Investigating the Effect of Block Trading Mechanisms on Stock Liquidity in the Listed Companies on the Tehran Stock Exchange

Document Type : Original Article

Authors

University of Mazandaran

Abstract

Abstract

Introduction: Block trades, as a significant event in the capital market due to the large volume of transferred shares and their potential to influence price and liquidity, are consistently under the scrutiny of financial analysts. These transactions can bring implications for market efficiency and information transparency through the transfer of information and changes in corporate ownership structures. On the other hand, media coverage plays a moderating role in this process by reducing information asymmetry and increasing investor awareness. Accordingly, the present study investigates the effect of block trades on stock liquidity, emphasizing the mediating role of media coverage in the Tehran Stock Exchange (TSE).

Method: In terms of objective, the present research is categorized as applied, as the resulting findings can serve as a basis for decision-making by managers, policymakers, and capital market investors. In terms of data type and execution process, this research adopts a quantitative approach with an ex-post-facto nature. The data comprises information on all companies listed on the TSE from 2014 to 2022 (1393 to 1401 solar calendar) that have executed at least one block trade. After applying a systematic exclusion method, 100 companies with a total of 11,237 block trades were selected as the final sample. Data analysis was performed using descriptive and inferential statistics, employing the Generalized Method of Moments (GMM). The utilized tools include Rahavard Novin and Excel software for data extraction and variable calculation, and Stata for hypothesis testing and results analysis.

Results and Discussion: This study examined the effect of block trades on stock liquidity in the TSE, considering the moderating role of media coverage. The main findings indicate that block trades have a positive and significant impact on the long-term stock liquidity, contributing to increased market efficiency. This is facilitated by the exit of large volumes of shares without causing a price shock. Furthermore, media coverage plays a positive and significant moderating role in this relationship. This means that active media coverage strengthens the positive effect of block trades on liquidity by reducing information asymmetry. The final conclusion rests on the principle that information disclosure mechanisms, such as media coverage, play a vital role in optimizing the benefits of block trades for the Iranian capital market.

Conclusion Generally, regarding the level and trend of block trades in Iran, it can be stated that the Iranian stock market, unlike other markets, faces challenges due to issues such as inefficiency, high ownership concentration, and a lack of transparency in information disclosure. The untimely announcement of positive and negative news, coupled with investors’ mistrust in existing information, prevents the informational content of block trades from being properly reflected in stock prices. This situation leads to increased access to private information for informed investors, consequently intensifying information asymmetry between informed and uninformed investors. Moreover, the complementarity between the regular and block markets helps reduce stock liquidity shocks and, in turn, lowers the risk associated with these stocks. Companies with active block markets possess higher liquidity and experience a greater trading volume compared to the regular market. Improved liquidity following block trades aids in enhancing market efficiency. In the second part of the research, the conclusion suggests that media coverage has a positive effect on the relationship between block trades and stock liquidity. Companies with active media coverage that disclose the information and objectives of block trades in a timely manner are able to attract new buyers, thereby influencing stock liquidity.

Keywords

References

  1.  

