The Relationship between Market Liquidity and Market Efficiency: A Detrended Cross-Correlation Analysis (DCCA) of Tehran Stock Exchange

Document Type : Research Paper

Authors

1 Faculty of Humanities and Social Sciences, Golestan University, Gorgan, Iran.

2 Faculty of Administrative Sciences and Economics, Mazandaran University, Babolsar, Iran.

Abstract

This study examines the dynamic relationship between market efficiency and liquidity in Tehran Stock Exchange (TSE) from March 2010 to March 2024. To achieve this, we employ the Detrended Cross-Correlation Analysis (DCCA) method using a one-year rolling window. Initially, we calculate the market efficiency index (EI) through the Detrended Fluctuation Analysis (DFA) applied to the time series of daily closing prices.  Simultaneously, the moving average of daily trading volume over a one-year period is used as a proxy for market liquidity. The results indicate that the correlation between efficiency and liquidity fluctuates over time, exhibiting both positive and negative values in different periods. However, these variations remain weak, with correlation coefficients being close to zero for most time frames. This suggests that there is no clear or stable relationship between the two variables. Unlike previous studies that have suggested a significant role of liquidity in enhancing market efficiency, our findings do not support a strong link between trading volume and efficiency in the TSE. These results imply that market liquidity, as measured by trading volume, does not exhibit a strong or consistent relationship with market efficiency and vice versa. Accordingly, increasing trading volume and market liquidity does not necessarily translate into greater efficiency, and other influential factors must be considered to enhance market efficiency.

Keywords

Main Subjects

Article Title [Persian]

رابطه بین نقدشوندگی بازار و کارایی بازار: تحلیل همبستگی متقاطع روندزدایی‌شده (DCCA) در بورس اوراق بهادار تهران

Authors [Persian]

  • مهدی شهرازی 1
  • میلاد شهرازی 2
  • زینب یزدانی چراتی 2
1 دانشکده علوم انسانی و اجتماعی، دانشگاه گلستان، گرگان، ایران.
2 دانشکده اقتصاد، دانشگاه مازندران، بابلسر، ایران.
Abstract [Persian]

این مطالعه به بررسی رابطه پویای بین کارایی بازار و نقدشوندگی در بورس اوراق بهادار تهران (TSE) در بازه زمانی اسفند ۱۳۸۸ تا اسفند ۱40۲ می‌پردازد. برای این منظور، روش تحلیل همبستگی متقاطع روندزدایی‌شده(DCCA)  با استفاده از یک پنجره متحرک یک‌ساله مورد استفاده قرار می‎گیرد. در ابتدا، شاخص کارایی بازار (EI) از طریق تحلیل نوسانات روندزدایی‌شده (DFA) بر روی سری زمانی قیمت‌های پایانی روزانه محاسبه می‌شود. به‌طور هم‌زمان، میانگین متحرک حجم معاملات روزانه در یک دوره یک‌ساله به‌عنوان شاخصی از نقدشوندگی بازار در نظر گرفته می‌شود. نتایج نشان می‌دهد که همبستگی بین کارایی و نقدشوندگی در طول زمان دارای نوسان بوده و در دوره‌های مختلف مقادیر مثبت و منفی را نشان می‌دهد. بااین‌حال، این نوسانات ضعیف می‏باشد و ضرایب همبستگی در اکثر بازه‌های زمانی نزدیک به صفر هستند. این یافته‌ها نشان می‌دهند که رابطه‌ای معین یا پایدار بین این دو متغیر وجود ندارد. برخلاف مطالعات پیشین که بر نقش معنادار نقدشوندگی در بهبود کارایی بازار تأکید داشته‌اند، نتایج ما از وجود پیوند قوی بین حجم معاملات و کارایی در بورس اوراق بهادار تهران حمایت نمی‌کند. این نتایج بیانگر آن است که نقدشوندگی بازار، که از طریق حجم معاملات سنجیده می‌شود، رابطه قوی یا پایداری با کارایی بازار ندارد و بالعکس. بنابراین، افزایش حجم معاملات و نقدشوندگی بازار لزوماً به معنای کارایی بیشتر نیست و برای بهبود کارایی بازار باید سایر عوامل مؤثر نیز در نظر گرفته شوند.

