Economic Research and Perspectives

Economic Research and Perspectives

Investigating Factors Affecting the Stock Price Index in Iran: A Machine Learning Approach

Document Type : Original Article

Authors
Mohammad Mahdi Barghi Osgouei 1
Reza Rajpour 2
Saman Hatamerd 3
Maryam Amini 4
Sadeq Rezaie Bargoshadi 5
1 Professor, Department of Economic Sciences, Faculty of Economics and Management, University of Tabriz, Iran
2 Professor, Faculty of Economics and Management, University of Tabriz, Iran
3 Assistant Professor, Department of Economic Sciences, Faculty of Humanities, University of Zanjan, Zanjan, Iran
4 PhD in Regional & Urban Economics, University of Isfahan, Isfahan, Iran
5 Assistant Professor, Faculty of Economics and Management, University of Tehran, Iran
10.48311/ecor.2025.13632
Abstract
Aim and Introduction 
Achieving sustained and long-term economic growth necessitates the optimal allocation and utilization of resources at the national level. This goal relies heavily on the existence of efficient financial markets, particularly well-functioning and extensive capital markets. Numerous macroeconomic variables can influence the level of risk associated with shareholder rights, corporate cash flows, and adjusted discount rates. Additionally, changes in economic conditions can alter both the quantity and nature of investment opportunities. 
However, establishing a fixed and consistent relationship between macroeconomic variables and stock price indices remains challenging. The complex and dynamic nature of financial markets makes it difficult to identify a method that accurately reflects economic conditions and captures the most critical influencing variables. Therefore, this study employs machine learning models to identify the key macroeconomic factors affecting affecting Iran’s stock price index, aiming to provide a comprehensive and systematic understanding of the relationship between these factors and stock market performance.
Methodology 
Feature selection is one of the most common and crucial techniques in data preprocessing and serves as an essential component of machine learning. This study employs feature selection models to identify the most relevant predictors of the stock price index. The models utilized include the random forest method and regularized linear regression. To examine the nature of the relationships between variables, the jointness method was applied. Additionally, the mutual information analysis was conducted to assess the influence of key variables over different decades, enabling a deeper understanding of how the impact of macroeconomic factors on stock prices has evolved over time.   
Findings
The study analyzed the impact of selected macroeconomic variables on stock price indices, focusing on the Tehran Stock Exchange. The findings from the Random Forest (RF) and Regularized Linear Regression (RLR) models indicate that exchange rates, financial development, inflation, economic growth, trade openness, and global uncertainty significantly influence Iran’s stock price index. The results demonstrate that global uncertainty, interest rates, and trade openness exert negative effects on stock prices, whereas the other variables positively influence stock prices. 
The jointness method was employed to analyze the relationships between these variables, further confirming their significance. Moreover, the Mutual Information method was used to examine how the influence of these key variables varied across different decades. The findings revealed that the exchange rate, financial development, and unemployment were consistently influential across all three decades. In contrast, other variables, such as interest rates, demonstrated varying degrees of significance depending on the decade, with some variables becoming influential only in particular periods
Discussion and Conclusion
Among the variables examined, exchange rates, financial development, inflation, economic growth, trade openness, and global uncertainty emerged as the most significant factors influencing Iran’s stock price index. This finding is not surprising, given Iran’s historical experience with significant exchange rate fluctuations and persistent inflationary pressures. Global uncertainty has consistently influenced domestic markets in Iran due to political and economic instability. Previous research has highlighted the complex relationship between exchange rate fluctuations and stock price indices (Ratanapakorn & Sharma, 2007). Scholars have argued that the relationship between stock prices and exchange rates can significantly affect monetary and fiscal policy, as a recessionary stock market can reduce overall demand and impact broader economic performance. 
Extensive research has also investigated the relationship between inflation and stock prices, identifying inflation as a significant factor affecting stock indices (Boudoukh & Richardson, 1993; Fama & Schwert, 1977; Jaffe & Mandelker, 1976). While some studies have reported a positive correlation between inflation and stock prices, others have found a negative relationship. 
Moreover, trade openness has been recognized as a key factor influencing stock market fluctuations. Open economies are more vulnerable to external shocks due to increased global risk-sharing among markets. Although some studies have not found conclusive evidence of a direct effect between trade openness and stock prices, trade openness remains one of the influential factors (Nickmansh, 2016).
Stock prices reflect the present value of future cash flows, which are subject to two main effects: cash flow changes driven by increased production and interest rates, which serve as a discount factor. Stock prices tend to decline when expected cash flows decrease or interest rates rise. The level of actual economic activity directly influences cash flows, as higher economic activity generally leads to increased cash flow. Among the various indicators used to predict commodity markets, real Gross Domestic Product (GDP) is considered the most comprehensive measure of economic activity (Yuhasin, 2011; Christopher et al., 2006). 
Finally, global uncertainty plays a significant role in shaping the internal economic environment of countries, making it an important global macroeconomic variable that influences the performance of publicly traded companies on the stock exchange.
Keywords
Machine learning
Stock Market
stock price

