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Liquidity Demand and Supply Behavior of Informed Traders

Abstract

Based upon the trader categorization defined in the preceding chapter, this chapter aims at describing and analyzing the liquidity demand (aggressor) and supply (originator) behavior of the different trader categories. The behavior of informed traders will be compared to the behavior of the other trader categories and results for all trader IDs.307 This chapter covers the last part of the empirical analysis as presented in Exhibit 9-1.

Keywords

Trading Volume Limit Order Price Impact Order Book Order Size 
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.

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References

  1. 313.
    Informed traders have a share of 21.6% of the total aggressor volume which varies across size classes: 14.4% volume share for small orders, 20.1% for medium size orders, and 31.3% for large orders. This is in line with Barclay/ Warner (1993), p. 288, who conclude that informed traders will mainly use larger order sizes.Google Scholar
  2. 316.
    As the data does not allow the identification of whether the order flow behind the trader ID is able to monitor the order book, this remains an open question. Gomber/ Schweickert/ Theissen (2005), p. 18, find that large orders are timed. As the average order size of trader IDs that are categorized as uninformed is rather small, it can be assumed that mostly retail order flow belongs to this trader category. Retail investors do not have direct access to the trading system and usually do not have access to order book information via vendors (which are quite costly). As a consequence, there is no possibility to monitor the order book.Google Scholar
  3. 320.
    See Cao/ Hansch/ Wang (2004), p. 23.Google Scholar
  4. 325.
    In this analysis, only orders that are executed are part of the data set. Thus orders that enter the order book but do not execute either being deleted before execution or remaining in the order book are not included. Strictly speaking, limit orders that enter the order book and are not executed do not provide liquidity, as at their price limit there is no liquidity demand. In addition, Hasbrouck/ Saar (2002), p. 21ff. and Hasbrouck/Saar (2004), p. 4 have identified so-called fleeting orders, which are limit orders that are cancelled within an extremely brief time, e.g. two seconds after their submission. They find that these limit orders are close substitutes to market orders that demand immediacy.Google Scholar
  5. 328.
    Kempf/ Mayston (2005), p. 8f. analyze the order book as well as the order flow for the DAX instruments in Xetra during 2 January to 31 March 2004. They find that only ∼25% of limit orders entered are also executed. For the limit orders that did not execute, they do not provide any information concerning their aggressiveness or the time they remained in the order book before they were cancelled.Google Scholar
  6. 329.
    Harris (1990), pp. 6–9 distinguishes two types of liquidity providers-passive traders that try to capture the spread and precomitted traders that try to lower their execution costs but switch to a liquidity demanding strategy if they are not executed.Google Scholar
  7. 344.
    Anand/ Chakravarty/ Martell (2005) and Kaniel/Liu (2006) find evidence for NYSE that limit orders convey more information than market orders concluding that informed traders implement limit orders. Bloomfield/O’Hara/Saar (2005) provide evidence for an experimental limit order book.Google Scholar

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

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