Keywords [Persian]

  • کارایی بازار
  • نقدشوندگی
  • حجم معاملات
  • تحلیل هم‌بستگی متقاطع روندزدایی‌شده (DCCA)
  • بورس اوراق بهادار تهران

References

Abounoori, E., Shahrazi M. and Rasekhi S. (2012). An investigation of Forex Market Efficiency Based on Detrended Fluctuation Analysis: A Case Study for Iran, physica A, 391(11), 3170–3179.
Admati, A., Pfleiderer, P. (1988). A theory of intraday patterns: volume and price variability. Review of Financial Studies, 1(1), 3–40.
Alvarez-Ramirez, J., Alvarez, J., Rodriguez, E., & Fernandez-Anaya, G. (2008). Time-varying Hurst exponent for US stock markets. Physica A, 387(24), 6159-6169.
Aitken, M. and Comerton-Forde, C. (2003). How should liquidity be measured?, Pacific-Basin Finance Journal, 11(1), 45-59.
Barberis, N., Shleifer, A., Vishny, R. (1998). A model of investor sentiment. Journal of Financial Economics, 49(3), 307–343.
Bariviera, A. F. (2011). The influence of liquidity on informational efficiency: The case of the Thai Stock Market. Physica A, 390(23), 4426-4432.
Brunnermeier, M.K. (2009) Deciphering the Liquidity and Credit Crunch 2007-2008, Journal of Economic Perspectives, 23(1), 77 -100.
Cajueiro, D. O., & Tabak, B. M. (2004). Evidence of long range dependence in Asian equity markets: the role of liquidity and market restrictions. Physica A, 342(3), 656-664.
Chordia, T., Roll, R., and Subrahmanyam, A. (2008). Liquidity and market efficiency, Journal of Financial Economics, 87(2), 249-268.
DeLong, J. Bradford, Andrei Shleifer, Lawrence H. Summers, and Robert J. Waldmann, (1990), Noise Trader Risk in Financial Markets, Journal of Political Economy, 98(4), 703-738.
Shostak, F. (1997). In Defense of Financial Analysis: A Critique of the Efficient Market Hypothesis. Review of Austrian Economics, 10 (2), 27-45.
Fama, E. (1970). Efficient capital markets: A Review of Theory and Empirical Work. Journal of Finance, 25(2), 383–417.
Galliani, C., Petrella, G., Resti, A., The liquidity of corporate and government bonds: drivers and sensitivity to different market conditions. Vol. 26498. Joint Research Centre Technical Reports, 2014.
Gilson, R. J. and Kraakman, R. H. (1984), The Mechanisms of Market Efficiency, Virginia Law Review, 70 (4) 549-644.
Grech, D., & Mazur, Z. (2004). Can one make any crash prediction in finance using the local Hurst exponent idea? Physica A, 336(1), 133-145.
Gu, R., Shao, Y., & Wang, Q. (2013). Is the efficiency of stock market correlated with multifractality? An evidence from the Shanghai stock market. Physica A, 392(2), 361-370.
Harris, L. E. 1990. Liquidity, trading rules and electronic trading systems. New York University Monograph Series in Finance and Economics, No. 1990-4.
Hodrea, R. (2015). An intraday analysis of the market efficiency-liquidity relationship: the case of BVB stock exchange. Procedia Economics and Finance, 32, 1432 – 1441.
Hou, L., & Pan, Y. (2022). Evaluating the connectedness of commodity future markets via the cross-correlation network. Frontiers in Physics, 10, 1-12.
Https://www.tse.ir
Kyle, Albert S., 1985, Continuous Auctions and Insider Trading, Econometrica (53), 1315- 1336.
Liu, L., & Ma, G. (2014). Cross-correlation between crude oil and refined product prices. Physica A, 413(C), 284-293.
Muniandy, S. V., Lim, S. C., & Murugan, R. (2001). Inhomogeneous scaling behaviors in Malaysian foreign currency exchange rates. Physica A, 301(1), 407-428.
Muranaga, J. and Shimizu, T. (1999), Market microstructure and market liquidity, Working Paper 99-E-14, Bank of Japan.
Oh, G., Kim, S., & Eom, C. (2007). Market efficiency in foreign exchange markets. Physica A, 382(1), 209-212.
Peng, C. K., Buldyrev, S. V., Havlin, S., Simons, M., Stanley, H. E., & Goldberger, A. L. (1994). Mosaic organization of DNA nucleotides. Physical review E, 49(2), 1685-1689.
Podobnik, B., & Stanley, H. E. (2008). Detrended Cross-Correlation Analysis: A New Method for Analyzing Two Nonstationary Time Series. Physical review letters, 100(8), 38-71.
