment Time Horizons in the Tehran Stock Exchange," Stock Exchange Journal, vol. 15, no. 57, pp. 225-254, 2022.

[30] H. Zare, "Political Uncertainty and Stock Market Volatility in Iran: Considering International Developments," Development and Capital, vol. 6, no. 1, pp. 123-145, 2021.

[31] S. Z. Tabatabaei, S. A. Hashemi, and H. Amiri, "Examining Market Reaction to Risk-Factor Disclosures," Asset Management and Financing, vol. 10, no. 3, pp. 47-66, 2022.

[32] Z. Karampour, M. Latifi Benmaran, and R. Darabi, "The Effect of Cryptocurrency Returns on the Behavior of the Tehran Stock Exchange Overall Index," Investment Knowledge, vol. 15, no. 60, pp. 547-568, 2026.

[33] C. P. T. Mendoza and N. G. Tubice, "Analyzing Historical Trends and Predicting Market Sentiment in Digital Currency Using Time Series Decomposition and ARIMA Models," Journal of Digital Market and Digital Currency, vol. 2, no. 3, pp. 270-297, 2025, doi: 10.47738/jdmdc.v2i3.37.