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Measuring the Stock Liquidity Using a Market Microstructure Approach | ||
Advances in Industrial Engineering | ||
دوره 54، شماره 3، مهر 2020، صفحه 311-331 اصل مقاله (1.56 M) | ||
نوع مقاله: Research Paper | ||
شناسه دیجیتال (DOI): 10.22059/jieng.2021.325016.1770 | ||
نویسندگان | ||
Nastaran Hadi Doulabi؛ Mohammad Ali Rastegar* ؛ Parastoo Mohammadi | ||
Faculty of Industrial Engineering & Systems, Tarbiat Modares University, Tehran, Iran | ||
چکیده | ||
The main objective of this research is to identify the most important liquidity measures and their behavior during the trading day. For this purpose, the intraday data of 7 stocks of the Tehran Stock Exchange have been used to calculate 27 liquidity measures selected from the literature. At the first step, the distribution features and the correlation structure of the liquidity measures are examined. Using the Principal Components Analysis method, these components are identified, and their intraday patterns are extracted. The results show that reducing the number of measures to four final measures that can describe all aspects of liquidity without eliminating helpful information is possible and helps reduce the complexity of studies in this area. Relative Spread with mid quoted prices can be mentioned as the most practical microstructure component affecting liquidity. Based on this measure's intraday pattern, it can be said that this measure is minimized in the middle of the day. Therefore, liquidity is high during these hours, and favorable conditions for trading are provided. In the end, stocks are ranked based on all 27 liquidity measures through two different methods, and the most liquid stock is determined. | ||
کلیدواژهها | ||
Intraday Patterns؛ Liquidity Measures؛ Market Microstructure؛ PCA؛ TOPSIS | ||
مراجع | ||
[1] Quah, H., Haman, J., and Naidu, D. (2021). The effect of stock liquidity on investment efficiency under financing constraints and asymmetric information: Evidence from the United States. Accounting and Finance, 61, 2109-2150.
[2] Ranaldo, A. (2000). Intraday trading activity on financial markets: The Swiss evidence (Doctoral dissertation, Université de Fribourg).
[3] Jain, P. K. (2003). Institutional design and liquidity at stock exchanges around the world. Available at SSRN 869253.
[4] Gould, M. D., Porter, M. A., Williams, S., McDonald, M., Fenn, D. J., and Howison, S. D. (2013). Limit order books. Quantitative Finance, 13(11), 1709-1742.
[5] Cui, W., Hon, M. E., and Brabazon, A. (2012). An Empirical Investigation of Price Impact: An Agent-based Modelling Approach (Doctoral dissertation, University College Dublin).
[6] Korolev, V. Y., Chertok, A. V., Korchagin, A. Y., and Zeifman, A. I. (2015). Modeling high-frequency order flow imbalance by functional limit theorems for two-sided risk processes. Applied Mathematics and Computation, 253, 224-241.
[7] Muranaga, J., and Shimizu, T. (1999). Market microstructure and market liquidity. Bank of Japan.
[8] Gabrielsen, A., Marzo, M., and Zagaglia, P. (2011). Measuring market liquidity: An introductory survey.
[9] Salighehdar, A., Liu, Y., Bozdog, D., and Florescu, I. (2017). Cluster analysis of liquidity measures in a stock market using high frequency data. Journal of Management Science and Business Intelligence, 2(2), 1-8.
[10] Będowska-Sójka, B. (2019). Commonality in liquidity measures. The evidence from the Polish stock market.
[11] Johann, T., Scharnowski, S., Theissen, E., Westheide, C., and Zimmermann, L. (2019). Liquidity in the German stock market. Schmalenbach Business Review, 71(4), 443-473.
[12] Minović, J., Stevanović, S., and Belopavlović, G. (2011). Survey of one-dimensional liquidity measures. IASSIST quarterly, 197-202.
[13] Irvine, P. J., Benston, G. J., and Kandel, E. (2000). Liquidity beyond the inside spread: Measuring and using information in the limit order book. Available at SSRN 229959.
[14] Von Wyss, R. (2004). Measuring and predicting liquidity in the stock market (Doctoral dissertation, Novidea di Luigi Hofmann).
[15] Olbryś, J., and Mursztyn, M. (2017). Measurement of stock market liquidity supported by an algorithm inferring the initiator of a trade. Operations Research and Decisions, 27(4), 111-127.
[16] Gençay, R., Dacorogna, M., Muller, U. A., Pictet, O., and Olsen, R. (2001). An introduction to high-frequency finance. Elsevier.
[17] Baradaran, V., Kazem zadeh, R. B., Amiri, A. H., and Mogouie, H. (2012). A modified PCA approach for solving MADM problems with dependent criteria. Advances in Industrial Engineering, 46(2), 133-145
[18] Peykani, P., Mohammadi, E., Rostamy-Malkhalifeh, M., and Hosseinzadeh Lotfi, F. (2019). Fuzzy data envelopment analysis approach for ranking of stocks with an application to Tehran stock exchange. Advances in Mathematical Finance and Applications, 4(1), 31-43.
[19] Tsaur, R. C. (2011). Decision risk analysis for an interval TOPSIS method. Applied Mathematics and Computation, 218(8), 4295-4304.
[20] Sobhanifard, F., and Shahraki, M. R. (2020). An Integrated Neural Networks and MCMC Model to Predicting Bank’s Efficiency. Advances in Industrial Engineering, 54(1), 1-14.
[21] Kumar, G., and Misra, A. K. (2015). Closer view at the stock market liquidity: A literature review. Asian Journal of Finance and Accounting, 7(2), 35-57.
[22] Krishnan, R., and Mishra, V. (2012). Intraday Liquidity Patterns in Indian Stock Market Monash. Discussion Paper, Monash University, Malasya. | ||
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