Podobnik, B., Horvatic, D., Petersen, A. M., & Stanley, H. E. (2009). Cross-correlations between volume change and price change. Proceedings of the National Academy of Sciences, 106(52), 22079-22084.
Podobnik, B., Jiang, Z. Q., Zhou, W. X., & Stanley, H. E. (2011). Statistical tests for power-law cross-correlated processes. Physical Review E—Statistical, Nonlinear, and Soft Matter Physics, 84(6), 66-118.
Rizvi, S. A. R., Dewandaru, G., Bacha, O. I., & Masih, M. (2014). An analysis of stock market efficiency: Developed vs Islamic stock markets using MF-DFA. Physica A, 407, 86-99.
Rodriguez, E., & Alvarez-Ramirez, J. (2021). Time-varying cross-correlation between trading volume and returns in US stock markets. Physica A, 391(16), 4128-4135.
Rösch, D. M., Subrahmanyam, A., & Van Dijk, M. A. (2017). The dynamics of market efficiency. The Review of Financial Studies, 30(4), 1151-1187.
Salehi Far, M. (2021). Examining Liquidity and Informational Efficiency in the Cryptocurrency Market. Investment Knowledge, 10(39), 465-482.
Sarkanian, J., Raei, R., & Fallahpour, S. (2015). Examining the Relationship between Liquidity and Stock Returns in the Iranian Stock Market. Financial Management Perspective, 5(11), 9-26.
Sensoy, A. (2013). Time-varying long range dependence in market returns of FEAS members. Chaos, Solitons & Fractals, 53(1), 39-45.
 Serbinenko, S.T. Rachev (2009). Intraday Spot Foreign Exchange Market. Analysis of Efficiency, Liquidity and Volatility, Journal of Investment Management and Financial Innovations, 6(4), 35-45.
Shahrazi, M. and shahrazi, M. M. (2023). An Application of Econophysics to Study the Relationship between trading volume and stock returns in Tehran Stock Exchange. Quarterly Journal of Quantitative Economics (JQE). doi: 10.22055/jqe.2023.42398.2529.
Sukpitak, J., & Hengpunya, V. (2016). The influence of trading volume on market efficiency: The DCCA approach. Physica A, 458(C) 259-265.
Tetlock, P.C., 2008, Liquidity and Prediction Market Efficiency. Working paper, Columbia University.
Utami, W., Wahyuni, P. D., & Nugroho, L. (2020). Determinants of Stock Liquidity: Forward-Looking Information, Corporate Governance, and Asymmetric Information. Journal of Asian Finance, Economics and Business, 7(12), 795–807.
Utami, Wiwik, Nugroho, L., & Farida. (2017). Fundamental versus technical analysis of investment: a case study of investors' decisions in Indonesia stock exchange. Journal of Internet Banking and Commerce, 22(8), 1-18.
Wang, Q., & Hu, Y. (2015). Cross-correlation between interest rates and commodity prices. Physica A, 428(C), 80-89.
Wang, Y., Wei, Y., & Wu, C. (2010). Cross-correlations between Chinese A-share and B-share markets. Physica A, 389(23), 5468-5478.
Wu, B., Jiang, F., Zhang, J., Liu, C., & Shi, K. (2024). Deep multifractal detrended cross-correlation analysis algorithm for multifractals. Physica A, 653, 130105.
Zebende, G. F. (2011). DCCA cross-correlation coefficient: Quantifying level of cross-correlation. Physica A, 390(4), 614-618.
Zebende, G. F., & Machado Filho, A. (2009). Cross-correlation between time series of vehicles and passengers. Physica A, 388(23), 4863-4866.
Zhao, L. (2023). Liquidity, informational efficiency and default risk in Chinese stock markets. Doctor of Philosophy (PhD) Thesis at Macquarie University.
Zhao, L., Li, W., Fenu, A., Podobnik, B., Wang, Y., & Stanley, H. E. (2018). The q-dependent detrended cross-correlation analysis of stock market. Journal of Statistical Mechanics: Theory and Experiment, 2018(2), 023402.
Zou, S., & Zhang, T. (2020). Cross-correlation analysis between energy and carbon markets in China based on multifractal theory. International Journal of Low-Carbon Technologies, 15(3), 389-397.
 

Files

History

  • Receive Date: 09 February 2025
  • Revise Date: 30 March 2025
  • Accept Date: 06 April 2025

Share

How to cite

Statistics