Document Type : Research Paper


Assistant Professor of Economics, Department of Economics and Social Sciences, Nahavand Higher Education Complex, Bu-Ali Sina University, Hamedan, Iran


 The information diffusion and interactions within financial markets have a significant impact on the price discovery process and the sentiment and risk dispersion. Despite its importance, limited research has been conducted on information flow dynamics within the Tehran Stock Exchange, which is a vital component of Iran's capital market. This study aims to fill this gap by examining the information flow dynamics among 39 major industries from March 27, 2010, to June 21, 2023. Effective transfer entropy is employed to quantify the intensity of information flow between industry indices. Sequence of information matrices are constructed using rolling one-week windows over one-year periods. Given the occurrence of critical events during the research period, their influence on information flow dynamics is analyzed using Frobenius distance-based k-nearest neighbor networks, Influence Strength analysis, and threshold networks. The findings reveal that the effective transfer entropy matrix exhibits time-varying characteristics and remains stable throughout most periods. Furthermore, critical events significantly impact information flow dynamics, with abnormal values of Influence Strength associated with market volatility and major events. Additionally, the dominant source of information in the information flow network changes over time, highlighting the transient nature of industry dominance within the network.


The diffusion of information and interactions within financial markets greatly influences the price discovery process and affects sentiment and risk dispersion. The potential for growth in the Tehran Stock Exchange (TSE) through the introduction of innovative financial instruments can offer investors additional investment opportunities. Therefore, understanding the dynamics of information transmission within the market aids investors in decision-making.
Existing literature suggests that stock price volatilities are interconnected, and stocks within the same industry often exhibit high correlations. Additionally, industry stock price indexes within the market can serve as leading indicators of economic activity. Analyzing the information flow network at the industry index level holds significant implications for investors, portfolio managers, and policymakers seeking to devise appropriate risk-mitigating strategies, especially industry sector rotation strategies.
Despite the Tehran Stock Exchange being a vital component of Iran's capital market, there has been limited research on the information flow network between industries and its time-varying characteristics. Furthermore, despite significant events occurring during the specified sample period, there is a lack of empirical evidence regarding their impacts on information flow within the Tehran securities market.

Methods and Material

In this research, the dynamics of information flow between the 39 major industries are investigated from March 27, 2010, to June 21, 2023. Following Ni (2023), the Effective transfer entropy that measures the intensity of information flow between industries indices is calculated. Then the sequence of information matrices is created by rolling a one-week calculation window. In this paper, the calculation window of 237-trading day widths and the rolling window of 5-day widths are used to calculate the information matrices of length 591. Moreover, using quantiles of return series,  and , the information matrix sequences are constructed.
Given that the research period encompasses critical events, their influence on information flow is examined using various methodologies, including the Frobenius distance-based k-nearest neighbor network, Influence Strength (IS) analysis, and a threshold-directed network of information matrices.
 Results and Discussion
Upon depicting the Frobenius distance matrix based on Q1, significant shifts in the distance between the information matrices are observed. These shifts often coincide with critical events that have impacted the market.
The IS series graph over the research period reveals several local peaks. For some peaks, no significant events occurred during the research period. Peak 2, however, corresponds to severe market fluctuations and turmoil, primarily stemming from the global impact of the 2008 financial crisis. Additionally, this time window aligns with the initial period of oil and petrochemical sanctions against Iran, leading to a decline in the total index of the TSE. Peak 4 reflects a decrease in the TSE's total index following Iran's nuclear agreement with the P5+1 in 2015 (post-JCPOA). During peak period 5, coinciding with the US withdrawal from the JCPOA and the re-imposition of all US sanctions, the TSE's total index experienced a drop. Peaks 1, 3, and 7 correspond to the bursting of stock price bubbles in 2009, 2013, and 2020, respectively.
The findings also highlight that the window corresponding to the maximum value of IS (0.1757) is from 31/12/2012 to 7/1/2014, coinciding with the bursting of the stock price bubble in January 2014. Peak 6 corresponds to the window from 19/7/2020 to 7/7/2021, which includes the early days of the COVID-19 pandemic. Lastly, from 1/6/2022 to 3/6/2023, the government's decision to abolish the preferential exchange rate for importing basic goods negatively affected the prices of some listed companies in the TSE and the indexes of related industries. Comparing the patterns of IS calculated based on Q1 vs. Q2 demonstrates the correspondence between the local peaks.
On the other hand, examining the Financial industry (node 37), the  series reached its peak during 2/2/2016-25/1/2017. During this period, the TSE faced a significant decline in the total index due to uncertainty caused by the JCPOA. Analysis of the directional network of the information matrix, filtered with a threshold of 0.01, reveals that in the post-JCPOA period, there is an information flow between the Financial industry and all other industries except the Furniture industry (node 20) and Peymankari industry (node 26).
Furthermore, aside from node 37, which serves as the central node during this period, node 34 (Banking industry, deg = 34), node 39 (EstekrajeNaft industry, deg=33), and node 35 (SayerMali industry, deg =32) also exhibit high degrees. Additionally, the network constructed from the information matrix corresponding to peak 6 indicates several central nodes. However, during the time window corresponding to peak 6, node 24 (Daroee industry) with the highest  (0.0105) exerts the strongest influence on the network.
The results also demonstrate that for certain industries, such as the Pharmaceutical industry, the value of  increased during the 19/1/2016-11/1/2017 period, corresponding to the post-JCPOA era. However, for other industries, the maximum value of  occurred mainly during other critical periods, such as the stock price bubble bursts in 2010 and 2014 and the imposition of new sanctions against Iran..