    1. Agarwalla, K., & Pandey, A. (2010). Price impact of block trades and price behavior surrounding block trades in Indian capital market. Working Paper.
    2. Ahern, K., & Sosyura, D. (2014). Who writes the news? Corporate press releases during merger negotiations. Journal of Finance, 69(1), 241–291.
    3. Ahmed, S., Kumar, S., Gupta, O., & Bamel, N. (2024). CEO media coverage and cash holdings. International Review of Financial Analysis, 91, 103041.
    4. Amihud, Y. (2002). Illiquidity and stock returns: Cross-section and time series effects. Journal of Financial Markets, 5, 31–56.
    5. Arellano, M., & Bond, S. (1991). Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. Review of Economic Studies, 58(1), 277–297.
    6. Armanious, A., & Zhao, R. (2024). Stock liquidity effect on leverage: The role of debt security, financial constraint, and risk around the global financial crisis and Covid-19 pandemic. International Review of Financial Analysis, 92.
    7. Baldaufm, M., Frei, C., & Mollner, J. (2024). Block trade contracting. Journal of Financial Economics, 160, 103901.
    8. Baltagi, B. H. (2008). Econometric analysis of panel data. John Wiley & Sons Ltd.
    9. Benouda, S. O., & Mezzez, H. D. (2003). The effects of automation on liquidity, volatility, stock returns and efficiency: Evidence from the Tunisian stock market. Review of Middle East Economic and Finance, 1(2), 43–55.
    10. Bian, J., Wang, J., & Zhang, G. (2012). Chinese block transactions and the market reaction. China Economic Review, 23, 181–189.
    11. Chen, J., Hong, H., & Stein, J. (2001). Forecasting crashes: Trading volume, past returns, and conditional skewness in stock prices. Journal of Financial Economics, 61, 345–381.
    12. Dang, T., Dang, M., Hoang, L., & Nguyen, L. (2020). Media coverage and stock price synchronicity. International Review of Financial Analysis, 67.
    13. Ding, M., & Suardi, S. (2019). Tradable block ownership, liquidity and threat of exit. (DOI: 10.2139/ssrn.3311077). (Original work published 2014).
    14. Easley, D., & O’Hara, M. (1987). Prices, trade size and information in security markets. Journal of Financial Economics, 19, 69–90.
    15. Fakhari, H., Faghih, M., & Imani, K. (2021). CEO media exposure and stock price synchronicity: Simultaneous equations system. Journal of Accounting Advances, 13(2), 197–226. (In Persian).
    16. Fama, E. (1965). The behavior of stock-market prices. Journal of Business, 38(1), 34–105.
    17. Gottardo, P., & Murgia, M. (2000). The upstairs market for large-block trades: An empirical analysis of price effects on Italian shares. City University.
    18. Grossman, S. J. (1992). The informational role of upstairs and downstairs trading. Journal of Business, 65(4), 509–528.
    19. Huang, C., Huang, H., & Chengho, K. (2024). Media coverage and stock liquidity: Evidence from China. International Review of Economics & Finance, 89, 665–682.
    20. Huynh, H., & Dang, T. (2025). The liquidity effect of media coverage: International evidence. Pacific-Basin Finance Journal, 93.
    21. Keim, D., & Madhavan, A. (1996). The upstairs market for large-block transactions: Analysis and measurement of price effects. Review of Financial Studies, 9(1), 1–36.
    22. Kita, B., Czerwinski, M., & Presperepeczo, A. (2017). What drives shareholder reaction and wealth effect in block trades? Evidence from the Warsaw Stock Exchange. Emerging Markets Finance and Trade, 54(7), 1586–1607.
    23. Kraus, A., & Stoll, H. R. (1972). Price impacts of block trading on the New York Stock Exchange. Journal of Finance, 27(3), 569–588.
    24. Kurek, B. (2017). The information content of equity block trades on the Warsaw Stock Exchange: Conventional and bootstrap approaches. Journal of Forecasting, 35(1), 43–53.
    25. Lefebvre, J. (2010). Block trades and market liquidity on Euronext Paris. (Unpublished manuscript). EUROFIDAI - AFFI Finance Meeting, May 26, 2010.
    26. Li, X., Qiao, P., & Zhao, L. (2019). CEO media exposure, political connection and Chinese firms’ stock price synchronicity. International Review of Economics and Finance, 63, 61–75.
    27. Limem, M. (2020). Liquidity and volatility around large trades: Evidence from the Tunisian market. Net Journal of Social Sciences, 3(2), 50–63.
    28. Liu, B., & Zhang, X. (2017). Investor financial literacy and stock price synchronicity: A cross-country study. Review of Contemporary Business Research, 6(2), 26–37.
    29. Meng, Q., Song, X., Liu, C., & Zeng, H. (2020). The impact of block trades on stock price synchronicity: Evidence from China. International Review of Economics & Finance, 68, 239–253.
    30. Meshki, M. (2011). A study of influential factors in stock returns and profitability in the Tehran Stock Exchange (TSE): GMM & GLS models. Journal of Accounting Advances, 3(1), 91–119. (In Persian).
    31. Nahhal, B. (2023). Effect of block trading on the Moroccan stock exchange. African Development Finance Journal, 5, 33–52.
    32. Nasiry, M., Fakhari, H., & Malekian, E. (2025). The impact of synchronicity on block trading: Moderating role of media coverage. Journal of Financial Manager Strategy, 13(48), 117–140. (In Persian).
    33. Nguyen, B. D. (2015). Is more news good news? Media coverage of CEO’s, firm value, and rent extraction. Quarterly Journal of Finance, 5(4), 1–38.
    34. Seppi, D. J. (1990). Equilibrium block trading and asymmetric information: The Journal of Finance. Journal of Finance, 45(1), 73–94.
    35. Sterenczak, S., & Kubiak, J. (2022). Dividend policy and stock liquidity: Lessons from Central and Eastern Europe. Research in International Business and Finance, 62, 101727.
    36. Stoll, H. (1989). Inferring the components of the bid ask spread: Theory and empirical tests. The Journal of Finance, 44(1), 115–134.
    37. Sun, Y., & Ibikunle, G. (2017). Informed trading and the price impact of block trades: A high frequency trading analysis. International Review of Financial Analysis, 54, 114–129.
    38. Tetlock, P. C. (2010). Does public financial news resolve asymmetric information? Review of Financial Studies, 23(9), 3520–3557.
    39. Wang, Y. (2022). Volatility spillovers across NFTs news attention and financial markets. International Review of Financial Analysis, 83, 102313.
    40. Xia, J. (2024). Stealing the show: The negative effects of media coverage on peers’ stock liquidity. Finance Research Letters, 59, 104691.
    41. Yu, M., Kyung, H., & Tsang, A. (2024). Media coverage and price reactions to earnings news. China Journal of Accounting Research, 17(3), 100379.

     

Financial Management Perspective
Volume 15, Issue 3 - Serial Number 51
November 2025
Pages 101-120

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