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 stock price indices.
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.
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).