The findings indicate that the effective transfer entropy matrix exhibits time-varying characteristics and remains stable over the majority of periods. Additionally, critical events have notably impacted the dynamics of information flow, with abnormal values of Influence Strength correlating with market volatility and significant events. Moreover, the primary source of information in the sequence of the information flow network evolves over time, suggesting that the dominant industry in the network is not consistently sustainable.


اصولیان، محمد و کوشکی، علی. (1399). بررسی توانایی معیار آنتروپی باقی‌مانده تجمعی در پیش‌بینی بحران بوسیلۀ داده‌های شبیه‌ساز بحران نقشۀ لوجستیک و شاخص کل بورس اوراق بهادار تهران. چشم‌انداز مدیریت مالی، 10(31)، 9-27.
 doi: 10.52547/jfmp.10.31.9
جهانگیری، خلیل و حکمتی فرید، صمد. (1394). مطالعه آثار سرریز تلاطم بازارهای سهام، طلا، نفت و ارز. پژوهشنامه اقتصادی، 15(56)، 161-194.
کشاورز حداد، غلامرضا و وحیدی، حامد. (1401). نابرابری اطلاعاتی بین معامله‎گران حقیقی و حقوقی: شواهدی از بازار سهام تهران. پژوهشنامه اقتصادی، 22(86)، 1-36. doi: 10.22054/joer.2023.69382.1086
محمدی، احمد، سواری، زینب و احمدزاده، خالد. (1395). تجزیه‌وتحلیل کارکرد کشف قیمت قراردادهای آتی سکه طلا در ایران. پژوهشنامه اقتصادی، 16(63)، 25-60. doi: 10.22054/joer.2017.7583
مهاجری، پریسا و طالبلو، رضا. (b1401). بررسی پویایی‌های سرریز تلاطمات بین بازده بخش‌ها با رویکرد اتصالات خودرگرسیون برداری با پارامترهای متغیر در طول زمان (TVP-VAR)، شواهدی از بازار سهام ایران. تحقیقات اقتصادی، 57(2)، 321-356. doi: 10.22059/jte.2023.349895.1008727
طالبلو، رضا و مهاجری، پریسا. (a1401). اتصالات و سرریز ریسک در بازار سهام ایران، یک تحلیل بخشی با به‌کارگیری مدل خودرگرسیون برداری با پارامترهای متغیر طی زمان (TVP-VAR). مدلسازی اقتصادسنجی، 7(3)، 95-125.
 doi: 10.22075/jem.2022.28780.1771
طالبلو، رضا و مهاجری، پریسا. (1400). الگوسازی تلاطم در بازارهای دارایی ایران با استفاده از مدل تلاطم تصادفی چند متغیره عاملی. مدلسازی اقتصادسنجی، 6(3)، 63-96. doi: 10.22075/jem.2021.23659.1607
نمکی، علی، خورسندی، اشکان و سلیمانی دامنه، مجید. (اسفند 1400). بررسی انتقال اطلاعات میان صنایع مختلف بورس اوراق بهادار تهران با استفاده از انتقال آنتروپی، مجموعه مقالات دومین کنفرانس فیزیک اقتصاد و اقتصاد پیچیدگی. تهران، ایران.
Assaf, A., Mokni, K., & Youssef, M. (2023). COVID-19 and information flow between cryptocurrencies, and conventional financial assets. The Quarterly Review of Economics and Finance, 89, 73-81.
Behrendt, S., & Schmidt, A. (2021). Nonlinearity matters: The stock price–trading volume relation revisited. Economic Modelling, 98, 371-385.
Dimpfl, T., & Peter, F. J. (2014). The impact of the financial crisis on transatlantic information flows: An intraday analysis. Journal of International Financial Markets, Institutions and Money, 31, 1-13.
Dimpfl, T., & Peter, F. J. (2019). Group transfer entropy with an application to cryptocurrencies. Physica A: Statistical Mechanics and its Applications, 516, 543-551.
