This chapter starts with the research approach outlining the different steps in analyzing liquidity and informed trading in the DAX instruments, the classification of traders according to their level of information, and the analysis of their liquidity demand and supply behavior. What follows is a description of the data underlying the study, specifically any cleansing, selection, or enrichment measures taken. Initial descriptive statistics on the DAX instruments are presented and, finally, the set of hypotheses that will be analyzed in the empirical part is outlined.


Trading Volume Limit Order Order Book Informed Trader Limit Order Book 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 246.
    Markets in Financial Instruments Directive (MiFID) determines the average retail order size to be 7,500 €; see CESR (2005), p. 68.Google Scholar
  2. 249.
    Only Deutsche Telekom AG reveals a comparable average order size and number of orders (the difference being less than 0.5%). Lufthansa AG reveals a slightly smaller average order size for originator orders but five times as many originator as aggressor orders of that size. Bae/ Jang/ Park (2003), p. 533f. find for NYSE that large orders are more likely to be limit orders and conclude that order size is an important determinant of order type.Google Scholar
  3. 251.
    Admati/ Pfleiderer (1988) developed a theory where intraday patterns arise endogenously as a consequence of strategic trading behavior of informed and (uninformed) liquidity traders. Key to their model is the assumption of discretionary liquidity traders; see Admati/Pfleiderer (1988), p. 5.Google Scholar
  4. 256.
    Similar results have been reported for Euronext by Foucault/ Moinas/ Theissen (2004), p. 30 f. and Beltran/Durée/Giot (2004), p. 13; for SEHK by Ahn/Bae/Chan (2001), p. 773; for NYSE by Lee/Mucklow/ Ready (1993), p. 360f.Google Scholar

Copyright information

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

Personalised recommendations