Subjects

Abbasinejad, H., Mohammadi, S., & Ebrahimi, S. (2017). Dynamics of the relation between macroeconomic variables and stock market index. Journal of Asset Management and Financing, 5(1), 61-82.
     doi: 10.22108/amf.2017.21153. [In Persian]
Hatamerad, Saman, Adrangi ,Bahram, Asgharpur, Hossein, & Haghighat, Jafar. (2023). Investigating the Impact of Macroeconomic Factors on the Iranian Stock Price Index by Using Averaging Methods. Iranian Journal of Economic Research, 28(95), 193-236. magiran.com/p2631396. [In Persian] 
Hatamerad, S., Asgharpur, H., Adrangi, B. et al. (2024). Stock price index analysis of four OPEC members: A Bayesian approach. Financ Innov., 10, 134.
Samadi, Saeed, & Bayani, Ozra. (2011). The relationship between macroeconomic variables and stock returns in the Tehran stock exchange. Journal of Financial Economics (Financial Economics and Development), 5(16). [In Persian] 
Abakah, E. J. A., Tiwari, A. K., Alagidede, I. P., & Hammoudeh, S. (2023). Nonlinearity in the causality and systemic risk spillover between the OPEC oil and GCC equity markets: A pre-and post-financial crisis analysis. Empirical Economics, 1-77. https://ideas.repec.org/a/spr/empeco/v65y2023i3d10.1007
Al-Sharkas, A. (2004). The dynamic relationship between macroeconomic factors and the Jordanian stock market. International Journal of Applied Econometrics and Quantitative Studies, 1, 1.
Boyd J.H., Jagannathan R., & Hu, J. (2001). The stock market’s reaction to unemployment news: Why bad news is usually good for stocks? NBER Working Paper 9182.
Breiman, L. (2001). Random forests. Machine learning, 45, 5-32.
Bulmash, S. B., & Trivoli, G. W. (1991). Time-lagged interactions between stocks prices and selected economic variables. The Journal of Portfolio Management, 17(4), 61-67.
Chen, N. F. (1991). Financial investment opportunities and the macroeconomy. The Journal of Finance, 46(2), 529-554.
Chen, N. F., Roll, R., & Ross, S. A. (1986). Economic forces and the stock market. Journal of Business, 383-403. https://www.jstor.org/stable/2352710
Chinzara, Z. (2011). Macroeconomic uncertainty and conditional stock market volatility in South Africa”. South African Journal of Economics, 79(1), 27-49.
Cutler, D. R., Edwards Jr, T. C., Beard, K. H., Cutler, A., Hess, K. T., Gibson, J., & Lawler, J. J. (2007). Random forests for classification in ecology. Ecology, 88(11), 2783-2792.
Dixit, A., Mani, A. & Bansal, R. (2020). Depsosvm: Variant of differential evolution based on PSO for image and text data classification. International Journal of Intelligent Computing and Cybernetics, Vol. 13, No. 2, 223- 238. https://doi.org/10.1108/IJICC-01-2020-0004
Doppelhofer, G., & Weeks, M. (2009). Jointness of growth determinants. Journal of Applied Econometrics, 24(2), 209-244.
Erol, A. F., & Aytekin, S. (2019). The role of macroeconomic indicators on stock index evidence from Turkey. Global Journal of Economics and Business Studies, 8(15), 61-68.
Fama, E. F. (1981). Stock returns, real activity, inflation, and money. The American Economic Review, 71(4), 545-565.
Flannery, M. J., & James, C. M. (1984). The effect of interest rate changes on the common stock returns of financial institutions. The journal of Finance, 39(4), 1141-1153.
Gan, C., Lee, M., Yong, H. H., and Zhang, J. (2006). Macroeconomic variables and stock market interactions: New Zealand evidence. Investment Management and Financial Innovations, 3(4), 89-101
Geske, R., & Roll, R. (1983). The fiscal and monetary linkage between stock returns and inflation. The Journal of Finance, 38(1), 1-33.
Geske, R., & Roll, R. (1983). The fiscal and monetary linkage between stock returns and inflation. The Journal of Finance, 38(1), 1-33.
Gultekin, N. B. (1983). Stock market returns and inflation. Evidence from other countries. The Journal of Finance, 38, 49-65.
Guo, B., & Nixon, M. S. (2008). Gait feature subset selection by mutual information. IEEE Transactions on Systems, MAN, and Cybernetics-part a: Systems and Humans, 39(1), 36-46.
Hall, M. (2000). Correlation-based Feature Selection of Discrete and Numeric Class Machine Learning. in Proceedings of the Seventeenth International Conference on Machine Learning (ICML 2000), Stanford University, Stanford, CA, USA, June 29 - July 2, 2000.
Humpe, A., & Macmillan, P. (2009). Can macroeconomic variables explain longterm stock market movements? A comparison of the US and Japan. Applied Financial Economics, 19(2), 111-119.
