Finanse, Rynki Finansowe, Ubezpieczenia

Previously: Zeszyty Naukowe Uniwersytetu Szczecińskiego. Finanse, Rynki Finansowe, Ubezpieczenia


ISSN: 2450-7741     eISSN: 2300-4460    OAI    DOI: 10.18276/frfu.2016.79-07
CC BY-SA   Open Access 

Issue archive / 1/2016 (79)
Głębokość rynku jako jeden z wymiarów płynności Giełdy Papierów Wartościowych w Warszawie SA
(Market Depth as One of Market Liquidity Dimensions on the Warsaw Stock Exchange)

Authors: Joanna Olbryś

Michał Mursztyn
Keywords: dimensions of market liquidity market depth trade classification algorithms
Data publikacji całości:2016
Page range:12 (101-112)
Cited-by (Crossref) ?:

Abstract

Purpose – The main aim of the paper was an empirical analysis of market depth as one of the market liquidity dimensions on the Warsaw Stock Exchange. The additional goal was a robustness analysis of results obtained with respect to the whole sample period January 2005–June 2015, and three adjacent subsamples of equal size: the pre-crisis, crisis, and post-crisis periods. Design/methodology – 53 WSE-listed companies from three size groups have been investigated. The highfrequency data was utilized. As the data set do not identify a trade direction, firstly a trade classification algorithm was employed to infer trade sides. Next the proxies of market depth were calculated using the so-called order ratio (OR). Findings – According to the literature, a high order ratio denotes high market depth and low liquidity. A small order ratio denotes small market depth and high liquidity. The empirical results reveal the smallest value of the OR indicator for the most liquid assets (e.g. KGH, OPL, PEO, PKN, PKO). Moreover, the results turned out to be robust to the choice of the sample and rather do not depend on a firm size. Originality/value – To the best of the authors’ knowledge, no such research has been undertaken for the Warsaw Stock Exchange thus far.
Download file

