Market Description: Liquidity and Informed Trading


This chapter covers the first step of the empirical analysis. See Exhibit 7-1. It is subdivided into separate descriptions and tests of common hypotheses of liquidity and informed trading as well as their relation. It lays the foundation for the interpretation of results on informed traders’ behavior, which are presented in the third step. The synopsis summarizes the results in comparison to the results found in other electronic limit order markets.


Trading Volume Price Impact Order Book Order Size Inform Trading 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 259.
    Gomber/ Schweickert (2002b), p. 6 implement a similar approach comparing trading volume and XLM(100). In contrast to the presented results, their sample (five trading days in March 2002) reveals stronger differences between the rankings.Google Scholar
  2. 260.
    See Gomber/ Schweickert/ Theissen (2005), p. 10.Google Scholar
  3. 261.
    See Irvine/ Benston/ Kandel (2000), p. 22f. Chordia/Roll/Subrahmanyam (2002), p. 117 find an order imbalance with more market buy than sell orders which implies higher depth on the ask side of the order book. Ranaldo (2004), p. 55f. discovers that buyers more frequently submit limit orders within the quote.Google Scholar
  4. 262.
    In addition, Gomber/ Schweickert/ Theissen (2005), p. 14 have shown that large orders are timed, i.e. they are entered when order book depth is exceptionally large. The authors chose the 100 largest transactions during one month; their average size is 899,402 € for the sell and 866,337 € for the buy side.Google Scholar
  5. 267.
    See Gomber/ Schweickert/ Theissen (2005), p. 11 and p. 37. Following discussions with the Xetra members, trading hours were reduced to 5:30 pm in November 2003. For the floor trading hours still extend until 8:00 pm. Wolff (2003), pp. 151–155 finds a U-shaped distribution for the DAX instruments during June to September 2001. McInish/Wood (1992), p. 760 find a reverse J-shaped distribution for NYSE spreads.Google Scholar
  6. 268.
    See Dennis/ Weston (2001), p. 4 and p. 19f., Chakravarty (2001), p. 291, and Anand/Chakravarty/Martell (2005), p. 290.Google Scholar
  7. 269.
    See Stoll (2000), p. 1482. Chung/Chaeronwong (1998), p. 5, who analyze NYSE and AMEX data, implement average daily trading volume, price, and risk as regression variables and report that using other measures of trading activity, such as the number of trades, yields similar results.Google Scholar
  8. 271.
    For NYSE and NASDAQ, effective spreads have been reported to be smaller than the quoted spread (BBA). The results reflect any price improvement granted by the specialist or market makers. See Bessembinder/ Kaufman (1997), p. 296f., SEC (2001), p. 15, and Boehmer (2005), p. 567. Electronic limit order books do not offer the opportunity to trade inside the spread; thus, effective spreads cannot be smaller than the BBA.Google Scholar
  9. 272.
    See Frey/ Grammig (2006), p. 1014, Grammig/Heinen/Renfigo (2004), p. 5f., and Beltran/Giot/Grammig (2005), p. 24.Google Scholar
  10. 274.
    Even for specialist and market maker markets, empirical studies have demonstrated that the inventory cost component is insignificant for high volume stocks. See Harris/ Panchapagesan (2005), p. 61, Hasbrouck (1988), pp. 243–247, Madhavan/Panchapagesan (2002), p. 107 and, Stoll (1989), p. 132. McInish/Van Ness (2002), p. 508 analyze intraday spread components for NYSE instruments, disregarding inventory costs based upon the results of the aforementioned authors.Google Scholar
  11. 276.
    Admati/ Pfleiderer (1988), p. 5 introduce the concept of discretionary trading. Biais/Hillion/Spatt (1995), p. 1657 find that traders monitor the order book and submit orders depending on the state of the order book. Gomber/Schweickert/Theissen (2005), p. 17ff. discover timing of large orders in Xetra.Google Scholar
  12. 277.
    See Hasbrouck (1991a), p. 199; Easley/Kiefer/O’Hara/Paperman (1996), p. 1407; Huang/Stoll (1997), p. 1010.Google Scholar
  13. 280.
    Barclay/ Warner (1993), p. 292 empirically demonstrate that medium sized orders display a disproportionately large price impact. Chakravarty (2001), p. 301 link order size and trader type, confirming that institutional in contrast to individual traders are much more likely to use medium sized orders, identifying them as informed.Google Scholar
  14. 287.
    See Beltran/ Durée/ Giot (2004), Biais/Hillion/Spatt (1995), Handa/Schwartz/Tiwari (2003) and, Pagano/ Padilla (2005).Google Scholar
  15. 288.
    See Hollifield/ Miller/ Sandas (2004) and, Sandas (2001).Google Scholar
  16. 289.
    See Ahn/ Bae/ Chan (2001), Brockman/Chung (1998), (1999), (2000) and (2002), and Chan (2005).Google Scholar
  17. 290.
    See Barclay/ Warner (1993), Bessembinder (1999) and (2003a), Bessembinder/Kaufman (1997), Boehmer (2005), Chakravarty (2001), Chung/Van Ness/Van Ness (1999) and (2004), Handa/ Schwartz (1996b), Hasbrouck (1991a) and (1991b), Heidle/Huang (2002), Huang/Stoll (1996a) and (1996b), Lee/Mucklow/ Ready (1993), McInish/Wood (1992), Stoll (2000), and Van Ness/Van Ness/Warr (2005).Google Scholar

Copyright information

© Deutscher Universitäts-Verlag | GWV Fachverlage GmbH, Wiesbaden 2007

Personalised recommendations