James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An Introduction to Statistical Learning with Applications in R. Springer.
Kalousis, A., Prados, J., & Hilario, M. (2007). Stability of feature selection algorithms: a study on high-dimensional spaces. Knowledge and information systems, 12, 95-116. 
Keho, Y. (2010). Budget deficit and economic growth: Causality evidence and policy implications for WAEMU countries. European Journal of Economics, Finance and Administrative Sciences, 18, 99-104.
Kelikume, I., & Muritala, O. (2019). The impact of changes in oil price on stock market: Evidence from Africa. International Journal of Management, Economics and Social Sciences (IJMESS), 8(3), 169-194. 
Kilian, L. &  C. Park. (2009). The impact of oil price shocks on the US stock market. International Economic Review, 50(4), 1267-1287.
koller, D., and Sahami, M. (1996). Toward Optimal Feature Selection. in International Conference on Machine Learning. Stanford, CA: Stanford InfoLab.
Kumar D., Kumar S., and Bansal R.(2020). Multi-objective multi-join query optimisation using modified grey wolf optimization. 67-79 https://doi.org/10.1504/IJAIP.2020.108760.
Maurya, P.K., Bansal, R. and Mishra, A.K. (2023).Russia-Ukraine conflict and its impact on global inflation: An event study-based approach. Journal of Economic Studies, Vol. ahead-of-print No. ahead-of print.
     https://doi.org/10.1108/JES-01-2023-0003
Merton, R. C. (1973). An intertemporal capital asset pricing model. Econometrica: Journal of the Econometric Society, 41, 867-887.
Molina, L. C., Belanche, L., & Nebot, À. (2002, December). Feature selection algorithms: A survey and experimental evaluation. In 2002 IEEE International Conference on Data Mining, 2002. Proceedings. (pp. 306-313). IEEE.
Mukherjee, T. K., & Naka, A. (1995). Dynamic relations between macroeconomic variables and the Japanese stock market: An application of a vector error correction model. Journal of Financial Research, 18(2), 223-237.
Nandha, M., & Faff, R. (2008). Does oil move equity prices? A global view. Energy economics, 30(3), 986-997.
Nelson, C. R. (1976). Inflation and rates of return on common stocks. Journal of Finance, 31(2): 471-483
Nikmanesh, L. (2016). Trade openness and stock market volatility in the ASEAN-5 countries: New evidence using SUR. Investment Analysts Journal, 45(3), 194-211.
Park, J. W., & Ratti, R. A. (2008). Oil price shocks and stock markets in the U.S. and 13 European countries. Energy Economics, 30(5): 2587-2608.
Pearce, D. K., & Roley, V. V. (1983). The reaction of stock prices to unanticipated changes in money: A note. The Journal of Finance, 38(4), 1323-1333. 
Pearce, D. K., & Roley, V. V. (1985). Stock prices and economics news. Journal of Business, 58, 49-67.
Peiro, A. (2016). Stock prices and macroeconomic factors: Some European evidence. International Review of Economics & Finance, 41, 287-294.
Rahman, M. (2008). Influences of money supply and oil price on US stock market. North American Journal of Finance and Banking Research, 2(2).
Ratanapakorn, O., & Sharma, S. C. (2007). Dynamic analysis between the US stock returns and the macroeconomic variables. Applied Financial Economics, 17(5), 369-377.
Sadorsky P. (1999). Oil price shocks and stock market activity. Energy Economics, 2, 449-469.
Solnik, B. (1983). International arbitrage pricing theory. The Journal of Finance, 38(2), 449-457.
Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society Series. No. 1, 267-288.
Verma R. K. , A. Kumar A. and Bansal R. (2021). Impact of COVID-19 on Different Sectors of the Economy Using Event Study Method: An Indian Perspective. pp. 109-120 | Published online:
     https://doi.org/10.1080/10599231.2021.1905492.
 Verma, R.K. & Bansal, R. (2021). Impact of macroeconomic variables on the performance of stock exchange: A systematic review. International Journal of Emerging Markets, Vol. 16 No. 7, 1291-1329.
     https://doi.org/10.1108/IJOEM11-2019-0993
Zahera, S.A. & Bansal, R. (2018). Do investors exhibit behavioral biases in investment decision making? A systematic review. Qualitative Research in Financial Markets, Vol. 10 No. 2, 210-251.
     https://doi.org/10.1108/QRFM-04- 2017-0028
Zhao, H. (2010). Dynamic relationship between exchange rate and stock price: Evidence from China. Research in International Business and Finance, 24(2), 103-112.