Document Type : Research Paper
Authors
1 Faculty Member, School of Management and Economics, Science and Research Branch, Islamic Azad University, Tehran, Iran
2 Associate Professor, School of Management and Economics, Science and Research Branch, Islamic Azad University, Tehran, Iran
Abstract
Conventional economics posits that the presence of arbitrage in financial markets forces market participants to act rationally in order to maximize profits. This assumption underpins the efficient market hypothesis (EMH). However, in recent years, behavioral economics has challenged the assumption of market efficiency and rational behavior by demonstrating the significant impact of seemingly irrelevant factors (e.g., weather conditions, air temperature, and pollution) on financial markets. The present research aimed to compare the explanatory power of these two perspectives by analyzing daily data from the Tehran Stock Exchange index over two periods: February 20th, 2022 to February 19th, 2023, and February 20th, 2023 to February 19th, 2024. The study relied on the daily data on the growth rate of the dollar as an explanatory variable for the total stock market index growth from a conventional economics perspective. From a behavioral economics viewpoint, the analysis incorporated variables such as air temperature, weather conditions, and the pollution index. Given the nature of financial markets, the study used the EGARCH method for analysis. The results indicated that during the period from February 20th, 2022 to February 19th, 2023, when the dollar rate exhibited a significant upward trend, the explanatory power of behavioral variables decreased, with some even losing their significance in explaining the total stock market index. However, during the period from February 20th, 2023 to February 19th, 2024-when the exchange rate remained relatively stable-behavioral variables had a significant impact on the total stock market index.
Introduction
In the conventional economics perspective, which has long dominated the analysis of financial markets, actors are assumed to behave rationally. This means that they adjust their beliefs accurately (according to Bayes’ rule), aligning their subjective probabilities with reality and making decisions based on expected utility. However, in recent decades, the deviation of conventional economics theories from empirical data—along with the emergence of large, persistent, and severe price bubbles in financial markets—has led a group of economists to question the explanatory power of conventional theories and the assumption of rational behavior in financial markets. The present study aimed to address the duality between conventional and behavioral economics within the context of Iran’s developing economy. Focusing the country’s unique economic conditions, the study sought to determine which perspective—conventional or behavioral economics—provides a better explanation for stock market behavior. Two distinct time periods were analyzed: 1) from February 20th, 2022 to February 19th, 2023, during which the exchange rate (U.S. dollar) nearly doubled as a representative variable of the macroeconomic situation; and 2) from February 20th, 2023 to February 19th, 2024, when the exchange rate remained relatively stable, increasing by about 40%. The impact of behavioral variables on stock market returns was examined in two scenarios: one characterized by significant changes in macroeconomic variables and the other by more moderate changes. Conventional economics suggests that humans act rationally when the data is clear and the analysis is straightforward. However, as complexity increases and data becomes less clear, individuals tend to deviate from rational behavior due to limited rationality (Thaler, 2009). This hypothesis was tested by focusing on the two time periods of the study. In the first period, when macroeconomic variables exhibited a clear and specific trend, conventional economic theories were expected to provide a more accurate explanation of stock market behavior, with the influence of behavioral variables likely to decrease. Conversely, in the second period, when macroeconomic variables lacked a clear direction, it was anticipated that behavioral economics—along with variables rooted in psychological influences and the internal states of actors—would offer a better explanation for stock market performance. In this study, environmental variables such as air temperature, atmospheric conditions, and air pollution were considered representative of behavioral variables. The analysis investigated the impact of behavioral variables on the Tehran Stock Exchange index.
Materials and Methods
Financial sector data often exhibit heteroskedasticity, which makes the use of linear structures for estimation and modeling problematic. Additionally, fluctuations in financial data tend to cluster, indicating that the variance is self-explanatory. These characteristics make ARCH and GARCH models particularly suitable for modeling in this context. When using ARCH and GARCH models, it is essential for the estimated coefficients to be non-negative, which can present challenges in the estimation process. To address this issue, EGARCH models, which is the logarithmic form of the GARCH model, can be employed. This approach eliminates the need to impose the non-negativity condition on the variance coefficients. The current study estimated the daily growth rate of the total index of Tehran Stock Exchange over two separate time periods: from February 20th, 2022 to February 19th, 2023, and from February 20th, 2023 to February 19th, 2024. The analysis applied the AR(1) model and incorporated both behavioral and conventional variables into the variance component of the model to explain fluctuations in the total index efficiency.
Results and Discussion
During the first period (February 20th, 2022 to February 19th, 2023), the exchange rate experienced a clear and significant increase of 100%. Market players, adhering closely to conventional economic theories, operated under the assumption of rational and optimizing behavior. As a result, the exchange rate variable became more effective in explaining market fluctuations, while some behavioral variables, such as climate and air pollution, lost their explanatory power in the variance equation. In the second period (February 20th, 2023 to February 19th, 2024), the conventional variable (currency growth rate) became less significant and transparent. Market players increasingly relied on behavioral variables, which offered a better explanation for fluctuations in the total stock market index. The estimated coefficient for the conventional variable (foreign exchange growth rate) lost its significance during this period. The results showed that air temperature had a negative and significant impact on fluctuations in the growth index during both periods, consistent with the findings of previous studies.
Conclusion
This study analyzed two distinct economic periods: one marked by significant growth in foreign exchange rates, and the other characterized by relative stability in the foreign exchange market. The objective was to examine the behavior of financial actors and compare the explanations provided by conventional and behavioral perspectives on financial markets using the available data. According to the results from the two estimated models, the exchange rate growth (as the representative variable of the conventional view) had a significant and positive impact on stock index fluctuations during the first period, when exchange rates exhibited a clearly upward movement. However, this variable lost its significance in the second period, when exchange rates remained relatively stable. During the second time, the explanatory power shifted to behavioral variables such as weather conditions, pollution, and air temperature.
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