Elsayed, A. H., Naifar, N., Uddin, G. S., & Wang, G. J. (2023). Multilayer information spillover networks between oil shocks and banking sectors: Evidence from oil-rich countries. International Review of Financial Analysis, 87, 102602.
He, J., & Shang, P. (2017). Comparison of transfer entropy methods for financial time series. Physica A: Statistical Mechanics and its Applications, 482, 772-785.
Hung, N. T., Nguyen, L. T. M., & Vo, X. V. (2022). Exchange rate volatility connectedness during Covid-19 outbreak: DECO-GARCH and Transfer Entropy approaches. Journal of International Financial Markets, Institutions and Money, 81, 101628.
Jahangiri, K., & Hekmati Farid, S. (2015). Investigating the Effects of Volatility Spillover between Stock, Gold, Oil and Exchange Markets. Economics Research, 15(56), 161-194. [In Persian]
Jizba, P., Kleinert, H., & Shefaat, M. (2012). Rényi’s information transfer between financial time series. Physica A: Statistical Mechanics and its Applications, 391(10), 2971-2989.
Jurczyk, J., Rehberg, T., Eckrot, A., & Morgenstern, I. (2017). Measuring critical transitions in financial markets. Scientific Reports, 7(1), 11564.
Keshavarz Haddad, G., & Vahidi, H. (2022). Informational Asymmetry between Institutional and Individual Traders: Evidence from Tehran Stock Exchange. Economics Research, 22(86), 1-36. doi: 10.22054/joer.2023.69382.1086. [In Persian]
Long, H., Zhang, J., & Tang, N. (2017). Does network topology influence systemic risk contribution? A perspective from the industry indices in Chinese stock market. PloS one, 12(7), e0180382.
Marschinski, R., & Kantz, H. (2002). Analysing the information flow between financial time series: An improved estimator for transfer entropy. The European Physical Journal B-Condensed Matter and Complex Systems, 30, 275-281.
 Mohajeri, P., & Taleblou, R. (2022). Investigating the Dynamics of Volatility Spillovers across Sectors’ Returns Utilizing a Time-Varying Parameter Vector Autoregressive Connectedness Approach; Evidence from Iranian Stock Market. Journal of Economic Research (Tahghighat- E- Eghtesadi), 57(2), 321-356. [In Persian]
Mohammadi, A., savari, Z., & Ahmadzadeh, K. (2016). Analyzing Price Discovery Function of Gold Coin Futures Contracts in Iran. Economics Research, 16(63), 25-60. [In Persian]
Münnix, M. C., Schäfer, R., & Grothe, O. (2014). Estimating correlation and covariance matrices by weighting of market similarity. Quantitative Finance, 14(5), 931-939.
Nie, C. X. (2020a). Correlation dynamics in the cryptocurrency market based on dimensionality reduction analysis. Physica A: Statistical Mechanics and its Applications, 554, 124702.
Nie, C. X. (2020b). A network-based method for detecting critical events of correlation dynamics in financial markets. Europhysics Letters, 131(5), 50001.