Article file

Bibliography

1.Ahn H.-J., Bae K.-H., Chan K. (2001), Limit orders, depth, and volatility: Evidence from the Stock Exchange of Hong Kong, „Journal of Finance” vol. 56, no. 2, s. 767–787.
2.Asquith P., Oman R., Safaya C. (2010), Short sales and trade classification algorithms, „Journal of Financial Markets” vol. 13, no. 1, s. 157–173.
3.Bernstein P. L. (1987), Liquidity, stock markets, and market makers, „Financial Management”, vol. 16, no. 2, s. 54–62.
4.Bekaert G., Harvey C.R., Lundblad C. (2007), Liquidity and expected returns: Lessons from emerging markets, „Review of Financial Studies” vol. 20, no. (6), s. 1783–1831.
5.Chakrabarty B., Li B., Nguyen V., Van Ness P.A. (2007), Trade classification algorithms for electronic communications network trades, „Journal of Banking and Finance” vol. 31, no. 12, s. 3806–3821.
6.Chakrabarty B., Moulton P.C., Shkilko A. (2012), Short sale, long sale, and the Lee-Ready trade classification algorithm revisited, „Journal of Financial Markets” vol. 15, no. 4, s. 467–491.
7.Chan K., Fong W.-M. (2000), Trade size, order imbalance, and the volatility-volume relation, „Journal of Financial Economics” vol. 57, s. 247–273.
8.Chordia T., Roll R., Subrahmanyam A. (2000), Commonality in liquidity, „Journal of Financial Economics” vol. 56, s. 3–28.
9.Chordia T., Roll R., Subrahmanyam A. (2001), Market liquidity and trading activity, „Journal of Finance” vol. 56, no. 2, s. 501–530.
10.Chordia T., Roll R., Subrahmanyam A. (2002), Order imbalance, liquidity, and market returns, „Journal of Financial Economics” vol. 65, s. 111–130.
11.Chordia T., Sarkar A., Subrahmanyam A. (2005), An empirical analysis of stock and bond market liquidity, „The Review of Financial Studies” vol. 18, no. 1, s. 85–129.
12.Doman M. (2011), Mikrostruktura giełd papierów wartościowych, Wydawnictwo Uniwersytetu Ekonomicznego w Poznaniu, Poznań.
13.Dong J., Kempf A., Yadav P.K. (2007), Resiliency, the neglected dimension of market liquidity: Empirical evidence from the New York Stock Exchange, Working Paper, available at SSRN 967262.
14.Ellis K., Michaely R., O’Hara M. (2000), The accuracy of trade classification rules: Evidence from Nasdaq, „Journal of Financial and Quantitative Analysis” vol. 35, no. 4, s. 529–551.
15.Finucane T.J. (2000), A direct test of methods for inferring trade direction from intra-day data, „Journal of Financial and Quantitative Analysis” vol. 35, no. 4, s. 553–576.
16.Foster F.D., Viswanathan S. (1993), Variations in trading volume, return volatility, and trading costs: Evidence on recent price formation models, „Journal of Finance” vol. 48, no. 1, s. 187–211. http://www.bossa.pl.
17.Huberman G., Halka D. (2001), Systematic liquidity, „Journal of Financial Research” vol. 24, no. 2, s. 161–178.
18.Jankowski R., Olbryś J. (2015), Wymiary płynności rynku papierów wartościowych, Zeszyty Naukowe Uniwersytetu Szczecińskiego nr 854, „Finanse, Rynki Finansowe, Ubezpieczenia” nr 73, Wydawnictwo Naukowe Uniwersytetu Szczecińskiego, Szczecin, s. 645–658.
19.Korajczyk R., Sadka R. (2008), Pricing the commonality across alternative measures of liquidity, „Journal of Financial Economics” vol. 87, no. 1, s. 45–72.
20.Kyle A.S. (1985), Continuous auctions and insider trading, „Econometrica” vol. 53, no. 6, s. 1315–1336.
21.Lee C.M.C., Mucklow B., Ready M.J. (1993), Spreads, depths, and the impact of earnings information: An intraday analysis, „The Review of Financial Studies” vol. 6, no. 2, s. 345–374.
22.Lee C.M.C., Ready M.J. (1991), Inferring trade direction from intraday data, „Journal of Finance” vol. 46, no. 2, s. 733–746.
23.Lee C.M.C., Radhakrishna B. (2000), Inferring investor behavior: Evidence from TORQ data, „Journal of Financial Markets” vol. 3, s. 83–111.
24.Lesmond D.A. (2005), Liquidity of emerging markets, „Journal of Financial Economics” vol. 77, no. 2, s. 411–452.
25.Lin W.T., Sun D.S., Tsai S.-C. (2012), Does trading remove or cause friction?, „Emerging Markets Finance & Trade” vol. 48 (Supp. 2), s. 33–53.
26.Lu Y.-C., Wei Y.-C. (2009), Classification of trade direction for an equity market with price limit and order match: Evidence from the Taiwan stock market, „Investment Management and Financial Innovations” vol. 6, no. 3, s. 135–147.
27.Nowak S. (2014), Order imbalance on the Warsaw Stock Exchange 2000–2012, Międzynarodowa Konferencja Inwestycje Finansowe i Ubezpieczenia, Wrocław, 17–19 września.
28.Nowak S., Olbryś J. (2015), Direct evidence of non-trading on the Warsaw Stock Exchange, Wroclaw Conference in Finance. Contemporary Trends and Challenges, Wrocław, 22–24 września.
29.Odders-White E.R. (2000), On the occurrence and consequences of inaccurate trade classification, „Journal of Financial Markets” vol. 3, s. 259–286.
30.Olbryś J. (2013), Zastosowanie wybranych miar płynności aktywów kapitałowych na Giełdzie Papierów Wartościowych w Warszawie S.A., „Zarządzanie i Finanse” nr 11 (3), s. 65–77.
31.Olbryś J. (2014), Wycena aktywów kapitałowych na rynku z zakłóceniami w procesach transakcyjnych, Difin, Warszawa.
32.Olbryś J., Majewska E. (2014a), Identyfikacja okresu kryzysu z wykorzystaniem procedury diagnozowania stanów rynku, Zeszyty Naukowe Uniwersytetu Szczecińskiego nr 802, „Finanse, Rynki Finansowe, Ubezpieczenia” nr 65, s. 699–710.
33.Olbryś J., Majewska E. (2014b), Direct identification of crisis periods on the CEE stock markets: The influence of the 2007 U.S. subprime crisis, „Procedia Economics and Finance” vol. 14, s. 461–470.
34.Olbryś J., Majewska E. (2015), Bear market periods during the 2007–2009 financial crisis: Direct evidence from the Visegrad countries, „Acta Oeconomica” vol. 65, no. 4, s. 547–565.
35.Olbryś J., Mursztyn M. (2015), Comparison of selected trade classification algorithms on the Warsaw Stock Exchange, „Advances in Computer Science Research” vol. 12, s. 37–52.
36.Pagan A.R., Sossounov K.A. (2003), A simple framework for analysing bull and bear markets, „Journal of Applied Econometrics” vol. 18, no. 1, s. 23–46.
37.Peterson M., Sirri E. (2003), Evaluation of the biases in execution costs estimation using trades and quotes data, „Journal of Financial Markets” vol. 6, no. 3, s. 259–280.
38.Piwowar M.S., Wei L. (2003), The sensitivity of effective spread estimates to trade-quote matching algorithms, „Electronic Markets” vol. 16, no. 2, s. 112–129.
39.Ranaldo A. (2001), Intraday market liquidity on the Swiss Stock Exchange, „Swiss Society for Financial Market Research” vol. 15, no. 3, s. 309–327.
40.Sarr A., Lybek T. (2002), Measuring liquidity in financial markets, IMF Working Paper 02/232.
41.Stoll H.R. (2000), Friction, „Journal of Finance” vol. 55, no. 4, s. 1479–1514.
42.Von Wyss R. (2004), Measuring and predicting liquidity in the stock market, Dissertation Nr. 2899, University of St. Gallen.
43.Theissen E. (2001), A test of the accuracy of the Lee/Ready trade classification algorithm, „Journal of International Financial Markets, Institutions and Money” vol. 11, no. 2, s. 147–165.
44.Wong J., Fung L. (2002), Liquidity of the Hong Kong stock market since the Asian financial crisis, Proceedings of the Third Joint Central Bank Research Conference, Bank for International Settlements, Basel, Switzerland, s. 180–211.