Nie, C. X. (2021). Dynamics of the price–volume information flow based on surrogate time series. Chaos: An Interdisciplinary Journal of Nonlinear Science, 31(1), 013106.
Nie, C. X. (2023). Time-varying characteristics of information flow networks in the Chinese market: An analysis based on sector indices. Finance Research Letters, 54, 103771.
Nie, C. X., & Song, F. T. (2023). Stable versus fragile community structures in the correlation dynamics of Chinese industry indices. Chaos, Solitons & Fractals, 167, 113044.
Nie, C. X., & Xiao, J. (2022). Dynamics of information flow between the Chinese A-Share market and the US stock market: from the 2008 crisis to the COVID-19 pandemic period. Entropy, 24(8), 1102.
Oh, G., Oh, T., Kim, H., & Kwon, O. (2014). An information flow among industry sectors in the Korean stock market. Journal of the Korean Physical Society, 65, 2140-2146.
Osoolian, M., & Koushki, A. (2020). Investigating the crisis forecasting ability of the cumulative residual entropy measure by using logistic map simulation data and Tehran stock exchange overall index. Journal of Financial Management Perspective, 10(31), 9-27. [In Persian]
Papana, A., Kyrtsou, C., Kugiumtzis, D., & Diks, C. (2016). Detecting causality in non-stationary time series using partial symbolic transfer entropy: Evidence in financial data. Computational Economics, 47, 341-365.
Peng, S., Han, W., & Jia, G. (2022). Pearson correlation and transfer entropy in the Chinese stock market with time delay. Data Science and Management, 5(3), 117-123.
Schreiber, T. (2000). Measuring information transfer. Physical Review Letters, 85(2), 461.
Škrinjarić, T., Quintino, D., & Ferreira, P. (2021). Transfer entropy approach for portfolio optimization: An empirical approach for CESEE markets. Journal of Risk and Financial Management, 14(8), 369.
Taleblou, R., & Mohajeri, P. (2023). Modeling the Daily Volatility of Oil, Gold, Dollar, Bitcoin and Iranian Stock Markets: An Empirical Application of a Nonlinear Space State Model. Iranian Economic Review, 27(3), 1033-1063. doi: 10.22059/ier.2023.328120.1007235[In persian]
Tse, C.K., Liu, J., & Lau, F.C.M. (2010). A network perspective of the stock market. Journal of Empirical Finance, 17(4), 659-667.
Yang, R., Li, X., & Zhang, T. (2014). Analysis of linkage effects among industry sectors in China’s stock market before and after the financial crisis. Physica A: Statistical Mechanics and its Applications, 411, 12-20.
Yang, Y., & Yang, H. (2008). Complex network-based time series analysis. Physica A: Statistical Mechanics and its Applications, 387(5-6), 1381-1386.
Yue, P., Cai, Q., Yan, W., & Zhou, W. X. (2020a). Information flow networks of Chinese stock market sectors. IEEE Access, 8, 13066-13077.
Yue, P., Fan, Y., Batten, J. A., & Zhou, W. X. (2020b). Information transfer between stock market sectors: A comparison between the USA and China. Entropy, 22(2), 194.